Imarticus Learning, Author at Finance, Tech & Analytics Career Resources

Top Data Science Careers Set to Dominate the Job Market by 2030

Posted on July 10, 2025July 11, 2025 by Imarticus Learning

Data science is transforming industries worldwide. It’s a field that blends technology, statistics, and domain expertise.

By 2030, data science careers are expected to dominate the job market. The need for data-driven decisions drives this surge.

Organisations rely on data to gain insights and stay competitive. This reliance fuels the demand for skilled data professionals.

Data science courses and certifications are crucial. They equip individuals with the necessary skills to thrive in this field.

Key roles include data analysts, machine learning engineers, and AI specialists. Each role plays a vital part in the data ecosystem.

Data science skills are in high demand. Programming, statistical analysis, and data visualisation are essential for success.

Continuous learning is vital in this ever-evolving field. Online courses offer flexibility and accessibility for learners worldwide.

Prepare for a data-driven future by exploring top data science careers. Discover how to enhance your skills and seize opportunities.

The Rise of Data Science: Why 2030 Will Be a Landmark Year

Data science is rapidly becoming a cornerstone of modern business. By 2030, it is set to fully permeate all industries.

The shift toward data-driven decision-making is profound. Companies now rely heavily on data to inform their strategic decisions.

Several factors contribute to this trend. The exponential growth of data and advancements in technology are pivotal.

Global digitalisation continues to accelerate. This generates vast amounts of data, necessitating the use of sophisticated analysis techniques.

Organisations across various sectors are adopting data-centric strategies. This creates numerous opportunities for skilled data professionals.

Data science will play a critical role in innovation. It aids in developing new products, optimising processes, and enhancing consumer experiences.

Notable reasons why 2030 is a key year for data science include:

Widespread AI integration into everyday applications.
The maturation of machine learning technologies.
Increasing demand for personalised consumer experiences.
Expanding the use of big data analytics in decision-making.
Rise of smart cities powered by data.

These developments underscore the importance of robust data science skills. They equip professionals to navigate complex data landscapes.

As technology continues to evolve, new data science roles will emerge. This makes continuous learning and skill enhancement imperative.

Essential Data Science Skills for Future-Proof Careers

To thrive in the data science field, mastering certain skills is crucial. These skills ensure you can tackle the demands of tomorrow.

Programming is a foundational skill for data scientists. Proficiency in languages like Python and R is advantageous. They facilitate data manipulation and analysis.

Mathematical acumen, especially in statistics, is non-negotiable. It enables data scientists to construct and interpret advanced models. This skill is essential for making informed, data-driven insights.

Machine learning is a powerful tool in the data science arsenal. It involves designing algorithms that enable systems to learn from data. Understanding various algorithms is crucial to unlocking the full potential of machine learning.

Equally important is data visualisation. It transforms complex data into understandable visuals. This skill enables the conveyance of insights to stakeholders succinctly.

Database management skills are essential and cannot be overlooked. SQL knowledge is vital for accessing and managing datasets. It ensures data is efficiently queried and manipulated.

Essential data science skills include:

Programming (Python, R)
Statistical Analysis
Machine Learning
Data Visualization
Database Management (SQL)

Soft skills also play a significant role. Effective communication is essential. It ensures that complex findings are clearly articulated.

Problem-solving abilities are invaluable. They help in identifying solutions to data-related challenges. Adaptability in fast-paced environments is likewise crucial.

Professionals should cultivate curiosity and a continual learning mindset. This is imperative in the ever-evolving field of data science.

Future data scientists should focus on:

Communication Proficiency
Problem-Solving
Adaptability
Curiosity
Continuous Learning

These skills provide a robust foundation for a thriving career. They ensure professionals remain competitive in the global job market.

The Role of Data Science Courses and Certifications

Data science courses offer a structured path for acquiring essential skills. They provide comprehensive coverage of key topics.

A well-designed course covers programming, statistics, and machine learning. These topics are foundational for any aspiring data scientist.

Certifications complement these courses by validating your expertise. They serve as proof of your proficiency in data science domains.

Employers often seek certified professionals. Certifications enhance your credibility in a competitive job market. They indicate commitment to learning and improvement.

Both online and offline courses offer flexibility in learning. Online platforms provide accessibility and convenience for global learners. This flexibility is crucial for working professionals.

Selecting the right course involves considering your career goals. Assess whether the course covers your intended specialisation areas. It’s imperative to choose accredited and reputable providers.

Key benefits of data science courses and certifications include:

Structured Learning Paths
Skill Validation
Increased Credibility
Flexible Learning Options
Specialization Opportunities

Networking opportunities often arise from these courses. Engaging with peers facilitates the exchange of ideas and collaboration.

Participating in data science communities extends your learning. It connects you with other professionals and experts.

Courses often include hands-on projects. Projects provide practical experience and enhance understanding.

Investing in a data science course can be a game-changer. It equips you with the knowledge to excel and innovate in the field.

Top Data Science Careers to Watch by 2030

The field of data science is expected to transform the job market by 2030. It includes diverse roles suited to various skill sets.

Emerging data science careers offer numerous opportunities. These roles address the growing need for data-driven insights. Key positions will dominate various industries.

Highlighted careers in data science you should watch include:

Data Analyst
Machine Learning Engineer
Data Engineer
AI Specialist
Business Intelligence Analyst

These roles leverage technology and data insights. They promise growth and innovation across fields. Professionals can build meaningful careers in these positions.

Predicting future job trends is essential for effective career planning. Aspiring professionals should focus on these promising paths. By acquiring the right skills, they can ensure long-term success.

Data Analyst: The Backbone of Data-Driven Decisions

Data analysts play a crucial role in interpreting data. Their work helps organisations make informed decisions. They focus on extracting valuable insights from raw data.

Skills needed include data visualisation and statistical analysis. Analysts effectively communicate their findings using charts and graphs. They provide a clear picture of trends and patterns.

Key responsibilities of a data analyst:

Collecting and cleaning data
Analysing datasets for insights
Generating reports for stakeholders
Collaborating with other departments

Industries like finance and healthcare rely heavily on data analysts. They help identify opportunities and improve efficiency. The demand for skilled analysts remains high across various sectors.

A career as a data analyst offers diverse paths for advancement. Continuous learning enhances proficiency and job value. Data analysts are vital to any data-driven organisation.

Machine Learning Engineer: Building Intelligent Systems

Machine learning engineers develop algorithms for learning from data. They enable machines to mimic human intelligence. Their work powers advancements in AI technologies.

Understanding programming and mathematics is essential. Skills in Python and R are highly relevant for this role. Engineers design systems that improve with experience.

Key duties of machine learning engineers:

Designing machine learning models
Training algorithms with data
Optimising model performance
Collaborating with data scientists

Industries such as e-commerce and tech prioritise these roles. They facilitate automation and intelligent systems. Engineers enhance customer experiences and operational efficiency.

Machine learning engineers have a prosperous career outlook. As AI evolves, its expertise becomes indispensable. They drive the future of intelligent technology.

Data Engineer: Architecting the Data Ecosystem

Data engineers build frameworks for data management. Their work ensures smooth data flow across an organisation. They focus on data storage, processing, and retrieval.

Key skills include proficiency in SQL and data warehousing. Engineers must understand distributed systems and cloud solutions. Their role involves creating scalable data pipelines.

Core responsibilities of data engineers:

Developing data architecture
Maintaining data infrastructure
Ensuring data quality and integrity
Working with data scientists for integration

Industries from technology to retail require skilled data engineers. They support data-driven projects and infrastructure. Demand for these professionals is growing rapidly.

Data engineers work in a dynamic environment with constantly evolving tools. They are crucial in supporting data science initiatives. Their work forms the foundation of data science ecosystems.

AI Specialist: Shaping the Future of Automation

AI specialists focus on creating intelligent systems. They develop models that replicate cognitive functions, such as learning. Their innovation drives the future of technology.

AI specialists need strong analytical skills and domain knowledge. They should be familiar with natural language processing and robotics. Continuous advancement is key in this field.

Primary tasks of AI specialists:

Developing AI models for automation
Implementing machine learning algorithms
Collaborating on AI system design
Monitoring AI system efficiency

These professionals work across various sectors like gaming and healthcare. Their contributions advance operational capabilities. AI specialists are at the frontier of innovation.

The rise of AI heralds new opportunities for skilled specialists. Their impact on automation will be long-lasting. AI specialists shape technological progress through groundbreaking work.

Business Intelligence Analyst: Turning Data into Strategy

Business intelligence analysts transform data into actionable strategies. They help businesses make tactical and operational decisions. Analysts use data to forecast trends and outcomes.

Strong analytical skills are necessary for success. Familiarity with data visualisation tools like Tableau is crucial. Communication skills are equally important.

Key responsibilities include:

Designing analytical reports
Interpreting business data
Presenting recommendations to management
Tracking key performance indicators (KPIs)

These professionals are integral to sectors like marketing and finance. Their insights support strategic planning and decision-making. Businesses value their ability to uncover meaningful data stories.

The role of a business intelligence analyst combines technology and business acumen. It’s a dynamic position with significant influence. Analysts guide organisations toward achieving strategic goals.

Emerging Data Science Roles: What’s Next?

The field of data science is rapidly evolving. As technology advances, new roles are emerging. These roles address the complex data challenges of tomorrow.

One notable trend is the rise of specialised positions. These positions focus on niche areas within data science. They offer unique career opportunities for professionals.

Future data science roles might include:

Quantum Data Scientist
Ethical Data Specialist
Data Governance Expert
Automated Systems Analyst

Quantum Data Scientists explore quantum computing applications. Their work could revolutionise data processing speeds. They are essential to the future of big data solutions.

Ethical Data Specialists address issues of fairness and bias. These roles ensure data practices align with ethical standards. They help organisations maintain trust and integrity.

As data science evolves, so do the roles it encompasses. New specialities will reflect technological and ethical concerns. A proactive approach ensures professionals remain competitive.

Industries Driving Demand for Data Science Professionals

Data science is integral across various sectors. Each industry leverages data to enhance operations and decision-making processes. As a result, the demand for skilled professionals continues to grow.

