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Category: Data Science and Analytics

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

Posted on 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 fees

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 TypeApprox. 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

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. Do hiring managers prefer accredited programmes?

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

  1. How important are internships and projects during the course?

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

  1. 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 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

CategoryTools
Programming LanguagesPython, R, SQL
VisualisationTableau, Power BI, Seaborn, Matplotlib
Machine LearningScikit-learn, Keras, TensorFlow, PyTorch
Data ManipulationPandas, NumPy, Excel
Cloud ComputingAWS, Azure, Google Cloud
Big Data ProcessingHadoop, Apache Spark
Project ManagementJupyter Notebook, Git, VS Code

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

Data science skills and tools

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 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
Data Science

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

  1. Data Analyst
  2. Junior Data Scientist
  3. Data Engineer (Trainee)
  4. Business Intelligence Developer
  5. Machine Learning Intern
  6. AI Support Associate
  7. 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:

RoleAverage 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

ToolPurposeLevel to Aim
PythonScripting and MLIntermediate
SQLQuerying dataIntermediate
Power BIVisualisationBeginner
Jupyter NotebookDocumentation and modellingBeginner
Scikit-LearnML model buildingBeginner

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

  1. Data Analyst (0–1 year)
  2. Associate Data Scientist (1–3 years)
  3. Senior Data Scientist (3–5 years)
  4. Data Science Manager/Lead (5–8 years)
  5. 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:

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

Too Many Data Scientists? Market Trends in 2025

Posted on by Imarticus Learning

Over the last decade, Data Scientists have been celebrated as the rockstars of the professional world. In fact, Harvard Business Review famously labelled it the “sexiest job of the 21st century.” But today, many are starting to ask: Are too many people becoming Data Scientists? Let’s dive deep into this pressing question by analysing the data science job market trends, the demand for data scientists, the future of data science careers, & the growing competition in data science jobs.

Understanding the Popularity Boom in Data Science

The explosive growth of data across industries has dramatically increased the demand for… skilled professionals who can interpret, analyse, & extract insights from this information. As a result, there has been a major surge in enrolments for data science courses worldwide, like the Postgraduate Program in Data Science & Analytics by Imarticus Learning.

Universities, bootcamps, & online programs have responded by flooding the market with newly trained professionals. But does this rapid influx mean that data science career opportunities are drying up? Let’s explore.

Data Science Job Market Trends: What the Numbers Say

To answer this, we need to look at data science job market trends. According to a recent LinkedIn report…data science roles have grown by over 650% since 2012. Companies across sectors—finance, healthcare, retail, & tech—continue to depend heavily on data-driven strategies.

However, there is also a growing nuance: while entry-level jobs are plenty, senior-level & niche specialisations are where the real competition lies. This shift is making many wonder: Is data science oversaturated?

Another important trend is the rising expectation for Data Scientists to possess hybrid skills—like domain expertise, strong communication skills, & a business mindset—in addition to technical prowess. This evolution is shaping the data science employment outlook.

If you want to dive deeper into how roles in data science & data analytics differ, check out this detailed blog on Data Science vs Data Analytics.

Is Data Science Oversaturated?

Now, let’s tackle the elephant in the room: Is data science oversaturated? The short answer is—it depends.

In some regions & for certain roles, there’s definitely a crowd. Entry-level positions, in particular…see stiff competition, especially among candidates who lack real-world experience or advanced technical skills.

But when you examine high-demand roles like machine learning engineers, deep learning specialists,… or data architects, the talent shortage still persists. Therefore-while the number of job seekers is increasing, the demand for data scientists with the right skills remains strong.

Moreover, the way industries are evolving ensures that the future of data science careers is far from bleak. From artificial intelligence to predictive analytics, new applications are emerging, widening the scope for trained professionals.

The Future of Data Science Careers

Speaking of the future, the future of data science careers looks incredibly dynamic. Emerging technologies like generative AI, blockchain, & IoT are creating new fields where Data Scientists will play a critical role.

Experts predict that the number of global data science & analytics jobs will hit 11 million by 2026. Fields like healthcare (personalised medicine), automotive (autonomous vehicles), & finance (fraud detection) are just a few areas experiencing rapid data transformation.

If you’re passionate about understanding how data is transforming industries, this blog on how data science is transforming modern businesses provides fascinating insights.

Thus, while competition might be rising, so are the data science career opportunities.

Competition in Data Science Jobs: Realities on the Ground

It’s important to realistically acknowledge the level of competition in data science jobs. With-more people entering the field, hiring managers are becoming increasingly selective.

Candidates today must demonstrate proficiency beyond theory: they must showcase real-world project experience, advanced problem-solving skills, & industry-specific knowledge. Certifications, specialisations, & continual upskilling are no longer optional—they are essential to standing out.

For example, building a portfolio showcasing projects across different industries (like e-commerce forecasting models or healthcare predictive analytics) is now a standard expectation for landing premium roles.

Furthermore, mastering critical data analytics skills is a major differentiator. If you’re unsure which skills are in demand, here’s a handy guide on data analytics skills for every professional.

Data Science Employment Outlook: Challenges & Opportunities

When looking at the data science employment outlook, the picture is a mixed bag—full of both challenges & opportunities.

On the one hand, automation is beginning to handle basic data tasks like cleaning & preliminary analysis. This change means that basic-level roles may shrink. On the other hand, high-value tasks such as model optimisation, strategic decision-making, & ethical AI development are becoming critical—and these need human intelligence.

Thus, Data Scientists who invest in lifelong learning & domain specialisation are poised to thrive. Programs like the Postgraduate Program in Data Science & Analytics by Imarticus Learning are designed specifically to help aspirants navigate this evolving landscape.

