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
Data Science is the discipline of turning raw data into business-changing decisions using statistics, machine learning, and storytelling. From forecasting demand at Deliveroo to detecting fraud at Stripe, 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.
Curriculum
Each phase moves from competence building into portfolio-visible output.
Timeline
Weeks 1-8
Establish a strong base in Python, statistics, and applied analysis workflows.
Timeline
Weeks 9-18
Learn to choose, train, and evaluate models with decision quality in mind.
Timeline
Weeks 19-28
Translate model output into business recommendations and clean narratives.
Timeline
Weeks 29-36
Package your work into visible, role-facing proof.
Own analysis, experimentation, and predictive work that influences product and business decisions.
$88,000
starting pay for
Data Scientists
Bridge structured analysis with practical model implementation, deployment, and evaluation.
$95,000
starting pay for
Applied ML Engineers
Turn ambiguous business questions into measurable, data-backed recommendations and experiments.
$80,000
starting pay for
Analytics Scientists
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
Select a market benchmark to view salary estimates at different experience stages.
Global Benchmark Salary
Global Benchmark Salary
Global Benchmark Salary
Global Benchmark Salary
Source: Glassdoor.com and LinkedIn Salary Insights
A clear picture of the professional profile you will build over the program.
$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.
Grouping tools by what they enable keeps the learning story cleaner and more persuasive.
Analysis
Modeling
Communication
Real-World Applications
See how the curriculum of the **Data Science** program is directly applied in solving critical challenges across global sectors. Click any industry to view practice projects you can build.
“Predictive modeling engines to classify patients at high risk of readmission or septic shock using streaming telemetry data from ICU monitors.”
“Classification models predicting student drop-out likelihood based on submission delays, forum activity, and quiz scoring.”
“Time-series forecasting models predicting equity index movements, option pricing indicators, and interest rate trends.”
“Dynamic pricing algorithms optimizing margins based on competitive price scrapers, weather, and inventory levels.”
“Vibration telemetry anomaly classifiers predicting bearing failures on turbine shafts using autoencoders.”
“Malware family classifiers categorizing binary files based on assembly patterns and API call strings.”
“Yield prediction models estimating harvest volumes based on weather cycles and soil sensor data.”
“Time-series forecasting models predicting transport demand and port congestion rates.”
“Customer clustering models grouping user segments based on navigation patterns and purchase histories.”
“Anomaly detection models flagging public benefit claims fraud based on filing patterns and credential metrics.”
“Flight path anomaly classification models predicting engine degradation from stream telemetry.”
“Time-series predictive models estimating battery health decay in electric vehicle fleets.”
“Molecular mix prediction engines estimating compound shelf life based on temperature telemetry.”
“Time-series classification models predicting structural decay in bridge support sensors.”
“Classification models predicting client churn risk based on service engagement telemetry.”
“Recommendation classification models matching viewer profiles to catalog titles.”
“Convolutional neural networks classifying fabric quality and identifying weave anomalies.”
“Time-series classification models predicting oxygen decay in aquaculture tanks.”
“Time-series classification models predicting refrigeration failure in transit vehicles.”
“Time-series forecasting models predicting hotel occupancy rates based on search telemetry.”
“Time-series forecasting models predicting server CPU load based on application traffic.”
“Classification models predicting litigation duration based on court docket data.”
“Time-series classification models predicting drill bit wear based on telemetry.”
“Classification models predicting donor churn based on engagement history logs.”
“Time-series classification models predicting solar cell decay based on telemetry.”
“Time-series forecasting models predicting rental occupancy rates based on search telemetry.”
“Molecular mix prediction engines estimating compound shelf life based on temperature telemetry.”
“Time-series classification models predicting cell tower decay based on telemetry.”
A structured path for learners who want to move from notebooks and theory into robust analysis, machine learning decisions, and portfolio-grade problem solving.
Move from dashboard support into predictive, experimental, and model-backed decisions.
Turn quantitative ability into a sharper, marketable data profile.
Build confidence with practical modeling instead of broad, unfocused theory.
The same trust-first system used on the homepage carries through to each program detail page.
01
Refine the story you tell about your background, projects, and direction.
02
Turn assignments into portfolio assets, case studies, and stronger proof.
03
Move into applications and interviews with clearer materials and tighter narratives.