Healthcare benefits significantly from data science insights. The sector uses data for patient care optimisation and precision medicine. This demand is expected to increase over time.

Finance also thrives on data analysis. Financial institutions rely on data for risk management and fraud detection. Professionals in this field play a crucial role in safeguarding financial stability.

Technology companies are at the forefront of data science innovation. They utilise advanced data analytics to inform product development and enhance user engagement. This industry remains a crucial employer of data professionals.

Key sectors driving demand include:

Healthcare
Finance
Technology
E-commerce
Manufacturing

In e-commerce, data science optimises customer experiences. Analysing consumer behaviour helps tailor products and services. It ultimately increases sales and customer satisfaction.

Manufacturing uses data to streamline processes and reduce costs. Predictive maintenance and quality control are critical applications. These improvements lead to increased efficiency and productivity.

How to Choose the Right Data Science Course for Your Career Goals

Selecting an appropriate data science course is vital for success. It requires careful consideration of personal and professional aspirations. A well-chosen course can have a significant impact on career progression.

Begin by identifying your career goals. Understanding these can guide your course choice. Consider what skills you wish to develop or enhance.

Next, thoroughly evaluate the course content. Ensure it covers essential data science skills such as programming and statistical analysis. Check if it aligns with the areas you aim to specialise in.

Accreditation and instructor expertise are also crucial. Courses from reputable institutions or led by industry experts hold more value. They often provide up-to-date knowledge and practical insights.

Key factors to consider include:

Relevance to career goals
Accreditation and instructor expertise
Course content and structure
Flexibility and delivery mode (online, in-person)
Cost and time commitment

Finally, consider the course format. Online data science courses offer flexibility and convenience. They can be an excellent option if you need to balance work and learning.

Building a Successful Data Science Career: Tips and Resources

Navigating the path to a thriving data science career can be a complex process. However, with the right strategies, success is achievable. Embrace continuous learning and stay adaptable.

Networking is a powerful tool in building your career. Engage with data science communities online and offline. They provide support and the chance to learn from peers.

Enhancing your data science skills is essential for advancement. Regularly update your knowledge with the latest trends and technologies. Employers value candidates who demonstrate their ongoing commitment to learning.

Useful tips and resources for success:

Participate in data science forums and webinars
Engage in projects to apply your skills practically
Pursue data science certifications for career growth
Read industry reports and journals to stay informed
Connect with professionals on platforms like LinkedIn

Remember, building a successful career in data science is a dynamic and evolving journey. Each step you take opens new opportunities. Stay curious, keep learning, and seize networking opportunities to reach your goals.

Conclusion: Preparing for a Data-Driven Future

As we approach 2030, the landscape of data science continues to evolve. The demand for data-driven insights is expected to continue growing, shaping various sectors. Therefore, aspiring data professionals must equip themselves for the changes ahead.

Data science courses and certifications play a crucial role in acquiring skills and advancing careers. They provide the necessary foundation and keep professionals at the forefront of industry developments. Continuous learning is not optional—it’s essential.

Ultimately, adopting a future-oriented mindset is crucial. Stay open to new technologies and methodologies in the field. By doing so, you can effectively position yourself in the data-driven world, ready to seize upcoming opportunities and make substantial impacts.

How Imarticus’ Data Science Program Can Help You Succeed

Imarticus Learning’s Postgraduate Program in Data Science and Analytics is meticulously designed to equip learners with industry-relevant skills and hands-on experience needed to thrive in the fast-growing data science job market. With a curriculum aligned to real-world applications and developed in collaboration with industry leaders like IBM, the program offers:

Job Assurance with Guaranteed Interviews: Get 100% job assurance through dedicated placement support and a guaranteed 10 interview opportunities.
6-Month, Industry-Aligned Curriculum: Master key data science tools and techniques like Python, SQL, Machine Learning, Tableau, Power BI, and Deep Learning, structured into foundational, core, and advanced modules.
Live Training with Real-World Projects: Learn from expert faculty through live online sessions and apply your knowledge to 25+ industry-relevant projects and case studies.
10+ Industry Tools: Gain proficiency in widely used data science tools and platforms, increasing your job readiness.
No Prior Coding Background Needed: Ideal for beginners, this program starts from scratch and gradually builds advanced competencies.
Soft Skills and Career Services: Dedicated soft skill training, resume building, interview preparation, and career mentorship to make you job-ready.
Flexible Learning Options: Designed for working professionals and freshers alike, with a practical approach that suits different learning needs.

With alumni placed in top companies like IBM, Amazon, Genpact, Flipkart, and more, Imarticus’ program offers a solid launchpad for a rewarding career in data science.

How Long Does It Take to Complete the ACCA Course?

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

The ACCA course is regarded as one of the most familiar and recognised qualifications in the accountancy and finance profession. Recognise worldwide in 180+ countries, this certification is apt for candidates who want to develop a career as an auditor, tax expert, financial reporter, or adviser.

But one of the most asked questions by aspirants remains: How long does it take to complete the ACCA course?

Whether you’re a student, working professional, or career switcher, the ACCA journey looks different for everyone. This blog breaks down the ACCA course duration, exam structure, how long does acca take, acca exam timeline, acca study plan duration, fastest way to finish acca, influencing factors, and how to accelerate your path to becoming a globally certified professional.

What Is the ACCA Course?

Association of Chartered Certified Accountants (ACCA) is a global and professional UK-based accountancy body with an internationally recognised accounting qualification. The course has 13 exams on three levels:

Applied Knowledge (BT, MA, FA)
Applied Skills (LW, PM, TX, FR, AA, FM)
Strategic Professional (SBL, SBR + 2 optional papers)

Along with the exams, ACCA applicants must pass the Ethics and Professional Skills module and 36 months of relevant work experience in their respective fields to become fully certified.

ACCA Course Duration: At A Glance

Level	Number of Exams	Average Duration
Applied Knowledge	3	6 months
Applied Skills	6	12–15 months
Strategic Professional	4	12–18 months
Ethics Module & PER	–	Done concurrently with exams
Total	13 Exams	2.5–3 years

Time Required to complete ACCA Depending Upon Varied Backgrounds?

New Commerce Graduates

Can be eligible for exemptions in 3–5 papers.
Average time: 2–2.5 years.

Executives With Work Experience (MBA/CA Inter)

High possibility of exemptions.
There is a possibility of completion of the course in 1.5–2 years.

Non-Commerce Graduates

From zero exemptions.
Duration: 3 to 3.5 years depending on speed.

Facts That Help Determine ACCA Course Duration

1. Exemptions

Depending on the level of your degree, you can be exempt from up to 9 exams. Check the ACCA official exemptions calculator to see what it means to you.

BCom/BAF: Up to 5 exemptions
MBA: Up to 6 exemptions
CA: Up to 9 exemptions

Check your exemptions at ACCA exemptions calculator

2. Study Mode

Full-Time Students: Can finish ACCA in less time by sitting for 2–3 exams at a time.
Part-Time Students: Generally prefer sitting for 1–2 exams at a time due to work commitments.

3. Exam Scheduling

You can sit for up to 4 exams in a session and 8 in a year under ACCA. Proper scheduling of sessions can reduce your overall tenure by a significant amount.

4.Pass Rate and Study

Global average pass rates are approximately:

Applied Knowledge: 65%
Applied Skills: 50–55%
Strategic Professional: 40–50%

Proper preparation and focused mentoring can reduce re-attempts and save time.

Realistic Scenarios for ACCA Completion

Profile	Exemptions	Study Time	Total Duration
BCom Student (Year 2)	3	2.5 years	~3 years
CA Inter + Working	6	15 months	~2 years
MBA Finance + Full-time Job	5	18–20 months	~2.5 years
Non-Commerce Grad (No Job)	0	Full-time	~3.5 years

Quickest Way to Complete ACCA

Become ACCA certified in 18–24 months? This is what your plan must be:

Step-by-Step Plan:

Use maximum exemptions based on prior qualifications
Appear for 2 exams a session (4 exams/year)
If required, go through structured mentorship (e.g., Imarticus ACCA programme)
PER and Ethics module done in parallel
Avoid exam gaps

Pro Tip:

Don’t leave too much time between sessions. Momentum is everything when it comes to professional certifications.

Unique Insight: ACCA vs Other Global Qualifications

Course	Duration	Global Reach	Flexibility	Career Roles
ACCA	2–3 yrs	180+ countries	High	Finance, Audit, Tax, Advisory
CPA	1.5–2 yrs	130+ countries	Medium	Accounting, Finance
CA (India)	4.5–5 yrs	India-focused	Low	Audit, Accounting
MBA	1–2 yrs	Global	Medium	Finance, Consulting, Strategy

ACCA is unique compared to other global qualifications in being modular, attainable at a distance, and with extremely high usage worldwide.

Statistics: In the United States, Glassdoor reports that the estimated total pay range for an Accountant at ACCA is between $62,000 and $98,000 per year, which includes base salary and additional pay. (Sources)

What Is the Practical Experience Requirement (PER)?

ACCA students need to attain in order to become a member:

36 months supervised work experience
Performance Objectives (POs) beyond core skills
Signed off by an approved mentor

You can acquire PER through studies, thus making it possible to complete both exams and experience within 3 years.

How Does Imarticus Assist?

Imarticus Learning provides one of the most organised and industry-related ACCA preparation courses in India. The salient features are:

100% job guarantee and pass-assurance
ACCA affiliate and member guidance
Live and recorded doubt-clarifying sessions with experts
Convenient online sessions to suit working professionals
Planned study plans corresponding to ACCA’s world map

How did I clear my ACCA while working full time at BCG | CA Nandini Agrawal

FAQs about ACCA Course Duration

How long is the ACCA course?

On average, 1.5 to 2 years.

Can I complete ACCA in 1.5 years?

Yes, with work experience and full-time study.

Is the ACCA course accompanied by work experience?

Yes, 36 months of PER are needed.

How long is the maximum ACCA duration?

7 years to complete Strategic Professional papers.

Can I study ACCA while I’m working?

Yes. Most students study it part-time.

How often are ACCA exams taken?

Four sessions a year: March, June, September, December.

Do I need to complete the Ethics module prior to exam completion?