Demand for Data Scientists: Still Strong, But Evolving

Despite increased competition, the demand for data scientists is not slowing down anytime-soon. What’s changing is the type of demand.

Companies no longer want just data engineers or analysts;… they are looking for full-stack professionals who can manage end-to-end data projects—from wrangling datasets to communicating actionable insights to stakeholders.

Additionally, specialised knowledge in areas like natural language processing (NLP), computer vision, cloud computing, & MLOps is highly sought after.

This reality shows that while the answer to “Is data science oversaturated?” might be “yes” for generalist roles, there is still a significant unmet need for specialists.

If you want a quick & comprehensive video on this evolution, watch this insightful video on the future of data science.

Data Science Career Opportunities: Where to Look

If you’re planning a career in this field, you should know where the real data science career opportunities lie. As of 2025, some of the top industries hiring include:

  • Healthcare: Bioinformatics, medical imaging, & patient data analysis
  • Finance: Risk modelling, fraud detection, & customer insights
  • Retail & E-commerce: Demand forecasting, inventory management, & personalisation engines
  • Automotive: Autonomous driving systems & predictive maintenance
  • Energy: Smart grid management & sustainability analytics.
Data Scientists

In addition, government organisations & NGOs are increasingly seeking experts to tackle public policy challenges, climate change, & social welfare initiatives.

Thus, smart positioning & strategic upskilling are key to capitalising on upcoming data science job market trends.

FAQs

1. What are the current data science job market trends?

The data science job market trends show-strong growth in roles requiring AI, machine learning, & big data expertise… Specialised skills continue to be in high demand across industries.

2. Is data science oversaturated in 2025?

Though more people are entering the field, data science is not yet oversaturated… There is still demand for professionals… with specialised skills in AI, predictive analytics, & machine learning.

3. What is the future of data science careers?

The future of data science careers is bright,…with emerging technologies like blockchain, AI, & IoT creating new opportunities. Businesses will-continue to rely on data scientists for strategic decision-making.

4. How competitive is the demand for data scientists?

While the demand for data scientists remains strong, competition is rising… for real example Focusing on niche skills such as deep-learning or data engineering can help you stand out.

5. What is the data science employment outlook for 2025?

The data science employment outlook is positive for 2025. Industries like:-healthcare, finance, & e-commerce will keep hiring data scientists for… roles requiring both technical & business expertise.

6. How can I improve my chances in the competitive data science job market?

To succeed in the competitive data science job market, focus on gaining real-world experience, earning certifications, &… specialising in high-demand areas like machine learning or data visualisation…

7. What are the best data science career opportunities for 2025?

Top data science career opportunities in 2025 will be in AI, predictive analytics, &… automation-across sectors like healthcare, finance, & e-commerce.  Roles that integrate AI & big data will be in high-demand.

8. Is there still demand for data scientists despite the growing number of professionals?

Yes, the demand for data scientists remains strong… By specialising & staying current with industry trends, professionals can continue…to find great opportunities in the field.

Conclusion: Is Becoming a Data Scientist Still Worth It?

In conclusion, are too many people becoming Data Scientists? Maybe! But that’s not the real question you should be asking.

The more important question is: are you willing to continuously learn, adapt, & specialise in an ever-evolving landscape?

The future of data science careers… belongs to those who embrace complexity, commit to mastering both hard & soft skills, & focus on delivering measurable business impact. The data science employment outlook is still full of promise, but it favours the agile & the ambitious.

If you’re serious about carving out a successful path in this exciting field, investing in the right education—like the Postgraduate Program in Data Science & Analytics by Imarticus Learning—can make all the difference.

After all, the demand for data scientists who bring genuine value will always stay strong, no matter how crowded the field becomes.

Top 10 Advanced Excel Functions Every Analyst Should Know

Posted on by Imarticus Learning

If you have met any financial analyst, you might have heard them either swear by Excel or tell you how much time they spend on that software. Excel has evolved itself to be the go-to tool for analysts as it’s one of the best ones out there to handle large data sets, perform calculations (however complex they might be) and to even help with data visualisation. But many of us Excel novices may not think beyond the simple SUM or AVERAGE functions, Excel, however, is much more powerful than that. It can offer a whole range of advanced functions so that you can get deep insights and have lower errors.

You might be someone who just knows the basic Excel functions but are also aware of how powerful Excel is and the advance functions of Excel. 

If you want to get a deep dive into Excel skills, a financial analysis course could put you on the right track. But for now, let’s take a look at the top 10 advance functions of Excel.

Watch:  Data Scientist vs Data Analyst – Which Is Right For You? (2025) I Imarticus Learning

1. VLOOKUP and HLOOKUP

Let’s say you want to find a corresponding value in another column and you are dealing with a large dataset, what do you do? You can try to go for the VLOOKUP function. The function helps you look at a value of a vertical column and return a corresponding value from another column. Similarly, HLOOKUP works horizontally, looking through rows instead of columns.

These advanced excel functions will really help out when you are working on datasets. Here’s how VLOOKUP can be implemented:

VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])

2. INDEX and MATCH

VLOOKUP is useful, but it has limitations. For example, you will be only able to search the first column. In such a situation, INDEX and MATCH, can help, which, in combination offer more flexibility.

  • INDEX provides the cell value from a specific row and column.
  • MATCH locates the value’s position in a row or column.

Here’s how to use them together:

INDEX(array, MATCH(lookup_value, lookup_array, 0))

3. SUMIFS and COUNTIFS

Let’s say you don’t want a total sum, but a sum or count value based on certain conditions, In such a situation, SUMIFS and COUNTIFS help a lot. SUMIF and COUNTIF functions can aid you by setting up multiple conditions to refine your analysis.

