What Is Data Science? A Complete Beginner's Guide to Data Science in 2026

If you've spent hours searching the web for what is data science only to still be scratching your head, you're definitely not the only one. It's no wonder people get confused - the term seems to stretch to cover everything from your average spreadsheet report to building AI models from scratch. And chances are, that's the reason people trying to break into the field end up more confused than when they started.

This guide is going to give you data science explained in plain English: what it actually is, how it really works, and what a day in the life of a data scientist looks like. We'll also run down the differences between data science, data analytics, and machine learning - and give you some real salary numbers to boot. On top of that, we'll be looking at the skills that actually matter and offering some down-to-earth advice on how to learn data science if you're starting from nothing.

Think of it as a handy data science for beginners reference point you can come back to again and again.

What is data science?

Data science is a field of work that tries to find useful information and make pretty good predictions by combining what we know from statistics and programming with a decent understanding of what's going on in the real world. It's a pretty wide ranging job that takes you from collecting raw data in the first place, to cleaning it up, trying to see any patterns, building models, and getting the results down to a level where someone can actually do something with them.

Depending on who you speak with, this all sounds either really cool or a bit of a vague mess. And to be fair, they both have a point. In a real world sense, data science is where the overlap of numbers, tech, and industry knowledge all come together. For instance, a data scientist at a hospital and one at an online store - they might be using similar maths and computer coding, but for entirely different things.

You've got websites predicting products you're likely to buy, banks spotting dodgy transactions in zero time, hospitals working out which patients are probably going to have to go back in. All three are quietly running on behind the scenes, only stepping into view when they get things wrong.

How does data science work?

Data science isn't rocket science - it's a simple process that gets repeated over & over : grab the data, get it in order, take a look at what the numbers are saying, build a model based on that, and then go out there & tell the world what you've found. That's basically what data science is all about, boiled right down to its most basic flow : ask a question, let the data provide the answer and then, try to figure out if you can actually believe what the data is telling you.

A typical project goes through six stages - but the reality is, not a single project follows this tidy little road map without veering off course before you know it:

Data collection: That's all about pulling data from any old place - databases, APIs, sensors, or just good old web scraping.
Data cleaning: Let's face it, most projects spend 90% of their time trying to fix missing values, duplicate entries, formatting errors and all the other things that are guaranteed to go wrong.
Exploratory analysis: You know - basically just staring at the numbers trying to see if there's anything worth building a model from.
Modeling: This is where the fun starts : using all sorts of mathematical wizardry to pull out the hidden patterns and predictions from the data.
Evaluation: seeing how well your model holds up when confronted with new data (which so often it doesn't).
Communication: turning all those numbers into a story the rest of the team can actually make use of.

And if you're looking for the real truth of what that list looks like - it's the cleaning that just keeps on eating away at your time. Data cleaning ends up taking up a whole lot more hours than the rest of the stages combined, and the sad truth is - nobody ever gives you a heads up about just how much work that is going to be before you start in the field. Come on in, the water's fine - except for the realisation that all that time spent on data cleaning is the only thing that stands between you and a model that looks great in a demo but falls apart the first time it has to deal with real live data.

Why is data science important?

Data Science matters, not just because some organisations are now churning out an awful lot more data than any team could possibly keep up with manually, but also because those organisations that manage to wrangle that data into some sensible decisions tend to leave the rest in their dust. And that's behind all sorts of cool stuff like personalised product recommendations, spotting dodgy transactions, and helping doctors make better decisions.

It's estimated that the world was slinging around a rough 132 zettabytes of data in just one year , according to some figures kicking around from Statista. The WEF Future of Jobs Report 2025 had some pretty interesting things to say about the future of work - including how roles related to AI and Big Data are going to be among the fastest-growing careers on the planet.

What does a data scientist do?

A data scientist spends their day juggling a whole mix of things : gathering and cleaning data, building models, testing out hypotheses, and then trying to boil all that down in a way that people who don't know a thing about tech can understand. What that looks like day to day will depend a lot on the company they work for.

A typical week in the life of a data scientist blends technical work with all sorts of non-technical stuff in a way that can be mighty disorienting if you're new to all this: writing some SQL to get data out of a database, then using Python to build a model, and a few hours later sitting in a meeting with a bunch of people from the marketing department trying to explain why they bombed last quarter.

The "data scientist" label has also started to splinter into all these different job titles over the last couple of years : analytics engineers who are in charge of keeping the data pipelines running smoothly, applied scientists who are just focused on building models, and ML engineers who actually get to put the finished model into use in the real world

Data science vs data analytics: what's the difference?

Aspect

Data Analytics

Data Science

Main question

What happened, and why?

What will happen, and how do we act on it?

Data type

Mostly structured (spreadsheets, databases)

Structured and unstructured (text, images, logs)

Core tools

SQL, Excel, Power BI, Tableau

Python/R, SQL, ML frameworks, cloud platforms

Typical output

Reports, dashboards

Predictive models, algorithms, data products

Common job title

Data Analyst

Data Scientist

Data science vs machine learning: what's the difference?

Machine Learning is a subset of Data Science , not some kind of separate entity trying to one-up it. Data Science encompasses the whole shebang - from sifting through, cleaning up and interpreting data right through to where it ends up. Machine Learning on the other hand, is all about the specific algorithms used to pick out patterns from data & actually improve their predictions over time, all without needing to be reprogrammed from scratch every single time a new scenario comes up.

Every single Machine Learning project you'll come across is essentially a Data Science project too. But, not every Data Science project necessarily needs Machine Learning to get the job done. A simple dashboard to sum up your sales figures from last quarter ? That's straight up Data Science right there. Now, use one of those fancy models to try and predict which customers are most likely to ditch you next quarter ? That's where Machine Learning starts to come into play, and trust us, there's plenty of good old fashioned analysis that will happily live without it.

