Practical, project-driven curriculum designed for real-world hiring. Each module contains hands-on labs, assignments and projects aligned to industry needs.
Foundation — SQL & Excel (Basics → Advanced)
- SQL: Intro to SQL, DDL/DML/DQL, Aggregate functions, Date functions, Sub-queries, Joins, Views, Indexes, Union/Intersect, Stored procedures, Advanced SQL practice.
- Excel: Importing data, formatting, formulas, lookup & reference, pivot tables, charts, what-if analysis, macros basics, reporting in Excel.
- Deliverables: SQL exercises, Excel reports & dashboards.
Core Track — Python, NumPy, Pandas, EDA
- Python fundamentals: variables, flow controls, functions, collections (list, tuple, dict), list comprehensions, lambda functions.
- NumPy: arrays, indexing, slicing, array operations.
- Pandas: Series & DataFrame creation, reading from files, indexing, sorting, concatenation, joins, merging, reshaping, pivot tables, groupby, missing-value handling, duplicates, treatment.
- Exploratory Data Analysis (EDA): summary statistics, handling missing values, variable distributions, correlation and covariance, advanced data exploration techniques.
- Deliverables: EDA notebook + data cleaning pipeline.
Visualization & Statistics
- Matplotlib & Seaborn: line plots, histograms, boxplots, scatter, heatmaps, pairplots, violin, joint, count plots.
- Summary statistics, central tendency, dispersion, skewness, kurtosis.
- Probability basics, discrete & continuous distributions (Bernoulli, Binomial, Poisson, Uniform, Normal).
- Hypothesis testing: t-tests, chi-square, ANOVA, post-hoc tests; assumptions, normality tests.
Machine Learning — Supervised & Unsupervised
- Supervised: Linear Regression (OLS), Logistic Regression (MLE), Model evaluation metrics, Regularization (L1/L2), Feature scaling, Feature selection.
- Tree-based: Decision Trees, Random Forests — feature importance, pruning, tuning.
- Ensembling: Bagging, Boosting (XGBoost/AdaBoost), stacking concepts.
- Unsupervised: K-means, Hierarchical clustering, PCA (dimensionality reduction).
- Model tuning: cross-validation, hyperparameter tuning, bias-variance tradeoff, overfitting/underfitting.
- Deliverables: Regression & Classification projects (Property price prediction, Vaccine usage prediction, Heart disease prediction).
Time Series & Forecasting
- Time series components, trend & seasonality, visualizing time series.
- Exponential smoothing: Holt, Holt-Winters; ARIMA modelling; forecasting evaluation.
- Deliverables: Forecast project (sales or mortgage analysis).
Advanced Analytics, Cloud & Deployment
- Advanced topics: Association rule mining (Apriori), Market Basket Analysis, Ensemble techniques, XGBoost.
- Cloud basics & deployment: AWS fundamentals, EC2, SageMaker overview, deployment steps, well-architected framework basics.
- Web integration: Flask basics, connecting ML models via APIs.
- Deliverables: Cloud-deployable model demo, Flask API wrapper for model inference.
BI & Visualization Tools (Tableau & Power BI)
- Power BI: report building, data transformation, dashboards.
- Tableau: interface, data connections, calculations, dashboards & stories, mapping & visual analytics.
- Deliverables: Dashboard project (interactive sales dashboard).
Projects & Capstone
- Hands-on projects throughout the course: E-commerce customer segmentation, Taxi fare prediction, Heart disease prediction, Property price prediction, Stock analysis, Forecasting sales.
- Capstone Project: real-world comprehensive project (students apply end-to-end pipeline from data ingestion to deployment).
- Presentation: Project demo, code repo, documentation & report submission.
Prerequisites & Who Should Apply
- Basic programming knowledge (recommended) — helpful but not mandatory.
- Motivation to work on datasets and complete assignments.
- Comfort with mathematics at high-school level; statistics basics helpful.
Assessment, Placement, & Certification
- Assessments: module-level quizzes, practical assignments, class assessments and project evaluations.
- Certification: JCRM certificate on successful completion & capstone project submission.
- Placement assistance: interview prep, resume review, and placement support network.