Here’s the SUMIFS formula:

SUMIFS(sum_range, criteria_range1, criteria1, [criteria_range2, criteria2], …)

4. IF with AND / OR

The IF function is quite a popular one is excel, but if you combine it with AND or OR, it becomes more powerful and can now handle multiple conditions.

Here’s a formula using IF clubbed with AND:

=IF(AND(C2>=C4,C2<=C5),C6,C7)

This checks if the value in C2 is between the values in C4 and C5. If true, it returns C6; otherwise, it returns C7.

5. OFFSET with AVERAGE or SUM

The OFFSET function is another basic function, but when in combination, this becomes one of the advance functions in Excel. When you combine this function with advanced SUM function in Excel or the AVERAGE function, it lets you return a range of cells that’s a specified number of rows and columns.

Here’s the formula:

=SUM(B4:OFFSET(B4,0,E2-1))

By adjusting the value in E2, the number of cells included in the sum changes automatically.

6. CHOOSE

The CHOOSE function is a masterful decision-making tool  in financial models. The function lets you choose a value from several options. For example, let’s say you have  different revenue growth assumptions for the next year, then when you use the function CHOOSE you return the value of the selected assumption.

Here’s the formula:

=CHOOSE(choice, option1, option2, option3)


7. XNPV and XIRR

Since we started with how analysts still like Excel, we should know one of the crucial functions that they use, which is XNPV and XIRR. These functions help working with cash flow data and are largely used in fields like investment banking, FP&A, or equity research. When you are using these functions, you can apply specific dates to each individual cash flow, and as a result address any issue where cash flows don’t always occur at equal time intervals.

With XNPV and XIRR, you are also capable of calculating the present value or internal rate of return for irregular cash flows. This wouldn’t be possible if you are also sticking to using Excel’s basic functions.

Here’s the formula for XNPV:

=XNPV(discount_rate, cash_flows, dates)

8. SUMIF and COUNTIF

While you have read about SUMIFs, there is also another tool for a more specific role – which is the SUMIF function. If you want to add cells using a specific condition, then this function would be your go-to one. On the other hand, let’s say, you don’t want the sum but rather are looking to count the number of cells but based on a specific condition. In such a situation, COUNTIF should be the function to go for.. These functions are helpful for quickly summing or counting cells based on a single criterion.

Here’s the formula for COUNTIF:

=COUNTIF(D5:D12,”>=21″)

9. PMT

The PMT function is a great way of making periodic-based calculations, that is let’s say, calculating periodic payment for a loan, based on a fixed interest rate and loan term. 

Here’s the formula:

=PMT(rate, nper, pv, [fv], [type])

10. INDIRECT

The INDIRECT function is a great tool that can be used if you want to create dynamic references. With this function you can  reference a cell or range based on a text string. What that does is, it makes your formulas more flexible and adaptable to changes in the spreadsheet.

Here’s the formula:

=INDIRECT(ref_text, [a1])


Watch: Excel Tutorial | Beginner to Advanced – Part 1 I Imarticus Learning

Conclusion

Mastering the advance functions of Excel is actually essential for any analyst. Since you will be dealing with large datasets, you need to have flexible models. As an analyst, one of your goals would be to automate repetitive tasks so that you can focus more on strategic elements. If you want to get the most out of Excel and then you should try to get a professional certificate from Imarticus Learning and apply them in financial modelling and analysis.

FAQs

1. What are the most essential advanced Excel functions for analysts?
 If you want to go for financial analysis, there are some functions like VLOOKUP, INDEX/MATCH, SUMIFS, and PMT which are extremely important.

2. How does SUMIFS differ from SUMIF?
As a analyst, you might often use SUMIFS. This function is used so that you can also use multiple criteria while using it. On the other hand, SUMIF is limited to one condition.

3. What makes INDEX/MATCH more versatile than VLOOKUP?
Since specific data in large data sets can be difficult to track, INDEX/MATCH is one of the functions that will help you search any column. On the contrary, VLOOKUP aids you with the first column.

4. How do I handle errors in Excel?
 If you want to replace error messages, you can do so with the IFERROR function to replace error messages with a custom value.

5. What is the OFFSET function used for?
 OFFSET is an important tool for you to use as an analyst as it will help you  to create dynamic ranges. By creating these dynamic ranges, you make your formulas more flexible.

Understanding Python Objects: A Comprehensive Guide

Posted on by Imarticus Learning

If you’ve ever written even a single line of Python code, you’ve already worked with a Python object: whether you realized it or not.

Objects lie at the heart of Python programming, and give it the ability to be not only elegant in nature, but flexible in design. Right from data to functions to classes – Python considers them all as objects. 

But what does that exactly mean? And how does that affect how you work with the programming language? 

In this guide, let’s help you figure out exactly that. 

This will also serve as an important foundation before you embark on any data science course. 

What is a Python Object?

A very straightforward and yet powerful feature that is at the heart of the Python design is that everything is an object in Python. However, when you ask what is object in Python, then by very simple means it can be interpreted as follows:

A Python object is a container that bundles two essential components:

  • State: This refers to the data the object holds, often called attributes.
  • Behavior: These are the actions the object can perform, known as methods.

So, creating a variable in Python automatically creates an instance of a class. And that is precisely what a Python object is! 

Take this simple line of code:

x = 5

On the surface, it looks like x is just holding a number. But under the hood, x is a fully-formed Python object of the built-in int class. That means it comes with several built-in methods and properties you can use. For example:

print(x.bit_length())  # Output: 3

In this case., bit_length is a method of int class. The approach is to determine the number of bits necessary to effectively present the number in its form of binary representation. You can observe the fact how even the lowest-level entities of the world of programming, i.e. the variables, turn out to be the objects in Python under the hood.