It’s best to complete it along with the Strategic Professional level.

What happens if I fail an ACCA paper?

You can re-sit it in the following session. There is no restriction on attempts.

Conclusion

Key Takeaways:

ACCA course duration ranges from 18 months to 2 years, depending on your exemptions, effort, and planning.
Strategic learning and guided support can help you finish early and negate the cost of re-takes.
Adaptable with global recognition, ACCA remains one of the top professional qualifications for emerging accountants and finance professionals.

Your ACCA success journey starts today—with the right platform, support, and strategy.

✅ 100% job guarantee and pass assurance

✅ Find expert-guided training

✅ Take your exemptions

✅ Enjoy an international borders career

???? Start with Imarticus ACCA Programme

Data Scientist Qualifications: A Guide for Career Changers

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Table of Contents

Introduction: Is Data Science for You?
Qualifications Needed for Data Science
How to Become a Data Scientist
Data Science Career Transition: Tips for Professionals
Best Degrees for Data Science
Skills Required for Data Scientists
Certifications for Data Science Careers
Breaking into Data Science Without Experience
Key Takeaways
FAQs
Conclusion

What Is Data Science: Is It for You?

Have you ever felt intrigued by numbers, patterns, or predictions? Do you often find yourself wondering how Netflix knows what to recommend next or how Google reads your thoughts? If you do-and if you are contemplating a career change-you could fit the bill for data science to a T. In this guide we explain the qualifications you will need as a data scientist to break into this fast-offering field, especially if you are doing so from a mid-career switch.

Qualifications Required for Data Science

Most people mistakenly believe that one needs a Ph.D. to qualify as a data scientist. Certainly, that can help; however, it is not a requirement. What is most important is a combination of education, analytical thinking, and tool-based skills. This section will explain to you the baseline qualifications needed for data science, especially for non-technical people.

Here’s a quick comparison of qualifications typically expected for entry-level vs. mid-level roles:

Role Level	Qualification Requirements	Preferred Background
Entry-Level	Bachelor’s in STEM, basic programming skills	Math, CS, Economics
Mid-Level	Master’s or certifications, industry experience	Engineering, Business, Stats
Senior-Level	Advanced degrees or 5+ yrs experience	Specialized domain expertise

Key Points:

A degree is important, but hands-on skills often matter more.
Employers value problem-solving abilities and project experience.

How to Become a Data Scientist

So, how do you start the journey? There isn’t just one road to data science. This section outlines how to tailor your path based on where you’re starting.

Here’s a basic roadmap for how to become a data scientist:

Learn Python or R – These are the industry favourites.
Master SQL – Knowing how to manipulate databases is crucial.
Understand Statistics – Core concepts like probability, regression, and distributions are musts.
Build Portfolio Projects – Use real datasets to solve problems.
Network and Join Communities – LinkedIn groups, Kaggle competitions, and meetups matter.

Whether you’re coming from finance, marketing, or engineering.. data science skills can give you an edge.

Harvard Business Review called data science- the “sexiest job of the 21st century”. Why not make it yours?

Data Science Career Transition: Tips for Professionals

Changing careers isn’t easy—but it’s entirely possible. With the right strategy, even professionals with no tech background can break into data science. This section is tailored for those making a data science career transition.

Mid-career professionals often hesitate to pivot due to fear of starting over. But data science is surprisingly welcoming to diverse backgrounds. Your business acumen, domain expertise, and communication skills are valuable in this field. This guide will show you how to transition effectively.

Top Tips for Career Changers:

Bridge the gap with certification programs.
Highlight analytical work in your past roles.
Start freelancing or take internships.
Use LinkedIn to showcase learning progress.
Get a mentor or career coach.

Best Degrees for Data Science

While many data scientists have traditional STEM backgrounds, there’s increasing diversity in academic paths. Here’s what you should know about the best degrees for data science.

Not sure if your degree qualifies? Don’t worry. Employers are open to various academic backgrounds as long as you can demonstrate competency in data-related skills. However, some degrees are more aligned with the field’s demands.

Degree	Relevance to Data Science	Special Notes
B.Sc in Computer Science	High	Offers solid programming foundation
B.Tech in IT	High	Includes software and systems focus
B.Sc in Math/Statistics	Medium to High	Strong in analytical foundations
MBA (with analytics)	Medium	Good for business-data intersection

Skills Required for Data Scientists

Data scientist qualifications go beyond degrees and certificates. Skills—both technical and soft—are what really matter in real-world jobs. Let’s explore the skills required for data scientists today.

Employers look for more than just theory. You need hands-on, job-ready skills. A good mix of coding, data wrangling, statistical modelling, and communication is crucial. This section breaks down the skill sets most valued in India’s data science job market.

Must-Have Skills:

Programming: Python, R, SQL
Data Handling: Pandas, NumPy, Excel
Visualization: Tableau, Power BI, Matplotlib
Machine Learning: Scikit-learn, TensorFlow, basic algorithms
Communication: Ability to present findings to non-tech stakeholders

Certifications for Data Science Careers

If you’re hoping to get into the field fast, certifications can help address the experience disadvantage. Here’s list of certifications for a career in data science that employers recognise.

Not everyone is able to do a full-time education. This is where certifications can help out because they are quicker, and more narrowly focused on the current industry needs. Picking the right one can improve your chances of getting the job you want.

Top Certifications:

Google Data Analytics Certificate
IBM Data Science Professional Certificate
Microsoft Certified: Azure Data Scientist Associate
Imarticus Learning – PG Program in Data Science and Analytics

LinkedIn Learning also offers certain courses aligned with industry skills.

Breaking into Data Science Without Experience

This is the most common concern for career changers: “Can I really do this without experience?” The answer is yes—with planning and persistence.

If you’re breaking into data science without experience, focus on what you can control—your learning path, your portfolio, and your online presence. Companies often hire based on demonstrable skills, not just resumes. Here’s how to start.

Actionable Tips:

Build 3–4 strong projects and publish on GitHub
Start a blog sharing your learning journey
Contribute to open-source or volunteer data projects
Take part in Kaggle competitions
Attend webinars and industry conferences

Key Takeaways

Data scientist qualifications include a mix of degrees, certifications, and skills.
You don’t need a traditional tech background to start.
Certifications and self-learning platforms can fast-track your career switch.
Building a portfolio is essential for showcasing your abilities.
Employers value practical skills and communication as much as formal education.

FAQs

1. Do I need a Ph.D. to become a data scientist?

No.. most entry-level roles only require a bachelor’s or master’s degree along with relevant skills in programming & analytics.

2. Can I become a data scientist at 35 or older?

Absolutely! Many professionals successfully transition into data science in their 30s or 40s with the right preparation and learning path.

3. Which certification is best for a career switch?

Imarticus Learning’s PG program is industry-focused and great for learners. Google’s Data Analytics Certificate or IBM’s Data Science Certificate are also some starting points.

4. Is programming a requirement?

Yes, a bit of programming experience is required (in particular, knowledge of Python or R), but you don’t have to be a programmer.

5. How long to goal-to-job-ready?

If you focus, you can be ready for the job market in about 6–12 months.

6. Generally – what do data scientists earn in India?

Entry-level jobs range from ₹6–10 LPA on average, while experienced data scientists can earn over ₹25 LPA.

7. Are there remote opportunities in data science?

Yes, many data science roles offer remote or hybrid working models.

8. What’s more important—degree or portfolio?

A strong portfolio often outweighs a traditional degree, especially for career changers.

9. Can I switch from a non-tech role like HR or marketing?

Yes! Your domain knowledge combined with new data skills can give you a unique edge.

10. Should I pursue a Master’s in Data Science?

If time and budget allow, yes. But short-term certifications are also highly effective.

Conclusion

Stepping into the data science field may seem challenging but it’s more probable than ever to be able to break in. With the right data scientist qualifications, specific learning plan, and compelling portfolio, non-technical professionals can also get into this high demand field. The future is data-driven so it is a good time to explore your options and get involved.

Top Paying Companies for Data Scientist Salary in India

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Data science is no longer a buzzword—it’s a business imperative. Now that the world is data-driven, companies rely on data scientists to interpret complicated data and make strategic decisions. This growing demand is directly converted into huge compensation levels.

If you’re considering the most rewarding data science roles, this guide will walk you through the top companies offering the best data scientist salary in India, why they’re attractive, and how you can get in.

Why Data Scientist Salary Is Rising in India

The average pay of a data scientist in India has risen by more than 35% in the past five years, fueled by digitization, the adoption of artificial intelligence, and growing volumes of consumer data. From global MNCs to Indian tech startups, every industry is recruiting data professionals in large numbers. (Source)

A recent report by Nasscom noted that India possesses 11% of the world’s data science talent and is expected to double its talent pool by the year 2026. This trend is evident in salary levels, especially for top employers.

Top Data Science Employers in India

The following are the top data science employers in India who are reputed to pay more than market-average salaries to data science professionals:

1. Google India

Average Salary: ₹28–40 LPA
Credited for hiring PhD-level talent, Google’s data scientists work on groundbreaking AI and deep learning initiatives.
Benefits: Flexible work, top-notch infrastructure, and stock options.

2. Amazon India

Average Salary: ₹20–35 LPA
Data science is deeply embedded in every suggestion engine, pricing, and supply chain model.
One of the strongest hiring managers of entry through senior-level data experts.

3. JP Morgan Chase

Average Salary: ₹18–30 LPA
Quantitative analytics, credit risk modelling, and fraud detection are the functions filled.
Offers specialised algorithmic trading and financial modelling roles.

4. Microsoft India

Average Salary: ₹22–32 LPA
Focused on enterprise AI solutions, cloud data architecture, and AI research labs.
High emphasis on upskilling and global work.

5. Fractal Analytics

Average Salary: ₹15–22 LPA
One of the best companies for data scientists to work on AI-based consumer insights.
Known for innovation-first culture and industry exposure.

