track · analyze
Data Science Track
Prepare for Data Scientist roles focused on exploratory analysis, statistical reasoning, predictive modeling, and communicating insight to technical and non-technical stakeholders.
Before you start
- Python, SQL, and spreadsheet fundamentals
- Algebra, probability, and statistics fundamentals
- Comfort turning an ambiguous business question into an analysis plan
- Ability to complete weekly analysis projects and maintain a portfolio
This track shares its analytical foundation with the Machine Learning Engineer track, built around exploration, statistics, and modeling. Where that track goes deep on production engineering, this one goes deep on analysis and communicating findings to stakeholders.
Roadmap
| Weeks | Focus | Portfolio checkpoint |
|---|---|---|
| Weeks 1-3 | Data handling and exploratory analysis | Clean EDA repository |
| Weeks 4-6 | Statistics and probability for analysis | From-scratch regression exercise |
| Weeks 7-9 | Feature engineering and data preparation | Reusable preprocessing workflow |
| Weeks 10-12 | Predictive modeling and segmentation | Business prediction + segmentation project |
| Weeks 13-14 | Communication and interview readiness | Stakeholder-ready portfolio report |
Detailed curriculum
› stage/01/exploreData Handling and Exploratory Analysis
Project: build a clean data ingestion, validation, and EDA pipeline for a messy customer, sales, or operations dataset.
- Python and data cleaning with pandas and NumPy
- SQL joins, aggregations, CTEs, and window functions
- Git workflow and reproducible notebooks
- Asking sharper questions of a raw dataset before modeling anything
› stage/02/statisticsStatistics and Probability for Analysis
Project: implement linear regression with gradient descent from scratch, then explain the statistical assumptions behind the result.
- Linear algebra: vectors, matrices, dot product
- Probability: distributions, conditional probability, Bayes' rule
- Statistics: mean, standard deviation, correlation, sampling, confidence intervals
- Reading a model's assumptions instead of trusting its output blindly
› stage/03/prepFeature Engineering and Data Preparation
Project: create a reusable preprocessing workflow for a tabular business dataset and document every transformation.
- Missing-value and outlier handling, duplicate checks
- Encoding and scaling choices, and when each applies
- Train/validation/test splits and leakage prevention
- Documenting transformations so another analyst can reproduce them
› stage/04/predictPredictive Modeling for Business Questions
Project: predict a business outcome (price, churn, risk, or demand) and explain the result in terms a stakeholder can act on.
- Regression and classification problem framing
- Choosing metrics that match the business objective, not just accuracy
- Residual and error analysis
- Translating a model's output into a recommendation
› stage/05/segmentSegmentation, Clustering, and Anomaly Detection
Project: build customer segmentation and anomaly detection for transaction, usage, or operational data.
- Distance metrics and clustering assumptions
- Cluster validation and profiling
- Dimensionality reduction for visualization
- Flagging unusual records worth a second look
› stage/06/communicateCommunicating Insight and Interview Readiness
Project: turn one analysis into a stakeholder-ready report and defend it in a mock review.
- Explaining metrics and trade-offs to non-technical audiences
- Writing a clear analysis README and summary
- Interpreting model drivers with SHAP/LIME-style explanations
- Answering data science interview questions with real trade-offs
Capstone portfolio projects
Business Insight Report
Take one messy dataset through EDA, statistical analysis, and a predictive model, then package the findings into a stakeholder-ready report with a clear recommendation.
Segmentation and Anomaly Deep Dive
Segment users, products, or transactions and flag unusual records worth investigating, backed by a written business recommendation.
Choose Your Format
Self-paced learners who want a personalized schedule.
- Full curriculum
- Weekly 1:1 sessions
- Paced to you
Learners who want peer accountability on a fixed schedule.
- Full curriculum
- Structured batch sessions
- Peer projects
Learners who want full support through to an offer.
- 1:1 or Cohort format
- Full placement support
- Interview prep
Includes full placement support and interview prep.