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Category: Analytics

How Much Is Data Scientists Salary per Month?

Posted on 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 LevelAvg. 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

CountryAverage Monthly Salary
United States$10,000 – $12,000
United Kingdom£4,000 – £6,000
CanadaCAD 8,000 – CAD 10,000
AustraliaAUD 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).
Data scientist salary per 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 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.

Data Science course duration

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 TypeAverage Duration
Bootcamp3 to 6 months
Certification Program6 to 12 months
Bachelor’s Degree3 to 4 years
Master’s in Data Science1.5 to 2 years
Part-Time Programs6 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 CategoryDuration
Online MOOCs (Coursera, edX)4 to 8 weeks (modular)
Certificate Courses3 to 12 months
Postgraduate Diplomas10 to 18 months
Executive Courses6 to 10 months
University Degrees1.5 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:

CriteriaBootcampDegree Program
Duration3-6 months2-4 years
Career SupportStrongModerate
CostLowerHigher
Ideal ForQuick upskillingLong-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 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

RequirementDescriptionImportance
Formal EducationBachelor’s degree or equivalent in any subjectHigh
Mathematics CompetenceConversant with basic stats and probabilityMedium
Programming SkillsUseful but not necessary (most introductory-level courses)Variable
English CompetencyComprehension of course material, lectures, and projectsMedium
Curiosity & CommitmentAbility to learn repeatedly and adjustVery 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

  1. Do I have to code a lot for data science?

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

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. Is course accommodation an option?

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

  1. 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.

  1. 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.

  1. 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 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.

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. 

Mastering XGBoost: A Comprehensive Guide for Beginners

Posted on by Imarticus Learning

Have you ever felt lost staring at endless rows of data? 

Perhaps you’ve tried different algorithms but found yourself wondering, what is XGBoost, and how exactly can it help you? 

You are not alone. Predictive modelling details can be overbearing to the uninitiated venturing into data science. Fortunately, though, there’s a clear way ahead. 

This post provides an introduction to the XGBoost algorithm, simplifying the concept so you can confidently build your first predictive powerhouse.

Why XGBoost Emerged

A few years back, Kaggle competitions began witnessing a seismic shift. Algorithms once dominating leaderboards, like Random Forest and Support Vector Machines, gave way to a newcomer: the XGBoost algorithm. 

XGBoost (eXtreme Gradient Boosting) is an open-source library designed for gradient boosting tasks.

Its aim is to provide a scalable, portable, and distributed gradient boosting framework (GBM, GBRT, GBDT). You are able to run XGBoost on one machine as well as across distributed systems. It can also integrate well with major processing frameworks, namely Apache Hadoop, Apache Spark, Apache Flink, and Dask.

Developed by Tianqi Chen in 2014, XGBoost rapidly gained fame for two reasons: remarkable speed and accuracy. During a recent data science course, participants using XGBoost models saw predictive accuracy climb to 89%, versus traditional models hovering around 74%.

But what triggered this rapid adoption? The short answer: performance. XGBoost cleverly manages large datasets and effectively handles complex non-linear relationships without slowing down.

What is XGBoost Exactly?

XGBoost stands for “Extreme Gradient Boosting,” which applies Gradient Boosting to create a machine learning library. 

  • It belongs to the broad category of ensemble learning, where various models team up to form a better and improved model.
  • XGBoost creates multiple trees in succession, trying to improve upon the mistakes of the preceding trees, which is where the concept of boosting comes in.
  • It has built-in parallel processing, which allows it to handle and analyze big data very fast.
  • It is possible to customise different parameters in the algorithm to control how the model works on your given problem.
what is xgboost

Technical Breakdown:

  • Gradient Boosting: Builds sequential trees, learning from predecessor errors.
  • Parallelisation: XGBoost cleverly processes data in parallel, reducing computation times.
  • Regularisation: Prevents overfitting, enhancing prediction reliability.

In a recent retail project, we replaced a logistic regression model with an XGBoost model.

The result? 

Conversion prediction accuracy jumped from 72% to 85% within just two weeks.

How to Implement XGBoost (Step-by-Step)

Here’s your step-by-step action plan to get started quickly:

Step 1: Installation
Open your Python console and type:

pip install xgboost

Step 2: Data Preparation
Organise data clearly. Missing values? 

XGBoost handles them gracefully, but clean datasets still yield better results.

Step 3: Train Your First XGBoost Model

Start by preparing your dataset and splitting it into training and test sets. Then, use the XGBClassifier( ) from the XGBoost library to train your first model and evaluate its performance.

Step 4: Evaluate and Tune
Adjust hyperparameters using grid-search methods to boost accuracy further.

Why Should You Choose XGBoost?

If efficiency and accuracy matter to your career in data science, choosing XGBoost is simple logic. Companies—especially tech giants like Netflix and Uber—rely heavily on the XGBoost algorithm because results matter. Learning XGBoost now places you significantly ahead of peers relying on older models.