Table: Top MNCs for Data Science Job & Salaries

Company	Average Salary (INR)	Best Known For
Google	₹28–40 LPA	Machine Learning & AI Research
Amazon	₹20–35 LPA	E-commerce & Logistics Algorithms
JP Morgan Chase	₹18–30 LPA	Risk & Financial Modelling
Microsoft	₹22–32 LPA	Enterprise AI, Cloud Platforms
Fractal Analytics	₹15–22 LPA	AI for Consumer Behaviour

Tech Firms Actively Recruiting Data Scientists

If you are open to jobs outside the top 5, here are additional tech firms actively recruiting data scientists in India:

Tata Consultancy Services (TCS)
Flipkart
Zoho
Accenture
IBM India

These companies may not pay top dollar salary initially but provide good exposure, especially for junior professionals.

Average Salary of Data Scientist in India based on Experience

Experience Level	Average Salary (INR)
0–2 years	₹6–10 LPA
3–5 years	₹12–18 LPA
6–10 years	₹20–30 LPA
10+ years	₹35 LPA and above

Salaries also vary based on locations, with Bengaluru, Hyderabad, and Pune being at the top as hubs for data science careers.

Top Data Science Companies: Why They Are Top Favorites?

Project Ownership: Top companies allow data scientists to own end-to-end products.
Global Exposure: MNCs generally allow collaboration with global teams.
Research Focus: Google and Microsoft, for example, offer employment aside from commercial applications—pushing AI research.
Career Acceleration: Immediate growth, worldwide tool access, and cross-functional learning opportunities.

Humanised Learning Path: Where To Start?

Good companies offer great salaries, but it requires the right skills to get into those companies. That’s where industry-ready programs save the day.

One of those highlights is Imarticus Learning’s Postgraduate Program in Data Science and Analytics. Designed by industry experts, the program explores:

Python, SQL, and Power BI
Machine Learning & Deep Learning
Tableau and Generative AI
Live projects and placement guidance

A Day In My Life As A Data Scientist in Gurgaon India | CARS24

While awareness is increasing, the talent pipeline is thin. Analytics India Magazine tells us that only 45% of graduates from data science courses are industry-ready.

This is an enormous opportunity. If you’re skilled and certified, companies are eager to pay you top packages—no matter if you’re non-technical.

FAQs About Data Scientist Salary in India

Q1. What is the highest data scientist salary in India?

A1. Google and Amazon senior professionals can receive ₹40–45 LPA.

Q2. What industry provides the best to data scientists?

A2. Finance, e-commerce, and AI research labs are the front runners.

Q3. Are MNCs better payers than startups?

A3. Startups can offer high stock options and early responsibility, while MNCs offer stability and growth.

Q4. How much can a fresher data scientist earn?

A4. ₹4–10 LPA anywhere, based on where you are and what you know.

Q5. Which Indian city is best for data scientists?

A5. Bengaluru is the hub, followed by Hyderabad, Pune, and Gurgaon.

Q6. Are online data science courses worth it?

A6. Yes, if they offer industry projects and placement support.

Q7. How can I transition to data science without coding skills?

A7. A few tools like Power BI and Tableau allow one to get into analytics with zero coding.

Q8. How much difference is there between analysts and data scientists’ salaries?

A8. Analysts receive ₹4–8 LPA, while data scientists can start at ₹10 LPA and scale very high.

Q9. Is certification a factor in salary?

A9. Yes. Those who have certifications from highly credible institutions earn a much better salary.

Q10. Which of the tools should you be familiar with, according to top employers?

A10. Python, SQL, Power BI, Tableau, TensorFlow, and cloud platforms like AWS or Azure.

Conclusion: What This Means for You

Whether you’re a fresher or an established working professional who wishes to shift, data science remains one of the most profitable career choices. Salaries do not just indicate demand, but also the relevance that firms are placing on making decisions based on data.

Key Takeaways

The India salary for data scientists ranges between ₹6 LPA and ₹40+ LPA depending upon skill and experience.
Google, Amazon, JP Morgan, and Microsoft are some of the best data science employers in India, with high salaries and growth prospects.
Upskilling through structured programs like Imarticus Learning’s Data Science & Analytics course can prepare you bold enough to take these roles.

Explore the Imarticus Learning Postgraduate Program in Data Science & Analytics to boost your journey to the best-paying data science roles.

How Much Is Data Scientists Salary per Month?

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Introduction

With big data, of course, comes the work of data scientists in the front, and companies are motivated to make sense of and leverage huge amounts of information. Where there is high demand, though, there are great rewards packages, and data science is one of the highest-paying career choices.

But how much, exactly, do data scientists earn per month or what are the average data science earnings? This blog does not waste time, providing salary information, global comparisons, and trending data to shed light on mean data science compensation.

Data Scientist Salary Summary

The mean monthly data scientist pay is extremely volatile based on location, experience, and aptitude. Data scientists worldwide receive between ₹100,000 in India and about $10,000 in the US per month. Glassdoor quotes an international mean monthly rate of between roughly $7,000 and $12,000 based on the value placed on data abilities.

Salary prospects of data scientists increase manifold with experience and domain specialisation. This is even greater in industries like finance, healthcare, technology, and e-commerce, where decision-making is data-driven and has a direct impact on profitability.

Average monthly salary in a nutshell:

Experience Level	Avg. Indian Salary (per month)
Freshers	₹50,000 – ₹1.2 lakh
Mid-level	₹1.5 lakh – ₹3 lakh
Senior	₹3.5 lakh – ₹6 lakh+

Factors Impacting Data Scientist Salaries

There are certain key factors which determine the salary of data scientists:

1. Experience

Entry-level positions largely involve the execution of simpler data work, while high-level positions involve high-level decision-making, thereby fetching much greater salaries.

2. Education

Individuals with a higher qualification (Master’s or Ph.D.) usually get far more money. Machine learning or big data analytics certification also adds earning capacity.

3. Industry

Finance and technical sectors that critically rely on data-driven accurate information usually pay higher than other sectors.

4. Geographical Location

Market demand and cost of living play a major role in salaries. High-cost geographies like the US, UK, and Australia offer higher compensation packages.

5. Skill Set

The ability to code in a couple of programming languages like Python, SQL, R, or knowledge of machine learning tools works magic with the dollars earned.

Entry-Level vs Senior Data Scientist Salary

Diverging quite extensively for junior and senior data scientists could be the pay differences. Junior staff members earn $5,000–$7,000 per month, performing entirely routine analytical and data processing work.

Senior data scientists are responsible for big data projects and earn $13,000–$16,000 or more per month. Managerial experience, greater analytical proficiency, and ample experience are the bases for senior roles, which is rewarded with increased compensation.

Global Salary Comparisons

Country	Average Monthly Salary
United States	$10,000 – $12,000
United Kingdom	£4,000 – £6,000
Canada	CAD 8,000 – CAD 10,000
Australia	AUD 9,000 – AUD 11,000
Germany	€6,000 – €8,000
India	₹100,000 – ₹150,000

They are mirrorings of differences in local cost of living, economic conditions, and local demand in each market.

Freelance vs Full-Time Data Science Salary

Full-time or freelance working data scientist is notable while considering stability of income and take-home pay:

Freelance: Freelancers are paid hourly rates ($50–$250/hr), and their earnings depend on the volume of work. Freelancers may have more possible earnings but have income uncertainty.

Full-Time: Full-time data scientists are paid fixed monthly salaries and other perks like medical cover, leaves, and retirement schemes, which are secure.

Highest-Paying Data Science Jobs

There are certain specialised data science jobs with very high pay due to special know-how and significant contribution:

Machine Learning Engineer: Creates and builds forecasting models ($11,000–$14,000/month).
Data Architect: Manages sophisticated data infrastructure ($12,000–$15,000/month).
AI Research Scientist: Contributes to the establishment of AI methods ($13,000–$17,000/month).
Big Data Engineer: Manages big data systems ($10,000–$13,000/month).

Current Trends in Data Science Salary

The growing need for data-driven solutions in industry and business is pushing data science salary trends in the positive direction. The advancements in AI, machine learning, and cloud computing have created a rising demand for talented data scientists, and salaries are rising everywhere too.

All About Data Science and Analytics Course by Imarticus Leaning | Data Science For Beginners

New careers in the form of AI experts, data ethics officers, and predictive analysts also hold out a pink financial future for data professionals. Companies are heavily investing in holding on to talent by offering high salaries, competitive benefits, and highly profitable career advancement opportunities.

FAQs

1. What is the average global data scientist salary?

Average ranges from ₹4 LPA to ₹8 LPA and are predominantly location, industry, and experience dependent.

2. Does experience overshadow data scientist salaries?

Yes, veterans or experienced people generally receive twice or thrice as much as freshers.

3. Which countries offer the highest paying data scientist salaries?

The United States, Australia, and the United Kingdom tend to offer the best paying salaries due to demand and cost of living.

4. What are the levels of education that drive salary rates up in data science?

Higher levels such as Master’s or Ph.D. in the same field drive earnings much higher.

5. Which industries provide the highest data scientist pay?

Technology, finance, health, and consulting firms are known to provide best pay.

6. What are the essential skills that drive data scientist salaries?

Competencies like Python, machine learning, deep learning, and statistical analysis are in demand and are well paid.

7. Is data science a new career in terms of pay?

Yes, the pay for data science has risen over time and will keep on rising even more with increasing demand.

8. How does certification affect the data science pay?

Professional credentials do count towards earning higher money by crediting certain competencies and aptitudes.

10. Can work in data science remotely achieve competitive rates?

Indeed, remote working does equate to competitive marketplace rates, particularly in multi-nation technology companies.

Conclusion and Key Takeaways

Data science is a high-cost, high-emphasis profession signaling the irreplaceable worth of data in today’s businesses. Three quick nuggets are:

Experience and Education: The greater the ranks, they have a direct relation to much greater pay.
Location and Industry: Industries of high demand and locations offer the highest pay.
Skill Specialisation: Higher, specialisation skills have a larger role in earning capability.

Planning on changing careers and salary prospects in data science? Take a look at our Postgraduate Program in Data Science & Analytics at Imarticus Learning and become a most-desired data specialist today!

What’s the Average Data Science Course Duration? Breaking It Down by Program

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Introduction

So, you are probably wanting to enter in the world of data science, but wonder: how much time will it take? For students determining their next moves and working practitioners choosing to upskill, knowing about the duration of a data science course will help them make an informed choice. There are different types of learning available, from a weekend boot camp to a two-year master’s program, that may confuse you. But, fear not; we are going to lay it all down for you.