This leads us to an important conceptual answer to the question, “Is Python object oriented?”

Let’s take a closer look at that question – is Python object oriented? 

Watch:#49 Python Tutorial for Beginners | Class and Object

Is Python Object-Oriented?

The simple answer is – yes, Python is object-oriented. 

The complex answer is – it’s object-oriented in its own unique, flexible way. Unlike some strictly object-oriented languages like Java or C++, Python supports multiple paradigms. These include procedural, functional, and object-oriented programming (OOP).

Because of this, it’s safe to say that Python is object-oriented, but not only that! 

What makes Python especially powerful is that OOP is built into its core. Here’s how:

  • You define and work with you own classes and objects.
  • You can reuse existing classes and use their code using inheritance. 
  • Your Python objects are capable of being private and hiding their internal state from the global environment through encapsulation. 
  • Your Python objects can have varied behaviors as per your context and the class they’re being used with, even if they share the same method name..

Here’s a quick OOP example:

class Car:

    def __init__(self, brand):

        self.brand = brand

    def drive(self):

        print(f”{self.brand} is driving!”)

my_car = Car(“Toyota”)

my_car.drive()  # Output: Toyota is driving!

In this case, my_car is a Python object of class Car. You’ve just built a mini data model using object-oriented principles!

Why Are Python Objects Important?

Python objects are important because they quite literally form the backbone of the language and everything you do based on it. 

  • All things are objects: Integers, lists, functions, modules, classes themselves are all objects.
  • Efficiency in memory: All objects are accessed through references, rather than through copies, which speeds up and makes memory efficient.
  • Customizability: through classes, you can specify the exact types of objects you wish to deal with, making it possible to write reusable, modular programs.

So, no matter your field of work. As long as you’re operating on Python, you are working with Python objects to manage complexity with ease! 


Watch: OOPs in Python | Understanding OOPs Concepts

Object Lifecycle in Python

Every object in Python has a lifecycle:

  1. Creation: when created out of a class.
  2. Usage: the calling of methods or accessing data.
  3. Destruction: the object is no longer being referred to and is cleaned-up by the garbage collector of Python.

Here’s a quick summary of the types of objects available in Python. 

TypeDescriptionExamplesUse Cases
Built-in ObjectsPredefined types provided by Python. Ready to use with built-in functionality.int, float, bool, str, list, dict, setArithmetic, text manipulation, data storage, iteration
User-defined ObjectsCustom objects created using class. Reflect complex or real-world structures.class Account, class ReportGeneratorModeling financial systems, data structures, AI models

Understanding this cycle helps you manage memory and debugging more effectively, especially when building larger applications.

Final Thoughts

The real power of Python appears when you begin doing more than writing scripts; you have to think in objects. Knowing the nature, behaviour, usage, as well as development of Python objects will not only better your skills as a coder, it will open the door to the world of highly proficient fields of work such as data analytics, automation, and financial analysis.

If you’re looking to expand your understanding and knowledge of Python, and see work on its real-world applications, check out the Postgraduate Financial Analysis Program. This state-of-the-art data science course, developed by Imarticus, provides you with the appropriate theoretical and practical knowledge to get going in the world of data science! 

Frequently Asked Questions (FAQs)

1. What is a Python object and how is it different from a variable?

In Python, an object is an instance of a class which has attributes (data) and functions (methods). A variable is simply just a pointer of an object.

2. Is Python object-oriented or something else?

Python is object-oriented at its core, but differs from traditional object-oriented programming languages. Python is more flexible than them, in that it also supports alternative paradigms like procedural and functional. 

3. Are functions and classes also Python objects?

True, everything is an object in Python, even functions and even classes themselves. It is possible to pass them as arguments, or even in other functions by defining them.

4. How do I know which Python object I’m working with?

You can call the inbuilt function type() along with the object name to get the type of the object. So, type (41) will return class int, and so on. 

5. Why is understanding Python objects important in data science?

Data in Python is nearly always organized as an object – such as DataFrame in Pandas or Array in NumPy. An understanding of how Python works behind its objects enables you to create better, faster, and readable code.

Getting Started with Python: Installation and Setup

Posted on by Imarticus Learning

With Python everywhere, from Netflix recommendation systems to fraud detection systems in finance, it’s a tool that is fueling our tech-heavy world. It stands out in science, automation, and artificial intelligence; among other fields.

One particular reason for Python being liked across the spectrum is its easy to learn, uncomplicated syntax. Regardless of whether you want to analyze market trends, optimize business processes, or just explore coding a bit, Python is the best place to start.

On that note, before you get started writing your first Python program, there is the one most important preparation – getting Python installed and configured correctly on your system.

Not sure how to install Python? 

Let’s help you out! 

In this guide, we’ll walk you through exactly how to install Python on Windows, macOS, and Ubuntu, without any prerequisites. 

And if you’re planning to apply your skills in finance, analytics, or tech, you’re in luck because several state-of-the-art data science courses are designed to teach Python with hands-on, industry-relevant projects.

Let’s get your Python environment up and running!

How to Install Python on Windows

Here’s how you can install Python on Windows quickly and without any hassle:

  • Head to python.org/downloads
  • Download the latest Python 3.x release.
  • Run the installer, and don’t forget to check the box that says:

      “Add Python to PATH”
  • Choose “Install Now” and let it finish.

