Become a Data Scientist
Build a modern data science profile with stronger modelling, communication, and product context.
Months
Part-time
Projects
Portfolio
Tools
Industry-std
1:1
Mentorship
Personal
What is Data Science?
Data Science is the discipline of turning raw data into business-changing decisions using statistics, machine learning, and storytelling. From forecasting demand at Swiggy to detecting fraud at Razorpay, data scientists power the hardest decisions in modern companies. In 9 months, you will learn Python, ML modelling, experimentation, and communication so you can own analysis that actually moves the needle.
Where can your Data Science Mastery training take you?
Data Scientist
Own analysis, experimentation, and predictive work that influences product and business decisions.
$88,000
starting pay for
Data Scientists
Applied ML Engineer
Bridge structured analysis with practical model implementation, deployment, and evaluation.
$95,000
starting pay for
Applied ML Engineers
Analytics Scientist
Turn ambiguous business questions into measurable, data-backed recommendations and experiments.
$80,000
starting pay for
Analytics Scientists
Quantitative Analyst
Apply statistical modelling and machine learning to finance, risk, and forecasting problems.
$110,000
starting pay for
Quantitative Analysts
Sources: Glassdoor.in
and LinkedIn Salary Insights
Data Science is a top-compensated career path with strong long-term growth
Source: Glassdoor.com and LinkedIn Salary Insights
You after Wills Education
A clear picture of the professional profile you will build over the program.
Data Scientist
$88,000
Expected salary
Hard Skills
Soft Skills
Education
Projects
Customer Churn Prediction
Built an end-to-end churn model with feature engineering, validation, and stakeholder writeup for a telecom dataset.
Curriculum
A paced roadmap,not a chapter list.
Each phase moves from competence building into portfolio-visible output.
Timeline
Weeks 1-8
Foundations and Analysis
Establish a strong base in Python, statistics, and applied analysis workflows.
Timeline
Weeks 9-18
Modeling and Evaluation
Learn to choose, train, and evaluate models with decision quality in mind.
Timeline
Weeks 19-28
Communication and Experiments
Translate model output into business recommendations and clean narratives.
Timeline
Weeks 29-36
Portfolio and Hiring Readiness
Package your work into visible, role-facing proof.
The stack is organized around capability, not feature-name overload.
Grouping tools by what they enable keeps the learning story cleaner and more persuasive.
Analysis
Modeling
Communication
A role-aware structure for learners with different starting points.
A structured path for learners who want to move from notebooks and theory into robust analysis, machine learning decisions, and portfolio-grade problem solving.
Analysts aiming higher
Move from dashboard support into predictive, experimental, and model-backed decisions.
STEM graduates
Turn quantitative ability into a sharper, marketable data profile.
Working professionals
Build confidence with practical modeling instead of broad, unfocused theory.
Career support is presented as a structured process, not a vague promise.
The same trust-first system used on the homepage carries through to each program detail page.
01
Position
Refine the story you tell about your background, projects, and direction.
02
Package
Turn assignments into portfolio assets, case studies, and stronger proof.
03
Pursue
Move into applications and interviews with clearer materials and tighter narratives.
Questions learners ask before choosing Data Science.
Apply
Move from interest to admissions with a clearer signal.
Data Science is designed to feel deliberate from first impression to final CTA. The application flow keeps that same tone.