Table of Contents

Overview of Data Science Course Duration
Data Science Course Length: Short-Term vs Long-Term Options
Duration of Data Science Programs: Comparing by Type
Data Science Certification Timeline: Quick Upskilling Paths
Full-time vs Part-time Data Science Courses: What Suits You?
How Long Does It Take to Learn Data Science?: Influencing Factors
Master’s in Data Science Duration: What You Need to Know
Bootcamp vs Degree in Data Science: A Timeline Comparison
Key Takeaways
FAQs
Conclusion

Overview of Data Science Course Duration

Before diving into specifics -let’s get an overall view. The data science course duration varies drastically based on your choice of program & learning pace. For instance, bootcamps may wrap up in a few months, while a full-fledged degree could stretch over two years.

Here’s a quick glance at typical durations:

Program Type	Average Duration
Bootcamp	3 to 6 months
Certification Program	6 to 12 months
Bachelor’s Degree	3 to 4 years
Master’s in Data Science	1.5 to 2 years
Part-Time Programs	6 to 24 months

Data Science Course Length: Short-Term vs Long-Term Options

Whether you’re in a hurry to switch careers or looking to build a strong academic foundation, data science course length matters. The shorter options are often intensive, while longer courses go deeper into concepts.

Short-Term Courses (3-6 months):

Bootcamps
Online Certifications
Corporate Upskilling Modules

Long-Term Courses (1-2+ years):

Master’s Degrees
Integrated Postgraduate Diplomas
Online Degree Programs

Choose based on your learning style, goals, and time availability.

Duration of Data Science Programs: Comparing by Type

Understanding the duration of data science programs by category can help clarify which path works for you. Here’s a breakdown by program format:

Program Category	Duration
Online MOOCs (Coursera, edX)	4 to 8 weeks (modular)
Certificate Courses	3 to 12 months
Postgraduate Diplomas	10 to 18 months
Executive Courses	6 to 10 months
University Degrees	1.5 to 4 years

Explore platforms like edX, Coursera, or Harvard Online for high-authority examples.

Data Science Certification Timeline: Quick Upskilling Paths

If you’re aiming for fast-track learning.. then focusing on the data science certification timeline is essential. These are ideal for professionals who want to add value to their resume quickly.

Common timelines include:

Introductory certifications: 2-4 months
Intermediate to advanced courses: 6-9 months
Specialized tracks (like NLP or Deep Learning): 3-6 months

Certifications are flexible, often online, and backed by industry relevance.

Full-time vs Part-time Data Science Courses: What Suits You?

When weighing your options, think about your current commitments. Full-time vs part-time data science courses vary not only in length but also in learning intensity.

Full-Time Courses:

Duration: 6 to 24 months
Immersive and faster-paced
Ideal for students or those on a career break

Part-Time Courses:

Duration: 12 to 36 months
Suited for working professionals
Often asynchronous and more flexible

Your availability determines the best mode for you.

How Long Does It Take to Learn Data Science?: Influencing Factors

The big question — how long does it take to learn data science? The answer depends on multiple factors:

Your existing knowledge of math/stats/programming
Time commitment per week
Learning path chosen (bootcamp, certificate, degree)
Career goals (entry-level analyst vs data scientist)

Estimated timeframes:

Beginner to job-ready: 6 to 12 months with focused study
Career switch to senior roles: 1.5 to 3 years

Your progress will also depend on the consistency of your practice, real-world project exposure, and mentorship availability. Platforms offering guided capstones and community support may reduce your learning curve significantly.

Consistency is more important than speed.

Master’s in Data Science Duration: What You Need to Know

For those seeking academic depth and research opportunities, the Master’s in Data Science duration typically ranges from 1.5 to 2 years. Offered globally and in top Indian institutes, these programs offer intensive coursework.

Course modules often include:

Machine Learning & AI
Big Data Processing
Data Ethics
Capstone Projects

Some universities offer accelerated 12-month options, while others may go up to 24 months with internship components. You can also find flexible or hybrid options that accommodate professionals looking to study part-time while continuing their jobs.

Bootcamp vs Degree in Data Science: A Timeline Comparison

Let’s compare bootcamp vs degree in data science to see how they stack up in terms of time:

Criteria	Bootcamp	Degree Program
Duration	3-6 months	2-4 years
Career Support	Strong	Moderate
Cost	Lower	Higher
Ideal For	Quick upskilling	Long-term academic

Bootcamps like those by Springboard or General Assembly are gaining momentum in India for fast-track transitions.

Key Takeaways

Data Science course duration varies based on the format and your goals.
Short-term bootcamps can last 3-6 months, while degrees stretch up to 2-4 years.
Certification paths offer flexibility and career acceleration.
Full-time vs part-time courses differ in intensity and duration.
Choose based on how much time you can commit and what role you aim to land.

FAQs

1. What is the mean duration for a data science course in India?

The mean time is 6 months for bootcamps or certifications, and up to 2 years for master’s degrees with respect to course type and intensity.

2. Is it possible to get job-ready by taking a three-month data science bootcamp?

Yes, but it requires a high level of personal commitment, and little prior knowledge of coding or statistics will help.

3. Is part-time learning effective for data science?

Absolutely. Part-time courses are ideal for working professionals and offer flexibility while covering all core topics thoroughly.

4. How long does a data science certification take?

Most range from 4 to 9 months, with faster options available for foundational learning or specializations.

5. Are degree programs better than bootcamps?

Degrees offer academic depth, but bootcamps are quicker and often come with industry-led projects and job support.

6. Is it possible to learn all of data science online?

Yes, there are many online platforms that provide comprehensive online programs as they happen in person and self-paced programs.

7. Master’s in Data Science duration abroad vs. India?

International programs usually last 18- 24 months, and Indian master’s can vary from 12 to 24 months depending on the university.

8. Do short-term courses have placement support?

Many reputable institutes do offer placement assistance even for short-term or certification programs.

9. How long before I can land a data science job?

With dedicated study, projects, and internships, most learners get job-ready in 6 to 12 months.

10. Are there weekend-only programs in data science?

Yes, several institutes offer weekend-only schedules for working professionals that span 6 to 18 months.

Conclusion

The data science course duration can be as short as 3 months or as long as 4 years. There’s no one-size-fits-all answer. The decisions hinge on personal background, commitment, and professional intentions. No path is immune to merit or demerits – from boot camps to master’s degrees. Key is to align one’s own scope and goals with any option chosen. And don’t forget – the journey is just as important as, if not more than, the destination.

External resource: How to become a Data Scientist

Can Non-Tech Students Apply? Understanding Eligibility for the Data Science Course

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Data Science is a lucrative career, intertwining analytics, technology, and innovative problem-solving to extract meaningful insights from massive databases. If you’re eager to learn about the eligibility for the Data Science Course and worry that a lack of technical expertise may hinder you, don’t worry.

Most non-technical professionals are now making a mark in data science, with areas of specialisation varying from data analysis to business decision-making. As the industry is growing, there must be an explanation of who can apply, what are the eligibility criteria for data science, and if non-coding data science jobs do exist or not.

In this step-by-step guide, we shall discuss Data Science Course Eligibility criteria, entry criteria for data science, and how non-technical students can pursue this career path.

We will also mention quality learning options, like Imarticus’ Postgraduate Programme in Data Science & Analytics, to guide you towards taking that initial step towards establishing a successful career.

Let’s explore how data science for beginners has become more accommodating than ever, and why companies are increasingly opening their doors to a diverse range of applicants.

What Are the Core Data Science Eligibility Criteria?

When it comes to data science, institutions often set certain foundational requirements. However, these vary significantly. Below are the most common data science eligibility criteria you’re likely to encounter:

Educational Qualifications

Most require a bachelor’s degree, but the subject matter can vary from economics to literature. STEM degrees are helpful, but they’re not often the only route to success.

Mathematical Aptitude

Basic statistics and probability knowledge is helpful. Not always required, but helpful to be acquainted with the application of the words mean, median, and standard deviation.

Programming Fundamentals

Certain courses want you to have prior coding experience, but others are for beginners. If you’re starting from scratch, search for courses that include beginner Python or R modules as part of the course.

English Proficiency

Since most of the course material and lectures are done in English, institutes typically want you to be English proficient. Some even require you to produce an English proficiency test if you’re an international student.

Willingness to Learn

Maybe the most undervalued necessity is a curious mind. Data science is tough but worth it. A can-do mentality is priceless, particularly for tech students who wish to bridge gaps.

Despite these requirements seeming daunting, do not eliminate data science for beginners from your list. Data science for beginners has never been so cool.

Many schools explicitly structure their coursework to accommodate beginners, providing assistance that develops strengths incrementally.

Data Science Non-Coding Jobs: Fact or Fiction

People tend to visualise someone deeply coding when they hear data science. But data science non-coding jobs are in abundance. Data science needs people to interpret the findings of the analysis and present them in an appropriate way to stakeholders.

Data Visualisation Specialist: All about taking raw data and making it persuasive graphs and dashboards. Although little coding may be required, visualisation tools such as Tableau or Power BI are more important to learn.

Business Analyst: Spans technical teams and management. Examines data-driven insights for decision-making without necessarily drilling into code.

Project Manager: Oversees end-to-end data projects, ensures timely delivery, and coordinates with technical experts, data engineers, and clients.

If you’ve ever wondered, “Can non-technical students learn data science?” the existence of these roles should be a testament to the field’s flexibility.

Organisations increasingly recognise that data solutions must fit business objectives and user needs—skills that often come from those with non-traditional backgrounds.

Can Non-Technical Students Learn Data Science?

The quick answer is yes. For each highly technical job, there exists a corresponding one that works with other sets of skills. More to the point, a person who is a business or humanities individual can specialise in data interpretation, storytelling, or even leadership positions in analytics teams.

When you’re making the decision of whether data science is for you, ask yourself: Do you enjoy learning new technology? Do you like patterns and puzzle-solving? If you answered yes, you already possess a major piece of data science entry requirements—a true passion for getting meaning out of data.