Alternatively, you could also use the MIcrosoft Store to install Python. This is a simpler method for personal devices, and ensures automatic updates for your Python installation. Just search for “Python” in the Store, install, and confirm with the following code on powershell. If python installation has been successful, you’ll see the version number:

python –version

Once you’ve installed Python on Windows, the next thing is to get an IDE, which is basically the environment on which you run your Python scripts. For starters, you can begin with visual code, using the following steps: 

  1. Install Visual Studio Code (VS Code):
  2. Add Python Support to VS Code:
    • Open VS Code
    • Go to Extensions (Ctrl+Shift+X)
    • Search for Python
    • Install the official extension by Microsoft
  3. Pick Your Python Interpreter:
    • Press Ctrl+Shift+P to open the Command Palette
    • Type “Python: Select Interpreter”
    • Choose the one labeled with the version you just installed

Finally, you have the Python installation and IDE set up completed in Windows. To verify this, run a basic program.  Open the terminal in VS Code (Ctrl + backtick or View > Terminal) and type:

python

You should see the Python prompt (>>>). Now try typing:

print(“Hello, Python!”)

You’ll see the string printed back on the console! You did it. You’re officially writing Python on Windows!

Now, let’s see how we can install Python in macOS.

Watch: Installing Python | Python Programming | Tutorials on Python – Imarticus. 

How to Install Python in Mac (macOS)

Installing Python on macOS is just as simple as how to install Python in Windows, but with a few mac-specific steps:

1. Start by check if Python is already installed. macOS often comes with Python 2.x pre-installed. For that, open terminal and type:

python3 –version

If you see a 3.x version, you may already have it.

2. Install Homebrew (If Needed)
If you want better control over versions, first install Homebrew.

3. Install Python via Homebrew
Once Homebrew is set up, type:

brew install python

4. Verify It Works

python3 –version

If you get the Python version printed, congratulations—you now have Python running on your Mac.

Now, let’s understand how to install Python in Ubuntu. 

How to Install Python Ubuntu (Linux)

Your Ubuntu OS comes preinstalled with Python. However, you need to ensure you have the altes installation in place. For that:. 

1. Update the List of Available Packages

Press Ctrl + Alt + T to get your Terminal on. Then, run the following command to refresh your package list. 

sudo apt update

This command fetches updates for your package manager without installing them yet.

2. Upgrade Existing Packages

Run the following command to upgrade your existing software:

sudo apt upgrade

This will bring all installed packages on your system up to date, reducing compatibility issues during the Python installation.

3. Install Python in Ubuntu

Now, you’re ready to install Python. Change the version number as per your requirement, in the example we’ve used version 3.10: 

sudo apt install python3.10

This way, you’ll quickly and seamlessly download and install the specified Python version on your. To check whether it has been done successfully, type:

python3.10 –version

You should now see a message displaying the installed version of Python.

With that, you have successfully learnt how to install Python on Ubuntu, along with installing Python on Mac and Windows. 

Watch: PYTHON for Beginners: Learn Python Programming from Scratch (Step-by-Step) | Module 01

Conclusion 

The installation process for Python is complete so now you can start learning its functions. But where should you begin?

Python serves as the perfect partner for people who want to analyze financial trends or forecast markets or begin a career in data science. It is essential to select a program which delivers Python education through practical real-world financial analysis applications in the industry.

This data science course teaches Python fundamentals through Python fundamentals and their application in finance-focused learning environments. Developed by Imarticus Learning, the course is for people who want to eventually build a solid career in data science and analytics, starting from the very foundations. 

Frequently Asked Questions (FAQs)

1. What’s the quickest way to install Python in Windows?

Go to python.org and get the latest Windows installer. Don’t forget to check the “Add Python to PATH” radio button while installing. 

2. I have a Mac. How do I install Python without breaking the system version?

macOS usually comes with an older version of Python (usually Python 2.x) already installed. It’s wise to not clobber the system version, so install Python on Mac using Homebrew. Once you have Homebrew installed, you can simply run brew install python in Terminal. 

3. I am using Ubuntu. Is Python installed, or do I need to install it myself?

A majority of Ubuntu systems have Python installed in them. Nevertheless, in order to make sure that you have got the latest version installed, open the Terminal and type in sudo apt update and then sudo apt install python3.

5. Is Python reinstallation necessary if you change from Windows to Mac or Ubuntu?

Yes, Python installations are platform-specific. So, if you change OS, you will need to figure out how to re-install Python again. 

Flask vs FastAPI: Which is Better for Deploying ML Models?

Posted on by Imarticus Learning

Getting machine learning models out there for people to use is a big deal. Data scientists pour their hearts into building amazing models. But what’s the point if no one can actually access them? This means putting them into action. 

Users then get to interact with them. Picking the right web framework for this is super important. Two really popular ones are Flask and FastAPI. We’re going to take a good look at both right here.

If you are eager to truly master data science, you should certainly consider enrolling in a program in data science and artificial intelligence. But, if you’re looking for a basic article to help you figure out which one between FastAPI vs Flask fits your needs best, this is the one. 

Why Put ML Models into Action?

Models exist to give predictions. That’s how they become genuinely useful. Businesses rely on these predictions every day. This is how they create real value. Deployment makes models available to everyone. 

They live on a server, ready to go. Users send their requests. The model then quickly gives back answers. That’s the whole point, isn’t it?

Watch: Data Scientist vs Data Analyst – Which Is Right For You? (2025) I Imarticus Learning

A Bit About Flask

Flask is what we call a micro-framework. It’s incredibly light. Developers really like its straightforward nature. You can get something up and running with Flask super fast. It gives you just the basics. 

You then add whatever else you need. This gives you a lot of freedom. Lots of projects use Flask. It’s a very reliable choice.

A Bit About FastAPI

FastAPI is pretty new on the scene. It’s made for speed, pure and simple. It uses some of Python’s most modern features. Handling multiple tasks at once, called asynchronous programming, is a core part of it. FastAPI even writes its own documentation automatically. This saves so much time. It’s getting more and more popular. Many folks are using it for building APIs.

FastAPI vs Flask: What’s the Real Difference?

