Data science is the discipline of turning raw data into decisions and products. It combines statistics, programming and domain knowledge to collect, clean, analyse and model data, then communicates what the numbers mean so that a business, a government or a machine can act on them.
What data science actually is
Every organisation now records far more than it understands: transactions, clicks, sensor readings, support tickets, images, documents. Data science is the set of methods that converts those records into three things:
- Insight — "customers who contact support twice in a month are 4× more likely to cancel."
- Prediction — "this loan application has a 12% probability of default."
- Automation — a recommendation engine, a fraud filter, a demand forecast that runs itself.
The role sits at the intersection of three skill families. Statistics tells you whether a pattern is real. Programming (Python, SQL) lets you work at scale. Domain knowledge tells you which questions are worth asking. Most people arrive strong in one and build the other two.
Data science is also an umbrella. Machine learning and generative AI are specialisms inside it; business analytics and data engineering are its neighbours.
How a data science project works
- Frame the question"Why is churn rising?" becomes a measurable target and a definition of success.
- Collect and cleanSQL, APIs, logs. Typically 50–70% of the effort: missing values, duplicates, inconsistent labels.
- Explore and visualiseDistributions, correlations, segments; the stage where most real insights appear.
- ModelRegression, classification, clustering or forecasting; validated on data the model has never seen.
- Deploy and monitorAn API, a dashboard or a scheduled job; watched for drift as the world changes.
- CommunicateA decision memo, a dashboard, a product change. Insight that nobody acts on is not finished work.
Data science vs analytics vs ML vs AI
Data science jobs and salaries in India
India is one of the three largest data-science job markets in the world, driven by IT services, banking, e-commerce, pharma and a dense start-up ecosystem. Entry points are broad: analysts, business analysts and data engineers all share the same core skills and routinely move into data scientist and ML engineer roles.
Aegis alumni from the PGP in Data Science work at Wells Fargo, Morgan Stanley, Amazon, Reliance, TCS, HDFC Bank, Deloitte and 500+ other companies; placement packages across cohorts have ranged from ₹8.5 LPA to ₹40 LPA, with salary hikes of up to 500% for career-switchers.
Skills and tools you need
Programming
Python (pandas, NumPy, scikit-learn), SQL, Git. R is optional in 2026.
Statistics & maths
Probability, hypothesis testing, regression, basic linear algebra. Enough to know when a result is noise.
Visualisation & BI
Power BI, Tableau, matplotlib/plotly. The skill that gets analysts promoted.
Machine learning
Supervised and unsupervised learning, model validation, feature engineering, boosting.
Cloud & deployment
AWS/Azure basics, Docker, APIs, scheduling. Models that never leave a notebook create no value.
GenAI literacy
Prompting, RAG, calling LLM APIs, evaluating outputs. Now expected of every data scientist.
Is data science right for you?
Learn data science at Aegis
Aegis launched India's first Post Graduate Program in Data Science in 2015, in association with IBM, and has run it every year since. Two formats cover the same curriculum:
Full-Time PGP in Data Science, Business Analytics & AI
11 months · On-campus Mumbai · Since 2015
India's first PGP in Data Science, launched with IBM. Statistics to deployment, with industry projects and 500+ hiring partners.
Aegis programExecutive (Weekend) PGP in Data Science & AI
11 months · Sat/Sun · For 3+ yrs experience
Same rigour as the full-time program, on weekends, for working professionals who cannot pause their careers.
Frequently asked questions
Do I need to be good at maths to learn data science?
You need working statistics and a comfort with algebra, not advanced mathematics. A structured program teaches the maths in the context of real problems; most Aegis students come from engineering, commerce, science and management backgrounds.
Can a non-programmer become a data scientist?
Yes. Python and SQL are learnable in a few months of guided practice. The Aegis PGP starts from zero and admits graduates from any discipline; many of our highest-placed alumni had no prior coding.
What is the difference between a data science course and a PGP?
A short course teaches a tool. A Post Graduate Program (PGP) is a structured 9–12-month curriculum with industry projects, placement support and a recognised credential. For a career change, a PGP is the usual route.
How long does it take to become job-ready?
With full-time study, 9–11 months. Part-time or self-taught, 12–24 months. The variable is not hours but whether you complete end-to-end projects that recruiters can inspect.
What is the data scientist salary in India for freshers?
Indicatively ₹6–12 LPA for data scientist roles and ₹4–8 LPA for analyst roles in 2026, rising to ₹25–45 LPA at senior levels. Aegis placements have ranged from ₹8.5 LPA to ₹40 LPA.
Which is better in 2026: data science or AI?
They are not alternatives. Data science is the foundation; GenAI and AI agents are the fastest-growing specialism on top of it. Learn the foundation first, then specialise, which is exactly how the Aegis PGPs are sequenced.