Data Science for Beginners: Why the Sudden Rush?

Data science for beginners has been increasing to popularity for very good reasons:

Data Explosion: Businesses generate and store more data than before.
Accessible Tools Emergence: Cloud solutions and code-friendly libraries make it easier to manage data challenges.
Competitive Advantage: Businesses look for professionals who have the ability to extract data to unearth secrets and spur creativity.

Based on IBM Analytics, the demand for data-savvy individuals is increasing. Institutions are thus eagerly recruiting students from various backgrounds to form groups that can handle multi-dimensional data issues.

Who Can Apply for Data Science Courses?

One of the questions that most prospective students want to know is, “Who can enroll in data science courses?” The general answer is that most applicants are welcome as long as they fulfill the minimum data science entry criteria. This may involve a bachelor’s degree, maths fundamentals, and a desire to learn core software packages.

Finance, healthcare, law, or even sociology professionals can draw on their domain expertise in data-driven fields. With specialized knowledge complemented by analytical techniques, you can be a valuable contributor to linking technical results to practical uses.

Brief Overview of Key Data Science Entry Requirements

Requirement	Description	Importance
Formal Education	Bachelor’s degree or equivalent in any subject	High
Mathematics Competence	Conversant with basic stats and probability	Medium
Programming Skills	Useful but not necessary (most introductory-level courses)	Variable
English Competency	Comprehension of course material, lectures, and projects	Medium
Curiosity & Commitment	Ability to learn repeatedly and adjust	Very High

These columns identify the overall picture. Naturally, each program could have its variations. But the overall trend remains the same: subject matter knowledge and a high enthusiasm to learn.

Best Data Science Courses for Non-Tech Students

So, which are the best data science courses for non-technical students? The trick is to find courses that cut and slice challenging topics:

Introductory Programming: Choose courses that instruct in Python, R, or even SQL from the ground up.

Applied Projects: Learning from actual datasets sets a firmer base.

Flexible Schedules: Most part-time or online courses are made for full-time employees.

Recommended options are Imarticus’ Postgraduate Programme in Data Science & Analytics. Further, sites like Coursera and edX acquaint you with concepts at the foundational level in a structured way. Alternatively, KDnuggets is ideal for guidance, breaking news, and forum discussions on the go.

Adding a Fresh Perspective – Industry Stats Beyond Tech

According to a recent survey, more than 45% of companies that are not technically inclined embarked on data-driven projects over the past five years. Education, sports, real estate, and media sectors invest in data analysts capable of interpreting relevant information for better decision-making. This makes it possible for people with non-tech backgrounds to excel by combining industry knowledge with analytical skills developed recently.

Sources: LinkedIn , 365 Data Science

Path of Non Technical Person Towards Data Analytics Industry-Data Analyst And Data Scientist

FAQs: Frequently Asked Questions Before You Get Started

Do I have to code a lot for data science?

Not really. Most jobs need data visualisation, domain knowledge, or project management.

Is higher-level maths a hard requirement?

Basic algebra and statistics are normally enough to begin with. Some programs learn associated math concepts during the process.

Can students without technical backgrounds master data science in a short time?

Yes, if they work hard on the foundation courses and hands-on exercises. The learning curve is different for different individuals, but yes, it is possible with sustained effort.

Are there any entry requirements for data science in terms of previous work experience?

Most programs do not expect prior experience. Passion, fundamental knowledge, and hard work are valued more.

Which one should I learn first: Python or R?

Python is more common. But R is still used in academia and statistics. Either is fine for a beginner.

Who can enroll in data science courses if they are not from a STEM field?

Anyone who has a bachelor’s degree and is interested in learning may typically apply. Business and humanities majors typically prove to be good methods.

Is course accommodation an option?

Yes. Practically all institutions offer online, part-time, and full-time availability that suits different timetables.

What if I only want to specialise in data visualisation?

This is more than fine. Software tools like Tableau, Power BI, and particular data visualisation streams are available on the majority of courses.

Do best data science courses for non-tech students exist that include only applied learning?

There are certain courses that have more project-based emphasis than theory, ideal for students who learn well through practice.

Is beginner data science career placement guidance included?

There are certain institutes that have job placement facilities or career counseling available, especially hot programs that would like to possess a high rate of success.

Conclusion

Data science isn’t merely for programming hobbyists. The profession is expansive, dynamic, and more open than ever before to candidates without technical backgrounds.

If you have subject-matter expertise that you wish to use in conjunction with data-driven insights, or simply have a sense of enjoying pattern detection, then your non-technical background might prove beneficial.

More than ever before, today’s courses are designed to open doors to novices, progressively developing the technical and analytical proficiency required.

Key Takeaways

Inclusivity: Eligibility to a Data Science Course is generally kept open for diverse professional and academic backgrounds.
Multiple Pathways: There are plenty of data science non-coding jobs available—emphasise your ability, be it data storytelling, consulting, or visualisation.
Continuous Evolution: Space for innovative ideas exists within the industry. Continuous learning goes with the package of a successful data science career.

Remember, however, that the question of “Who can apply for data science courses?” has a straightforward answer: almost anyone who is willing to put in time and effort to learn. The future holds endless possibilities, and your background may just be the secret ingredient to be the standout in data science.

Chart Your Data Journey

If you are willing to go deeper, look at the Postgraduate Programme in Data Science & Analytics by Imarticus Learning.

It offers both foundational and advanced modules, so it is a strong contender for future students of any kind. Don’t let your non-technical background hold you back to discover the power of analytics and insights.

Data Science Course Fees vs. Earning Potential: Is It a Wise Investment?

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Introduction

Data Science course fees are a key issue for a great number of professionals and graduates seeking to enter one of the most buzzed-about professions today. In a world where data is oil, the expense of obtaining a formal qualification in data science can look prohibitive.

But just as important is understanding how the fees relate to the potential salary and increase in this field of endeavor. By putting the cost of data science courses against the potential for data science salary, you know whether or not this is a career that you desire.

In this post, we get into the nuances of profit-making investment in education in data science, returns, and real-life things that can help you make a decision whether this is an ideal career choice.

If you’re evaluating Data Science course fees versus the potential earnings, remember that while tuition costs can be substantial, the career trajectory in data science often leads to lucrative roles with high demand and competitive salaries. Many find that the ROI of data science education quickly surpasses initial expenses.

Understanding the Fundamentals of Data Science Education

In the last few years, data science has evolved from a mystical topic into a cornerstone of strategic decision-making throughout industries. From healthcare to finance and beyond, organisations are applying giant datasets to gain valuable insights.

So, formal education—either in college, in a bootcamp, or an online course—is now a necessity for aspiring data scientists to learn statistics, machine learning, and data visualisation.

But as data science becomes more popular, so does the range of course types and prices. On the one hand, there are low-cost self-study online courses. On the other, high-profile institutions can charge a premium for their course, with prices often in the thousands of pounds.

In order to understand if this trip is indeed worthwhile, it’s essential to explore how prices are set, what they really include, and the interplay between Data Science course prices and the need for a satisfying career.

Factors that Influence the Price of Data Science Courses

Data science course price is not just the amount paid for tuition. Each program is different and can be worth paying a little extra. Look at the following factors:

Reputation and Brand Value

Colleges/Institutions with rich history in offering quality education and securing the graduates in lucrative jobs are usually costlier. Their programs carry prestige, leading to better networking and greater recognition in the marketplace.

Depth of Curriculum

Some programs provide a broad, introductory treatment of data science, while others focus on particular topics like deep learning, natural language processing, or big data engineering. Specialized programs tend to be more expensive.

Mode of Delivery

Traditional, full-time campus programs usually are more costly due to the infrastructure and facilities provided. Distance or combined modes cost less, but certain online institutions have a high cost since they are providing additional services, live mentoring, and access to networking facilities.

Fees levied by institutions in major cities or capitals of international education can be higher since there’s a business cost of doing things higher here and the consideration of the better location.

It’s possible that most programs provide add-ons like career counseling, internship experience, interview sessions, and alumni connections. These add-ons drive the cost up but contribute to the worth of the education.

When you look at the cost of data science courses, watch out for the extras besides lectures and books. You want a course that provides hands-on applications and actual projects, which will prepare you for the job market competition.

Is a Data Science Course Worth It? Measuring Your Return on Investment

One of the most common inquiries from prospective learners is: “Is a course in data science worth it?”

The response lies in understanding Return on Investment (ROI). ROI is the formula that weighs against each other the worth of a course—e.g., potential salary increases, career development, and the immaterial benefit of learning cutting-edge technologies—and its cost.

Here are significant fields to remember:

Immediate Employability

A recognised qualification can improve your interview chances, as organisations tend to look for formal qualifications as proof of competency.

Salary Increase

Jobs in data science generally carry good salaries. Even a small salary hike post-course completion can pay back fees in a couple of years.

Career Prospects in the Long Run

Growing demand for data professionals means continuous career progression. Data scientists get promoted to senior roles such as data science managers, directors, or chief data officers.

Industry Versatility

Data science talent is versatile across many industries, which provides it with confidence and ample scope for expansion.

Networking and Mentoring

Access to alumni networks, faculty specialists, and industry experts can be a boon for potential career progression.

Deciphering the ROI of Data Science Education in Numbers

Let us consider some numbers from a series of global surveys into perspective. To put it simply, Forbes mentions that companies which completely adopted data-driven strategies had revenues increase by 20% over two years. McKinsey highlights that demand for data scientists continues to be greater than their supply and hence drives their market value upwards.

If you invest, say, ₹2,00,000 in a thorough data science course and your starting salary jumps from ₹4,00,000 to ₹7,00,000 per year, the salary hike could help you recover the course fee within the first year itself. This back-of-the-envelope estimate shows how the ROI of a data science education can be extremely appealing.

The Unique Element: More Than Just Technical Expertise

True, raw technical information is found on the web for free. Structured programmes, though, provide something distinct: opportunities for collaboration. Peer reviews, group projects, and exposure to up-to-date data sets offered by the programme provide insight into how data equates to strategic thinking. This experience differentiates high-calibre data scientists from solo study without similar hands-on experience.