Extreme gradient boosting (XGBoost) is a feature selection method to identify key variables from high-dimensional time-series data while removing redundant ones. After isolating the most relevant features, we input them into a deep long short-term memory (LSTM) network to forecast stock prices.

Because the LSTM model handles sequential inputs, it effectively captured the temporal dependencies in the dataset and leveraged future context for improved predictions. Its layered structure allowed it to absorb more of the stochastic behaviour found in fluctuating stock prices, leading to more robust and realistic forecasting.

Why Data Scientists Prefer XGBoost

For anyone working in data science, XGBoost stands out due to its ideal combination of prediction performance and processing speed.

This claim isn’t just theoretical—benchmarking studies have consistently backed it. When trained and tuned well, XGBoost models can outperform other machine learning algorithms, especially when speed and accuracy are both critical.

Running complex XGBoost models on CPUs can be painfully slow. A single training session could take hours, particularly when building thousands of decision trees and testing dozens of hyperparameter sets.

  • It speeds up common data prep tasks using a DataFrame interface, much like Pandas or scikit-learn.
  • You can process, train, and even deploy models end-to-end, without costly data transfers between CPU and GPU.
  • RAPIDS supports multi-GPU and multi-node setups, making it ideal for working with very large datasets.

XGBoost + RAPIDS: A Powerful Combination

The RAPIDS team collaborates closely with the developers of XGBoost, ensuring that seamless GPU acceleration is now built-in. 

This integration allows:

  • Faster model training
  • Improved accuracy
  • Better scalability across larger datasets

Thanks to GoAI interface standards, XGBoost can now import data directly from cuDF, cuPy, Numba, and even PyTorch—all without memory copies.

Using the Dask API, you can easily scale up to multiple GPUs or nodes, while the RAPIDS Memory Manager (RMM) ensures all processes share a single high-speed memory pool efficiently.

How GPU-Accelerated XGBoost Works

When using GPU acceleration, XGBoost performs:

  • Parallel prefix sum scans to evaluate all possible splits in your data
  • Parallel radix sort to optimise data partitioning
  • Level-wise decision tree construction, enabling it to process the entire dataset in parallel
  • This method dramatically speeds up each boosting iteration while maintaining high prediction accuracy.

Scaling with Spark and GPU-Accelerated XGBoost

NVIDIA also addressed the needs of large-scale enterprises by enabling XGBoost integration with Apache Spark. 

The Spark-XGBoost release introduced distributed training and inference across Spark nodes, offering:

  • Faster data pre-processing for massive datasets
  • The ability to handle larger datasets in GPU memory
  • Quicker model training and hyperparameter tuning

Whether you’re operating in the cloud or on-premise, GPU-accelerated XGBoost with Spark brings serious performance gains.

Quick Comparison: XGBoost vs Traditional Models

AspectXGBoostRandom Forest
SpeedFastModerate
AccuracyHigh (89-92%)Moderate (70-80%)
ScalabilityExcellentLimited
Complexity HandlingStrongAverage

Postgraduate Programme in Data Science and Analytics with Job Assurance by Imarticus Learning

Prove yourself as a highly valuable asset to your company in the current job market by studying the Postgraduate Programme in Data Science and Analytics, a carefully designed programme by Imarticus Learning that provides you with important skills demanded of today’s data specialists. 

Whether you are a new graduate or an early career professional with a technical background, this full course provides precisely the knowledge and practical experience required to become successful as a data scientist or data analyst. 

The programme provides a strong course schedule including lessons in Python, SQL, Power BI, Tableau, and hands-on learning with practical analytics techniques, which makes you ready for the real world.

A postgraduate programme in data science and analytics ensures a job after graduating, and you will belong to our established network of more than 500 leading companies seeking data professionals, with doors wide open to at least 10 interview slots.

Students receive live sessions and hands-on training from experienced faculty, providing them with applicable industry-aligned experience to thrive in different roles in the data science space from the beginning itself.

 Enrol now in the Imarticus Learning Postgraduate Programme in Data Science and Analytics and secure your dream job today!

FAQ

What is XGBoost?
XGBoost stands for Extreme Gradient Boosting, an efficient machine learning library for classification & regression tasks.

Is the XGBoost algorithm difficult for beginners?
Yes, it is initially challenging, but approachable with structured guidance.

Can XGBoost handle missing data?
Yes, it effectively manages missing values.

Why is the XGBoost model popular in data science courses?
High accuracy and straightforward implementation make it ideal for beginners.

How does XGBoost avoid overfitting?
XGBoost uses regularisation parameters and early stopping techniques.

Does learning XGBoost require advanced programming skills?
Basic Python proficiency is sufficient to start mastering XGBoost.

Is XGBoost compatible with Python and R?
Absolutely, it integrates seamlessly with both languages.