There are some big differences between them. Flask works in a synchronous way. FastAPI, though, is asynchronous. This really impacts how fast things run. Flask needs more setup if you want to build APIs. FastAPI, on the other hand, comes with API features already built-in. It uses something called Pydantic for checking data. Flask doesn’t do this by default.

Performance Really Counts

When you’re putting ML models into action, how well they perform is everything. Models can be quite heavy. They might take some time to process things. The framework you choose shouldn’t make things slower. Requests need to get quick responses. Users expect things to be fast. This is truly vital for any application.

FastAPI vs Flask Performance: A Closer Look

Generally, FastAPI offers better performance. Its asynchronous design really helps and can handle many requests all at once. It doesn’t just sit and wait for one task to finish, but moves on to the next. This makes it super efficient. Flask, however, processes requests one by one. It might struggle a bit when things get busy.

Imagine a prediction scenario where a user sends in some data. The model gets to work on it. Then it gives back a result. If many users send data at the same time, a fast framework is a lifesaver. This is exactly where the FastAPI vs Flask performance comparison really shows itself. FastAPI clearly shines in these situations.

FeatureFlask (Synchronous)FastAPI (Asynchronous)
Request HandlingOne by oneConcurrent
SpeedGood for smaller loadsExcellent for high loads
Built-in FeaturesMinimalRich (API docs, validation)
Learning CurveLowerModerate


Building with Flask and FastAPI

Flask asks for less code to get started. You set up your routes, and write functions for them. It’s really simple to grasp. For smaller models, Flask works just fine. If you don’t expect a lot of requests, it’s good and beginners find it easy to learn. 

FastAPI uses something called type hints. This makes your code very clear. Pydantic checks all your data. It makes API development quite simple. Asynchronous functions really boost speed. 

For more complex APIs, FastAPI is better. It even creates its own documentation using Swagger UI. 

Watch: Data Science Careers: Job Roles, Scope, and Salaries in India | Imarticus Learning

FastAPI vs Flask: API Documentation 

FastAPI automatically makes API documentation for you. This is a massive plus point. Developers can easily see all the endpoints. They can even test them out right there. Flask needs you to add other libraries for this. You have to put them in yourself which is extra work. For big teams, automatic documentation is a must-have. You can learn more about API documentation on.

FastAPI vs Flask: Data Validation

FastAPI relies on Pydantic. It makes sure your data is correct. Any incoming data gets checked. This stops errors from happening. It makes your API very reliable. Flask doesn’t have this built-in feature. 

You have to add validation yourself. That means writing more code. This is definitely an advantage for FastAPI.

FastAPI vs Flask: Concurrency and Doing Things Asynchronously

This is a fundamental difference. Flask uses something called WSGI. It handles one request at a time per worker. FastAPI uses ASGI. 

It can handle many tasks at the same time. For tasks that involve waiting, like reading from a disk, this is crucial. ML model inference can often involve waiting. Loading model weights, for example, takes time.

Think about it like this: Flask waits for each step to finish. FastAPI, however, can do other things. While one model is loading, another can be busy predicting. This way of doing things in parallel is incredibly powerful. It significantly increases how much work your application can get done.

Conclusion 

Your choice really comes down to your project. For small, straightforward APIs, Flask is a fantastic starting point. It’s super easy to use. But for ML model deployments that need to be fast and handle lots of users, FastAPI is the clear winner. Its asynchronous nature and built-in tools are truly outstanding. It delivers speed. It provides reliability.

Imarticus Learning offers some really great courses. One of them is the program in data science and artificial intelligence. It helps you build exactly these kinds of skills. You can learn how to deploy models in a very effective way.

So, to sum it up, for modern ML model deployment, FastAPI is often the better pick. Its speed and features are truly hard to beat. However, Flask definitely still has its place for certain kinds of projects.

FAQs

Is FastAPI harder to learn than Flask?

    FastAPI is a bit tougher to get started with due to asynchronous programming and type hints, but it makes complex projects much clearer.

    Can I use Flask and FastAPI in the same project?

      You technically could, but it’s not really common. People usually stick to one main framework for their API.

      Does FastAPI mean Flask is no longer needed for anything?

        Not at all. Flask is still excellent for simpler web applications, quick prototypes, and projects where top-tier speed isn’t the main goal.

        How does FastAPI vs Flask performance look in tests?

          Tests consistently show FastAPI performing better than Flask in terms of how many requests it can handle per second and lower delays, especially when lots of people use it at once.

          Which framework is better for building smaller, independent services?

            Both can work well, but FastAPI’s speed and structured API design make it a very strong choice for building efficient microservices.

            Are there any specific code libraries that work better with one framework?

              Most machine learning libraries don’t care which framework you use. But FastAPI’s Pydantic integration is fantastic for making sure your input and output data for ML APIs is correct.

              How can I make sure my application is always available with either Flask or FastAPI?

                You can use tools like load balancers, run several copies of your application, and use containers (like Docker and Kubernetes) to keep both Flask and FastAPI applications running smoothly.

                K-Means Clustering Explained: A Beginner’s Guide with Python

                Posted on by Imarticus Learning

                Have you ever looked at a massive spreadsheet and thought, “How do I even begin to group these customers, users, or patterns?” You’re not alone.

                For data analysts and beginners stepping into machine learning, understanding how to organise unlabelled data is frustrating. You don’t want theory-heavy explanations. You want a hands-on approach that’s simple, practical and shows real results.

                That’s exactly where k means clustering fits in. Whether you’re building recommendation systems, segmenting customers, or detecting anomalies, k means clustering algorithm simplifies complex data by breaking it into logical groups.