Data Science Salary Potential: An Ever-Evolving Landscape

The earning capacity of a data science professional in India depends on various factors like your education level, the industry you work in, and your willingness to embrace new technologies. Entry-level data scientists typically earn between ₹6,00,000 and ₹12,00,000 per annum, according to Glassdoor India. With the right experience and specialised skills, this figure can rise significantly—often crossing ₹20,00,000 or more in senior roles.

Additional motivators such as yearly bonuses, stock options, and shareholder dividend plans enhance overall compensation even more. Data science’s great ability to earn is still an essential factor in the minds of most future professionals.

Fees vs. Earning Capacity in Data Science

One useful method for comparing the prices of Data Science courses is to compare educational expenditure, job roles, and entry-level salaries side-by-side. Look at the condensed table below, remembering that true costs vary by school and market conditions:

Programme Type	Approx. Fees (₹)	Typical Entry-Level Salary (₹)	Potential Senior Role Salary (₹)
Online Bootcamp	₹1,00,000 – ₹2,50,000	₹5,00,000 – ₹8,00,000	₹15,00,000 – ₹25,00,000
University Master’s	₹6,00,000 – ₹12,00,000	₹7,00,000 – ₹10,00,000	₹18,00,000 – ₹30,00,000
Executive Programme	₹5,00,000 – ₹10,00,000	₹8,00,000 – ₹12,00,000	₹25,00,000 – ₹40,00,000

Even at the higher rates of tuition, the pay in data science can often return the investment in a few years.

Highlight on Best Value Data Science Programs

While choosing a program, not only the course fee for Data Science must be taken into account, but also the course, reputation, and career guidance mechanism. For example, the Imarticus Learning Postgraduate Programme in Data Science and Analytics has a proper mix of theory, live projects, and strong career guidance. Further details about the course can be found on their website: Imarticus Learning’s Postgraduate Programme.

Such courses seek to offer the best value data science courses by finding a balance between costs and quality course provision so that you are well equipped for the data-driven career market.

All About Data Science and Analytics Course by Imarticus Leaning | Data Science For Beginners

How Data Science Career Investment Pays Off in the Long Run

While the short-term financial reward is tempting, the payback on a data science career investment tends to grow in the long term. Take a look at these points:

Continuous Skill Development

The ever-changing nature of data science requires constant upskilling, updating your skills from time to time.

Leadership Shift

Experienced data scientists can transition into leadership roles, influencing key business decisions and increasing their salaries as they ascend the corporate ladder.

Flexibility and Employability

Data science skills are in high demand and are highly transferable across many industries, forming a solid foundation for long-term career growth.

FAQs: Answers to Your Questions

What do typical Data Science course fees look like?

Fees can range from a few thousand pounds for bootcamps up to over £15,000 for university programs that cover everything.

Are data science course scholarships an option?

Yes. Many institutions offer scholarships or early-bird discounts. Always check the program’s website or contact admissions for the most recent options.

How quickly can I recover my investment after completing a course?

Recovery time will depend on how much your salary increases after completing the course. Many professionals find the investment worthwhile within one to three years.

Do I need a good maths background to pursue data science?

A familiarity with statistics, algebra, and calculus is an assistance, though much coursework includes foundation refresher instruction.

Is it worth taking a data science course if I’m career-changing?

Yes. Official programs can assist in making the transition easier and more attractive to the market, especially if you build on what you already know.

Can I specialise in one industry after completing a data science course?

Yes. The majority of programmes have elective modules or projects focused on specific industries like healthcare, finance, or marketing.

What kind of job positions will I be searching for upon graduation?

Typical positions are Data Scientist, Data Analyst, Business Intelligence Analyst, Machine Learning Engineer, and Data Engineer. Advanced positions tend to develop with experience.

Do hiring managers prefer accredited programmes?

Though accreditation is appreciated, employers also highly appreciate demonstrated skills and experience.

How important are internships and projects during the course?

Very important. Internships and project-based work enhance your skills and curriculum vitae greatly.

What are the key differentiators of best value data science programs?

The proportion of tuition fees to quality of curriculum, coupled with effective career guidance and mentorship, typically distinguishes the best value programmes.

Conclusion

Balancing Data Science course costs with potential returns is a determining factor for anyone willing to take up this fascinating career. The charges are significant, from online bootcamps to university master’s programs, but data science is one of the most fulfilling careers in today’s times. Data science earning potential and flexibility in positions guarantee the investment is typically paid back in the near and distant future.

Key Takeaways:

High ROI: Data science proficiency is in demand, and pay raises can easily fund tuition.
Quality of Curriculum Matters: Choose programs with real-world projects and strong mentorship to get the best outcomes.
Long-Term Growth: Data science careers offer long-term relevance, leadership opportunities, and ongoing skill enhancement.

Ready to embark on your data science journey? Learn about established programmes and career materials. Cross-validate course formats, do your own research, and engage in lifelong learning and networking. Investing in education can open doors to an entire universe of professional opportunities.

Do research on multiple programs and benchmark them against your career objectives.

Explore established programs such as Imarticus Learning’s Postgraduate Programme in Data Science and Analytics.

Stay updated with industry trends through websites like Data Flair.
Join data science communities or forums to network.
Continuously keep yourself updated to remain ahead in this fast-evolving career.

A wise evaluation of potential earnings, course fees, and the whole data science career investment schedule can take you to a successful and lucrative future. Use these results to decide if Data Science course fees are an apt investment for your professional life.

Data Science Skills and Tools Every Analyst Needs to Know

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

With the era of algorithms, data science skills and tools are the pillars of every contemporary business decision. From suggesting products on e-commerce websites to predicting financial trends, data science has emerged as the driving force behind smart decisions.

For anyone looking to join this fast-paced industry, it’s not enough to only know the buzzwords. You will need to establish a strong set of skills and learn a toolset that will enable you not only to land the job—but to rock it.

This blog post deconstructs the critical data science skills, must-learn data science technologies and the leading data analyst tools you need to become proficient with to succeed in the modern workforce.

Why Data Science Became Mission-Critical

Each click, swipe, and tap creates data. What’s done with it all?

Companies that harness data are achieving faster growth, improved customer retention, and a better product-market fit. McKinsey & Company states that businesses leveraging data are 23 times more likely to get customers and 19 times more likely to become profitable.

And this isn’t global speak alone—India is racing ahead too. According to NASSCOM, India will require more than 1.5 million data professionals by 2026.

Key Data Science Skills You Need to Master

A data analyst’s skillset is half logic, half creativity, and half business instinct. Here’s what you need to be industry-ready.

1. Statistical Thinking and Analytical Mindset

Statistics is at the core of data analysis.

Ideas such as distributions, sampling, p-values, and hypothesis testing enable analysts to meaningfully interpret patterns.

It is important to know correlation vs. causation, regression models, and inferential statistics.

✅ Pro Tip: Establish a solid foundation with Python libraries such as statsmodels and SciPy.

2. Programming Languages for Data Science

There’s no way around coding. It’s your data interface.

Python: The most general-purpose, utilised for data cleaning, analysis, machine learning, and deep learning.

R: Best for academic research, advanced statistical modelling, and visualisations.

SQL: Still the most requested language for querying databases.

And then there are: Julia (for high-performance computing), Scala (for Big Data), and Excel (for smaller datasets).

85% of data professionals use Python and 74% use SQL according to the Kaggle State of Data Science Report.

3. Data Cleaning and Wrangling

Raw data is not tidy. Mistakes, duplicates, missing values, typos—yep, that’s the real world.

Your data wrangling and pre-processing skills make you stand out. This includes:

Dealing with null values
Standardising formats
Removing outliers
Parsing strings and dates

Tools: Pandas, NumPy, OpenRefine

4. Data Visualisation Techniques

Raw data doesn’t talk—but pictures do.

Data storytelling is an essential soft skill for data analysts. You need to know:

Which chart type is best for which data type
How to prevent distortion or misrepresentation
How to effectively use colour, scale, and layout

Tools: Tableau, Power BI, Seaborn, Plotly, Matplotlib

Effective visualisation is what tends to win data buy-in from decision-makers.

5. Machine Learning and AI Tools

Those venturing into predictive and prescriptive models from descriptive analytics make machine learning a must.

Key concepts involve:

Supervised vs. unsupervised learning
Decision trees, random forests, logistic regression
Model accuracy, precision, recall, F1-score
Cross-validation, hyperparameter tuning

Well-known tools: Scikit-learn, TensorFlow, Keras, XGBoost

Bonus: Knowing how Large Language Models (LLMs) such as ChatGPT work is growing more valuable.

6. Data Ethics and Privacy Consciousness

With great power requires great responsibility.

As a data analyst, you are required to:

Be GDPR-aware, data masking, and anonymisation-conscious
Understand model bias and how it affects fairness
Ensure transparency and auditability in data pipelines

Learn more from OECD’s AI Principles and India’s Digital Personal Data Protection Act

7. Big Data and Cloud Tools

For datasets with high scale, local tools won’t do.

Learn how to use distributed computing tools such as Apache Spark and Hadoop
Understand cloud environments such as AWS, Google Cloud Platform, and Azure
Know how to deploy models into production environments using containers such as Docker

These are essentials for large businesses and tech-first companies.

Top Tools For Data Analyst Should Know

Category	Tools
Programming Languages	Python, R, SQL
Visualisation	Tableau, Power BI, Seaborn, Matplotlib
Machine Learning	Scikit-learn, Keras, TensorFlow, PyTorch
Data Manipulation	Pandas, NumPy, Excel
Cloud Computing	AWS, Azure, Google Cloud
Big Data Processing	Hadoop, Apache Spark
Project Management	Jupyter Notebook, Git, VS Code

Continuous practice on environments such as Kaggle and Google Colab will solidify tool proficiency.

Real-World Learning > Theoretical Learning

This is the area where most students fail—they gain familiarity with the tools but do not have practical context to apply.

Imarticus Learning’s Postgraduate Program in Data Science and Analytics provides:

25+ actual industry projects
100+ hours of hands-on coding
Tools such as Tableau, Python, SQL, Power BI, and Scikit-learn
Specialisations in AI, Data Visualisation, and Big Data
Career guidance with 2000+ hiring partners and job-guaranteed results

It’s a 360° makeover, not certification.

