What will I learn?
- Python Data Stack
- Pandas & NumPy
- SQL Analytics
- Scikit-Learn
- Statistical Inference
- Seaborn & Plotly
- Streamlit
- Model Deployment
What do I need before starting?
- Basic math / statistics
- Basic Python or spreadsheet familiarity
What is the weekly roadmap?
Week 1: Exploratory Data Analysis (EDA) & Cleaning
Data wrangling with Pandas · Handling missing values & outliers · Statistical summaries · Feature engineering
Week 2: Advanced SQL for Data Analytics
Window functions · Common Table Expressions (CTEs) · Cohort analysis · Customer lifetime value modeling
Week 3: Data Visualization & Storytelling
Matplotlib & Seaborn · Interactive Plotly dashboards · Design principles for charts · Executive reporting
Week 4: Statistical Testing & A/B Experimentation
Hypothesis testing (p-values, t-test, ANOVA) · A/B test design & sample sizing · Confidence intervals · Type I/II error prevention
Week 5: Supervised Machine Learning Models
Linear & Logistic Regression · Decision Trees & Random Forests · XGBoost & LightGBM · Cross-validation & ROC-AUC
Week 6: Unsupervised Learning & Clustering
K-Means Clustering · PCA dimensionality reduction · Customer segmentation · Anomaly detection
Week 7: Data App Development with Streamlit
Interactive widgets · Real-time model inference UI · Caching big datasets · Cloud deployment
Week 8: Enterprise Churn Prediction Capstone
End-to-end ML pipeline · Business impact dollar quantification · Executive presentation & Defense
How do I earn the Data Science & Analytics Sprint certificate?
Complete every weekly task and receive a reviewed or approved result of at least 65% on each. You can then purchase the optional ProofCohort certificate bundle. Partner-issued credentials require a confirmed, authorized issuing arrangement.
Read the assessment guide →Certificate verification and support →Prepare your project evidence
For each assigned task, connect its requirements to a deliverable the reviewer can open. Explain your contribution, decisions, checks, and any unfinished work. Use the actual task instructions for submission requirements and the templates below to organize your evidence.