                What is K Means Clustering and Why Does It Matter

                K means clustering, which is a vector quantisation method first used in signal processing. It partitions n observations into k clusters, where observation is basically assigned to the cluster with the nearest mean (also called the cluster center or centroid), which acts as the cluster’s representative.

                You tell the algorithm how many clusters (or “groups”) you want. It then:

                1. Picks some initial points (called centroids),
                2. Assign nearby data points to those centroids,
                3. Repositions the centroids based on the average of the assigned points,
                4. Repeat until nothing changes.

                It’s clean, fast, and widely used, especially in marketing, finance, and recommendation systems. If you’ve ever used YouTube or Amazon, you’ve already seen this in action.

                The k means clustering algorithm works best when the data naturally falls into separate groups. It’s used across sectors, from banking to telecom, where decisions need data-based segmentation.

                k means clustering

                Choosing the Right Number of Clusters

                A common question: how many clusters do I need?

                The answer? Use the Elbow Method.

                The algorithm calculates “inertia” and how spread out the points are in each cluster. The more clusters you add, the lower the inertia. But at some point, adding more clusters gives very little improvement. That “elbow” point is your sweet spot.

                k means clustering

                This is why many analysts plot inertia versus k. The curve tells you when to stop. In a Programme in Data Science and Artificial Intelligence, you’ll often use this graph before running any model.

                 K-Means in Action: A Simple Python Example

                In cluster analysis, the elbow method helps decide how many clusters to use in a dataset. You plot the explained variation against the number of clusters, then look for the ‘elbow’ point where the curve starts to flatten. That point usually shows the best number of clusters.

                k means clustering

                Let’s walk through a basic k means clustering example using Python:

                from sklearn.cluster import KMeans

                import pandas as pd

                # Sample dataset

                data = {‘Income’: [15, 40, 90, 55, 75], ‘SpendingScore’: [39, 81, 6, 77, 40]}

                df = pd.DataFrame(data)

                # Running the algorithm

                model = KMeans(n_clusters=3)

                model.fit(df)

                # Add cluster labels

                df[‘Cluster’] = model.labels_

                print(df)

                This code assigns each customer into a group based on how much they earn and spend. Easy to follow. That’s the power of k means clustering with Python, it lets you build results fast.

                When Should You NOT Use K-Means?

                While it’s a great tool, k means clustering algorithm has limits:

                • Doesn’t work well with non-spherical clusters.
                • It can break with too many outliers.
                • Needs you to guess the value of k (though elbow method helps).
                • Doesn’t perform well if features have different scales.

                So, always scale your data (using standardisation or normalisation) before applying it. And test with different k values.

                Real-Life Use Cases: K-Means at Work

                • Retail: Group customers into value segments for personalised promotions.
                • Healthcare: Group patients based on symptoms or treatment responses.
                • Finance: Spot unusual transactions that might indicate fraud.
                • Telecom: Segment users based on usage patterns and churn risk.

                Practical Example: Customer Segmentation

                Refer to the table attached. It shows a common use case in customer segmentation using a k means clustering example.

                With just two features, income and spending score, you can group users into three practical clusters: high-value, low spender, and mid-range. Each decision becomes data-driven.

                Customer IDAnnual income (₹000s)Spending ScoreAssigned Cluster
                11539Low Income
                24081High Value
                3906Low Spender
                45577High Value
                57540Medium

                Tips to Use K-Means Efficiently

                • Always standardise your data.
                • Use the elbow method to decide k.
                • Run multiple times to avoid poor initialisation.
                • Don’t rely on it for non-linear problems; go for DBSCAN or hierarchical clustering instead.

                These simple tweaks make a big difference in results.

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                FAQs

                1.  How does the k mean clustering algorithm work?

                The k means clustering algorithm works by first choosing k random points called centroids. Each data point is then assigned to the nearest centroid. After that, the centroids move to the centre of their assigned points.

                2.  Can you give an example of k, which means clustering in Python?

                Yes. A simple k means clustering example in Python, which would be using customer data like income and spending habits.

                3.  Is k means clustering used in real-world businesses?

                Yes. Many businesses use k, which means clustering, to improve customer targeting, detect fraud, manage inventories, or optimise services. For example, banks use it to group clients by risk level, while e-commerce platforms use it to show personalised product suggestions.

                4.  What is the ideal k value in k means clustering?

                There is no fixed k value. The best way to choose k is by using the elbow method. This involves testing different k values and seeing which one gives the best balance between accuracy and simplicity. The ‘elbow point’ in the chart usually shows the right number of clusters.

                5.  How does k mean used in a programme in data science and artificial intelligence?

                In a Programme In Data Science and Artificial Intelligence, k means clustering is a core technique in unsupervised learning. Learners practice real-life projects such as customer segmentation, anomaly detection, and pattern recognition. It’s one of the must-know algorithms in most data science curricula, including the one from Imarticus Learning.

                6.  Why is k means clustering important in data science courses?

                Because it helps you work with raw data without labels, real-world data is often unorganised. K means clustering helps make sense of it by grouping similar entries. That’s why it’s a foundation skill in any Programme In Data Science and Artificial Intelligence, especially when working with business or user data.

                The Final Words

                K means clustering, which may sound like just another algorithm. But once you use it on your dataset, you’ll realise how powerful it is. It simplifies chaos. It helps you take the first step toward advanced analytics.

                Start small. Try out the Python example. Tune it. Visualise it. Then scale up.

                If you’re serious about building a future in data science, this is one tool you can’t ignore.

                Python Data Types: An Introduction

                Posted on by Imarticus Learning

                Are you confused about why your Python code keeps throwing type errors? Or why do some variables behave unpredictably while others don’t change at all? The real issue lies in not fully understanding Python data types.

                Whether you’re just starting out or diving into a data science course, skipping the basics can cost hours of debugging. This post explains what are data types in python, explains mutability, and helps you use each type the right way so your programs run smoothly and efficiently.

