Data Science

Learning modules

1. 1Python for data science & scientific computing environment
2. 2Relational querying and data extraction with SQL
3. 3Numerical computing with NumPy & data wrangling with Pandas
4. 4Probability foundations, distributions & Central Limit Theorem
5. 5Statistical hypothesis testing: t-tests, ANOVA, Chi-Square & p-values
6. 6Exploratory Data Analysis (EDA) and data visualization with Seaborn
7. 7Feature engineering: Encoding, scaling, outlier handling & imputation
8. 8Supervised learning: Linear Regression, Logistic Regression, Decision Trees
9. 9Model evaluation: Cross-validation, confusion matrices, ROC-AUC, RMSE
10. 10Unsupervised learning: K-Means clustering & PCA dimensionality reduction
11. 11Capstone: End-to-end data science project from raw data to predictive model evaluation

Outcomes

• Perform thorough exploratory data analysis and feature engineering on complex datasets
• Apply probability and inferential statistics to test data-driven hypotheses
• Train, validate, and tune predictive machine learning models using scikit-learn
• Evaluate models objectively against real-world test sets using standard metrics
• Document and present complete reproducible data science workflows