Data Scientist vs Data Analyst – Which Is Right For You? (2025)

FAQs

1. What are the essential data science skills?

Key skills are statistics, data cleaning, Python/R, machine learning, and data visualisation.

2. Which programming language will be most beneficial for data science?

Python, followed by R and SQL, are the most useful.

3. Is machine learning necessary to be a data analyst?

Not necessary, but beneficial for experienced positions and to progress into data science.

4. What software does data visualisation use?

Industry favourites include Tableau, Power BI, Seaborn, and Matplotlib.

5. Is a technology background necessary to work in data analytics?

No. Most successful analysts have a commerce, science, or humanities background.

6. What is the typical time to get job-ready?

With structured learning, 6 to 9 months is possible.

7. Do beginners need cloud computing?

Not at first, but cloud computing skills provide you with a significant advantage in enterprise roles.

8. How is data ethics important to analysts?

It helps in maintaining transparency, fairness, and privacy in analysis is an increasing industry expectation.

9. What’s the most underappreciated skill in data science?

Communication. Your work is worth only if you can tell people about it clearly and convey the message.

10. Is a certification sufficient to be hired?

Certifications are helpful, but project experience, mastery of tools, and interview preparation are more important.

Conclusion: Your Future Depends on Skill, Not Buzzwords

You can’t “wing” data science. Businesses are looking for analysts who can apply clarity to complication—and that takes thinking, not just tools.

The domain is changing and evolving rapidly. Software is becoming more intelligent, datasets are becoming larger, and hiring managers want job-ready professionals, not course completions.

Key Takeaways

Construct a core toolkit: Python, SQL, Tableau, and Scikit-learn are the foundation of any analyst’s arsenal
Get hands-on: Projects, case studies, and real datasets are more important than theory
Stay updated: AI integration, ethical data usage, and cloud deployment are quickly becoming table stakes

Your Next Move: A Data Analyst with Confidence

Don’t spend time figuring it out on your own. Learn from the experts and work on real-world challenges that reflect industry requirements.

Visit the Postgraduate Program in Data Science & Analytics by Imarticus Learning

Let your data journey begin—with structure, purpose, and confidence.

Is Data Science a Good Career Path for Freshers?

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Introduction: The Emergence of Data Science Careers

Data science is the refinery, and the new oil — in today’s digital-first environment, is data. It has the ability to reshape business decisions as it already influences and forecasts customer behavior or even optimises healthcare processes.

If you’re a fresher wondering about your career path, you might be probably asking: Is data science a good career for me?

The answer isn’t a simple yes or no — it depends on your interests, aptitude, and willingness to learn.

But one thing is clear — career opportunities in data science are not just growing; they are exploding.

Data science is one of the world’s top five most promising jobs, with year-over-year hiring growth at 37%, according to LinkedIn.

Here, you’ll learn in depth:

Why data science is perfect for freshers
What jobs you can look forward to
What skills will help you succeed
What courses are worth it
What the future holds

Let’s break down the data on data science.

What Is Data Science?

Data science bridges statistics, computer programming, and business acumen to extract useful insights from data.

Fundamentally, it’s about providing answers to practical questions using data:

What should our next product to launch should be?
Which marketing tactic generates the most ROI?
How can we foretell fraud beforehand?

Practical Uses of Data Science

Healthcare: Forecasting disease outbreaks
Finance: Identifying fraud and credit scoring
Retail: Recommendation engines and customer segmentation
Sports: Player analysis
Government: Policy effect and citizen services

If you’ve ever seen a “Recommended for You” product, that’s data science in action.

Why Data Science Is a Good Career for Freshers

Unlike other tech roles that need years of coding experience, data science welcomes freshers who can grasp concepts and learn quickly.

Here’s why it’s beginner-friendly:

✅ Structured Learning Paths

You can start from zero by learning from courses like the Postgraduate Program in Data Science and Analytics from Imarticus Learning, which offers a comprehensive course in Data Science and Analytics.

✅ Project-Based Learning

Most courses involve real-world projects — you graduate with evidence of work.

✅ High Demand, Low Entry Barrier

Your skillset and problem-solving skills matter more to employers than degrees.

✅ Salary Growth

Data science roles have some of the best entry-level pay rates in industries.

Career Opportunities in Data Science

No one pathway — the field provides varied career paths. Let’s discuss some jobs that you can apply for as a fresher.

Entry-Level Data Science Jobs

Data Analyst
Junior Data Scientist
Data Engineer (Trainee)
Business Intelligence Developer
Machine Learning Intern
AI Support Associate
Quantitative Research Analyst

These activities include:

Data cleaning
Making reports
Code writing in basic Python/SQL
Dashboards making
Assistance in developing models

These positions entail:

Role	Average Salary (INR/Year)
Junior Data Analyst	₹5.5 – ₹7 LPA
Data Science Intern	₹25,000 – ₹45,000/month
Business Analyst	₹6 – ₹9 LPA
ML Engineer (Fresher)	₹8 – ₹12 LPA

Source: Payscale, AmbitionBox

These figures vary depending on the location, course qualifications, and hands-on experience.

Skills Needed for Data Science

Let’s break down the skills you’ll need into 3 tiers.

Technical Skills

Programming: Python or R
Data Visualisation: Tableau, Power BI
Database Management: SQL
Machine Learning Basics
Data Cleaning and Preprocessing
Statistics & Probability

Business and Soft Skills

Analytical thinking
Communication
Curiosity and adaptability
Business acumen

Data Science Tools to Learn

Tool	Purpose	Level to Aim
Python	Scripting and ML	Intermediate
SQL	Querying data	Intermediate
Power BI	Visualisation	Beginner
Jupyter Notebook	Documentation and modelling	Beginner
Scikit-Learn	ML model building	Beginner

These tools are all taught in the Imarticus Learning Postgraduate Program in Data Science and Analytics.

A Unique Insight: The “Proof of Work” Era

Traditional degrees are being overtaken by “proof of skill.”

What does it mean?

Firms desire projects, not papers
A GitHub portfolio is stronger than a résumé
Hackathons and Kaggle competitions are the new recruitment grounds

In fact, as per a 2024 Coursera report, 55% of hiring managers indicated that they’d employ a fresher with project-based certification over a master’s degree candidate.

Career Development Path in Data Science

Data science provides one of the most rewarding and versatile career development paths.

Common Growth Path

Data Analyst (0–1 year)
Associate Data Scientist (1–3 years)
Senior Data Scientist (3–5 years)
Data Science Manager/Lead (5–8 years)
Chief Data Officer / Consultant (8+ years)

With each step, responsibility, project scope, and, of course — increased compensation.

The best thing? Progression is merit-based, not age-dependent.

Career Opportunities for Data Science Entry-Level

Let’s discuss numbers once more.

More than 70,000+ data analyst and scientist jobs posted on Indian job sites
The demand is international in the US, UK, Singapore, and the Middle East
BFSI, Healthcare, E-commerce, EdTech, and Logistics industries are hiring
India is anticipated to have over 11 million analytics jobs by 2026, as per India Brand Equity Foundation.

The Future of Data Science Careers

The future looks bright, with innovation occurring quickly.

What’s Coming:

AutoML: AI that builds models with little human intervention
Explainable AI: Models that explain why they made a choice
Data Privacy Engineering
Predictive Analytics in Finance
No-code Data Science Tools

Predictive analytics is a prime skill in finance and analytics roles — perfect for those who are into finance + data science.

Best Courses to Begin Your Journey

The Imarticus Postgraduate Program in Data Science and Analytics is created for the fresher to start his or her career in the industry.

Why It Is Special:

Teaches full-stack capabilities from Python to ML
Contains industry capstone projects
Provides job placement assistance
Trained by industry professionals
Suits any graduate from any stream

How Long to Get Job-Ready?

Average 4–6 months with:

10–12 hours of study each week
2–3 projects
1 ready GitHub portfolio
1 mock interview round

Need a roadmap? Here’s what your first 12 weeks could be like:

Week	Focus Area
1–2	Python Basics + Data Types
3–4	SQL + Data Cleaning
5–6	Statistics + EDA
7–8	Power BI/Tableau + Dashboards
9–10	Machine Learning Basics
11–12	Capstone Project + Interview Prep

FAQs: Is Data Science a Good Career for Freshers?

1. Is data science difficult to learn for non-tech students?

Not at all. Several students from commerce and humanities have made the switch successfully.

2. Which one is preferable: a certification or a master’s degree?

It depends on your intention. If you need to get employed quickly, a project-based certification is better.

3. Can data science be a remote job?

Yes. A lot of companies have remote positions in data science, particularly at the entry level.

4. Do I require a laptop with heavy specs?

Mid-range laptops (8GB RAM, SSD) are enough to start with.

5. What is the stability of this job?

Very stable. Provided you continue to enhance your skills, the industry always has room for you.

6. Will AI destroy data science jobs?

AI will transform how we work and not replace us. Human discretion in interpreting data is priceless.

7. Can I transition from BCom or BBA to data science?

Yes. Many top data scientists have a business background and employ predictive analytics in finance as a transition.

8. Which firms recruit freshers/ job prospects for data science beginners?

TCS, Accenture, Capgemini, Cognizant, Flipkart, and startups in India.

9. What’s the future salary scope?

With experience of 5+ years, professionals can have a salary of ₹25–35 LPA in best companies.

10. Do I have to learn advanced maths?

No. Maths and statistics of high school level will be enough to begin.

Conclusion

So, is data science good for freshers? Definitely.

With formal training, practice, and proper guidance, even individuals with no background in tech can enter this high-potential profession.

If you’re attracted by the paychecks, diversity of work, or pure demand — data science provides new entrants an advantage in a cutthroat job market.

Key Takeaways

Data science is an entry-level career with thousands of job openings and increasing demand.
Certifications and live projects are more important than your degree or experience.
Programs such as Imarticus’ PGP provide a systematized roadmap to becoming 4–6 months job-ready.

Ready to Launch Your Data Science Career?

Discover the Postgraduate Program in Data Science & Analytics by Imarticus Learning and join thousands of learners who’ve already made the transition.

Upskill today, build tomorrow’s career.