                What are Data Types in Python?

                Python is a programming language that you can easily use for many different tasks.

                If you mess this part up, your program might crash, behave unexpectedly, or just slow you down.

                Why you need to know this:

                ·         You can prevent errors by choosing the right type.

                ·         It helps improve your code efficiency.

                ·         Data types make debugging faster.

                ·         Crucial for interviews, especially if you’re applying after a data science course.

                ·         You must understand them to master control flow, loops, or even basic functions.

                Understanding Python’s Built-In Data Types

                Python became popular because it is simple to use and easy to read. That’s why both beginners and experienced people like using it.

                Even in 2025, this is one big reason people choose Python. In data science, people come from many backgrounds. Some are good with numbers, and others know coding well. Python makes it easier for anyone to start learning and building things without much trouble.

                Python offers several built-in data types grouped into categories:

                1. Numeric Types

                int: For integers (e.g., 1, 500)

                float: For decimals (e.g., 10.5, 3.14)

                complex: For complex numbers (e.g., 4 + 3j)

                All of these are immutable data types in python, meaning their value cannot be changed after assignment.

                2. Text Type

                str: For text or string values (e.g., ‘hello,’ “world”)

                Again, strings are immutable. Even if you change a character, python creates a new string object behind the scenes.

                3. Boolean Type

                Bool: Can be either True or False. Often used in condition checks and logical operations.

                Mutable vs Immutable Data Types in Python

                Let’s clarify something: many beginners struggle with mutability.

                In simple terms:

                Ø Immutable means you can’t change the value once it’s set.

                Ø Mutable data types in python mean you can change the contents without creating a new object.

                Here’s a breakdown:

                Python Data Types: Mutable vs Immutable

                Data TypeCan You Change It? (Mutability)What It’s Usually Used For
                intNo (Immutable)Storing whole numbers like age, count, or price
                floatNo (Immutable)Dealing with decimal values, such as weight or salary
                strNo (Immutable)Handling text, names, messages, or any string data
                tupleNo (Immutable)Storing a fixed set of items like coordinates or settings
                listYes (Mutable)Holding a collection of items that may change, like a to-do list
                setYes (Mutable)Keeping unique items without duplicates, like tags or categories
                dictYes (Mutable)Pairing keys with values, like name and phone number in a contact list

                This comparison will help you avoid mistakes like modifying immutable types or incorrectly assuming you can change them, like lists or dictionaries.

                The Different Collection Data Types

                1. List – Mutable and Ordered

                A list is like a shopping list.

                You can add, remove, or change items.

                my_list = [1, 2, 3]

                my_list.append(4)  # This changes the original list

                Used when your data will change. Lists are mutable data types in python.

                2. Tuple – Immutable and Ordered

                Tuples are similar to lists but fixed.

                my_tuple = (1, 2, 3)

                Useful for data that shouldn’t be changed, like coordinates.

                3. Set – Mutable and Unordered

                Sets store unique values. Great for removing duplicates.

                my_set = {1, 2, 2, 3}

                print(my_set)  # Output: {1, 2, 3}

                4. Dictionary – Mutable and Key-Value Based

                Dictionaries are like real-life dictionaries. They pair a word (key) with a definition (value).

                my_dict = {“name”: “Rahul”, “age”: 25}

                my_dict[“age”] = 26

                Very useful in data science course work for structuring data.

                How Python Treats Mutability

                We visualised the number of common mutable vs. immutable types. This helps you mentally group what’s safe to modify and what’s not. Check the chart for clarity.

                python data types

                Type Conversion in Python

                You’ll often need to convert one data type into another:

                ·         str() to convert to string

                ·         int() to convert to integer

                ·         float() for decimal numbers

                ·         list() or tuple() for collection types

                Why is this important? 

                Because mismatched types lead to TypeErrors. You must know which conversions are safe.

                Real-world Example: Why Data Types Matter in Data Science

                In your data science course, imagine you’re working with a CSV file. If the “age” column comes in as strings (‘25’, ‘30’), you can’t calculate averages. You’ll have to convert it to integers or floats.

                Not knowing what data types are in python can lead to major headaches in data preprocessing. That’s why companies expect you to master this first.

                Small Errors That Cost Big in Python

                •  Assigning a mutable object as a default argument in a function.
                • Trying to change an element in a tuple.
                • Using a string method on a list.

                These things seem small but often cause major runtime errors. So be cautious, especially when handling immutable data types in python.

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                You’ll learn Python, SQL, Tableau, and Power BI and how to apply them in actual business scenarios. What sets this course apart? It’s not just theory. You’ll work on real projects, take part in hackathons, and get support with interviews from over 500 hiring partners.

                Even if you have no programming background, the course starts from the basics and guides you step by step.

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                FAQs

                1. What is the role of data types in a data science course?
                 Understanding Python data types is foundational in any data science course, especially when working with datasets, transformations, and type-safe operations.

                2. How do Python data types affect memory usage?
                 Mutable types can use more memory if altered frequently. Knowing when to use immutable types helps optimise performance.

                3. Which data types in python are immutable?

                Immutable data types in python include int, float, str, and tuple. You can not change their values after the assignment.

                4. Which data types in python are mutable?

                The list, dictionary, and set are mutable. You can easily modify their content once you create them.

                5. Can you convert one Python data type into another?

                Yes, using type casting like int(), str(), or list(), you can convert between compatible data types in python.

                6. Is a string mutable or immutable in python?

                A string is immutable. You cannot change a specific character in a string.

                7. Why do I get TypeError in Python?

                TypeErrors usually happen when you try to perform an operation not supported by the data type used, like adding a string to an integer.