What skills do you need to learn data science?

Core skills for a data analyst typically include either Python or R, SQL to wrangle data from a database, some statistics and probability to make sense of it all, data visualisation to get the story across, and just enough machine learning to build and give the once over on a basic model. And, that doesn't count for much if you can't explain your findings to some poor soul who doesn't have a clue how the darn thing works, but just wants to know if the advice is good or not.

You start with the basics: programming and querying. Python (or R does the heavy lifting on the analysis side) , and SQL gets the raw data out of the database in the first place. A good structured start here, such as eklabya’s Python Programming Mastery course, is usually the best way to get past that beginner plateau, because many self-taught learners waste weeks just scratching their head wondering what to practice next.

Finally, there's one more thing that has crept up on us as a near necessity: you need to be comfortable using AI tools to speed up your analysis and auto-generate some code for you. The people who just treat this as a way to avoid learning the fundamentals are going to plateau fast. On the other hand, those who are using it as a way to supplement solid fundamentals are moving a heck of a lot faster than everyone else in the room.

Is data science a good career in 2026?

Yes, there is a cautionary note to take on board before you dive in headfirst. The US Bureau of Labor Statistics is forecasting a whopping 36% boost in Data Science employment numbers between 2023 and 2033 - that's one of the fastest-growing occupations out there. A Data Science career in India can currently bring in a respectable national average of around ₹11 to ₹12 Lakhs Per Annum - entry level salaries tend to start off around ₹4 to ₹8 Lakhs a year.

Experience level

Approximate CTC in India (2026)

Fresher / entry-level

₹4–8 LPA (up to ₹15–25 LPA at top product companies)

Mid-level (3–6 years)

₹10–20 LPA

Senior (8+ years)

₹25–40 LPA and above

Figures based on 2026 estimates from Glassdoor India and AmbitionBox; actual pay varies by city, company type, and specialization.

The six figure data scientist with the fancy title on LinkedIn ? Yeah that's real, but so is the fresh out of college kid who has spent the last year applying for everything and hearing nothing back. Both stories aren't mutually exclusive, and the difference between them usually comes down to whether you've actually built some real projects or just watched a bunch of tutorials. A structured program like eklabya's data science certification was actually created to help bridge that gap : by guiding you through actual projects rather than leaving you to stumble about in the dark.

How to start learning data science

Start by learning Python and SQL first and then branch out into statistics. Put in the time to practice on real datasets, and ideally knock out 2 or 3 portfolio projects that you can explain to an interviewer without needing notes. While formal courses can be helpful, don't make the mistake of thinking hours spent on actual data is less valuable than the course you take, it's actually the other way around.

Weeks 1 to 6 - Focus on getting a solid grip on Python basics and SQL. Save the more advanced math for when you can write your first scripts without constantly looking everything up.

Weeks 6 to 10 : Dig into core statistics and probability, plus how to make data visualisations using tools like Power BI, it's not a bad idea to get familiar with something like that pretty early on.

Weeks 10 to 16 : Get a solid grounding in the basics of machine learning - this should cover regression, classification, model eval and other basics with the scikit-learn package.

ONgoing : then do some end-to-end projects on public datasets, places like Kaggle or government open-data portals are a good place to start. Rather than just listing the project as an achievement you can walk someone through the thinking behind your project and how you came to your conclusions.

Before You Apply: get some real feedback from someone in the business, whether that's a mentor, an online community or a course with an instructor who knows what's what.

One thing that catches out a lot of beginners is how easy it is to get slowed down deciding what to study next. That's why a lot of programmes offer a guided path - one that replaces your guesswork with a set curriculum, hands-on projects and actually some help with getting a foot in the door when you're ready to start applying. Programs like eklabya’s Data Science, Analytics & Power BI Program and its Machine Learning & AI Certification do this by giving you a set plan, projects to do and then some help with getting interviews once you're ready.

Frequently asked questions

Is data science hard to learn?

It takes real effort, but it's learnable in stages. Most beginners struggle less with math than with sticking to consistent daily practice.

Do I need a coding background to start?

No. Plenty of working data scientists started with zero programming experience and picked up Python specifically for this work within a few months.

How long does it take to become job-ready?

Six to 12 months for most focused learners, depending on prior math or programming exposure and how many real projects you actually finish.

What is the difference between a data scientist and a data analyst?

A data analyst mainly reports on what already happened. A data scientist builds predictive models and usually works with messier, larger, or unstructured data.

Will AI replace data scientists?

It's automating the repetitive parts (basic reporting, boilerplate code) while increasing demand for people who can frame the right question and check whether an AI-generated answer is actually correct.

What is the average data scientist salary in India in 2026?

The Average salary of a data scientist is ₹11 to 12 LPA in India. Entry-level professionals usually earn between ₹4 and 8 LPA, and senior roles cross ₹25 to 40 LPA depending on the company and city.

Conclusion

Burying the buzzwords, at its core data science boils down to taking a load of unsorted, messy data and turning it into something worthwhile to make a decision on, using a combination of stats , programming skills & a healthy dose of judgment to figure out which questions are even worth asking in the first place. So it's no surprise data science pops up in career chats 2026, whether you're a student trying to figure out what to study or a pro eyeing a career change.

The field actually rewards the folks who get real projects done rather than people just collecting certificates. Start by getting started with Python and SQL, then sit on one dataset long enough to actually get your head around it, and let your curiosity take you towards whatever specific area of data science gets you most fired up. Don't try to learn everything at once, it just won't stick.

If you'd rather not muddle through all this on your own then elakabya's data science and analytics programs are built with hands-on projects, working with good mentors and support to help get you from a complete beginner to job-ready.