Data Science Certification Program

1. Data Analysis Foundation (First 4-5 Months)

Excel & Google Sheets (1 Month)

• Formulas & Functions → VLOOKUP, HLOOKUP, INDEX, MATCH, IF, nested formulas
• Data Cleaning → Handling duplicates, missing values, formatting
• Pivot Tables & Charts → Summarising datasets, visual reporting
• Automation with VBA → Macros for repetitive tasks
• Google Sheets → Collaboration, add-ons, automation scripts

Widely used in finance, HR, operations, and reporting dashboards.

SQL(MySQL) & MongoDB (1.5 Months)

• SQL Basics: SELECT, WHERE, ORDER BY, GROUP BY
• Intermediate: Joins (INNER, LEFT, RIGHT, FULL), Subqueries, CASE statements
• Advanced: CTEs, Window Functions (ROW_NUMBER, RANK, LAG, LEAD), Stored Procedures, Indexes
• MongoDB (NoSQL): CRUD, Aggregation Pipeline, Indexing

SQL manages structured data (banking, ERP, CRM), MongoDB powers e-commerce, IoT, and user behaviour apps.

Python for Data Analysis (2 Months)

• Fundamentals: Data types, loops, OOP, error handling
• Libraries: NumPy (arrays), Pandas (wrangling), Matplotlib/Seaborn/Plotly (visuals)
• EDA: Cleaning, profiling, missing values, trend detection

Python automates workflows and prepares clean data for analytics & ML.

Business Intelligence & Visualisation (1 Month)

• Power BI: DAX, dashboards, publishing, mobile reports
• Looker Studio: Real-time dashboards from SQL/Sheets

Transforms raw data into insights for decision-making.

Project Management Tools (2 Weeks)

• Jira, Trello, Asana, Agile, Scrum basics

Prepares you for corporate collaboration.

Mini Projects: Automated reporting in Excel (VBA), E-commerce dashboard in Power BI, SQL-based customer segmentation.

2. Python for Data Science (2.5 Months, with project)

• Fundamentals → OOP: Data types, control flow, functions, error handling, OOP for reusable DS utilities; file I/O (CSV/JSON)
• Core Libraries: NumPy (arrays, vectorisation), Pandas (joins, groupby, missing data, time-series), Matplotlib/Seaborn/Plotly (static + interactive visuals)
• Stats Basics for DS: Mean/median/variance/std, distributions; quick inference to guide EDA

Project: Multi-source EDA + insight report (notebook + HTML export + slide deck)

3. Core Data Science Topics

A) Data Collection & Cleaning (1 Month)

• Pandas/NumPy for wrangling; BeautifulSoup/Scrapy for web data; outlier/missing-value treatment; scaling/encoding

Mini Project: Customer 360 cleaning pipeline + profiling report

B) Data Visualisation (3 Weeks)

• Matplotlib/Seaborn/Plotly, trend, correlation, distribution, heatmaps; interactive drilldowns for product teams

Mini Project: Interactive product performance explorer (Plotly)

C) Statistics & Hypothesis Testing (3 Weeks)

• SciPy/Statsmodels: t/chi-square/ANOVA, regression diagnostics, A/B test readouts; confidence intervals & effect sizes

Mini Project: Marketing lift study with executive summary

4. Machine Learning (2 Months, with project)

• Supervised: Linear/Logistic, Trees/Random Forests, Gradient Boosting (XGBoost/LightGBM/CatBoost)
• Unsupervised: K-means/hierarchical, PCA/t-SNE/UMAP for dimensionality reduction
• Evaluation: Train/validate/test splits; cross-validation; metrics (Accuracy, Precision/Recall/F1, ROC-AUC, MAE/MSE/R²)
• Model Tuning: Optuna/Hyperopt/RandomizedSearch; pipelines for production-ready training

Project: Churn/Fraud/Credit-risk prediction with hyperparameter-tuned models & SHAP insights

5. Deep Learning (2 Months, with project)

• Foundations: Perceptron, activations (ReLU/sigmoid), forward/backprop, losses; optimisers (SGD/Adam/RMSprop); regularisation (dropout, L1/L2, batch norm)
• CNNs: Image classification/defect detection
• RNNs/LSTM/GRU: Sequences & time-series forecasting
• Transformers & LLMs: Attention, BERT, GPT; embeddings, fine-tuning & RAG basics for domain Q&A
• GANs/Autoencoders: Synthesis, anomaly detection, compression
• Explainability: LIME/SHAP, gradient-based saliency for trust in models

Project: End-to-end DL use case (e.g., image classifier with API deployment)

6. Big Data & Distributed Computing (1 Month)

• Dask for out-of-memory Pandas; PySpark for distributed ETL/ML; partitioning & caching strategies

Project: Millions-row analytics in Spark + BI connector

7. NLP & Applied AI (1 Month)

• NLTK/SpaCy for tokenisation/NER; Hugging Face for transformers; prompt engineering for LLM tasks

Project: Sentiment & topic insights dashboard + chatbot prototype

8. Deployment & MLOps (1 Month)

• APIs: Flask/FastAPI to serve models; input validation; logging
• Ops: Docker/Kubernetes for portable/scalable serving; MLflow for experiment tracking; Airflow for training pipelines; DVC for data/versioning
• Cloud (Included): Basics on AWS/Azure/GCP, compute, storage, scheduled jobs, monitoring

Project: Model-in-production with CI/CD, monitoring & rollback plan

9. Databases: SQL & MongoDB (2 Months, with project)

SQL (Basic → Advanced)

• Data types/operators; SELECT/FROM/WHERE; GROUP BY; ORDER BY/LIMIT
• Joins (INNER/LEFT/RIGHT/FULL), subqueries, CTE, CASE; window functions (ROW_NUMBER/RANK/DENSE_RANK/LAG/LEAD)
• CTEs; HAVING; string/date functions; DML (INSERT/UPDATE/DELETE/MERGE)
• Performance: Indexes, query plans, partitioning; views; transactions for integrity

MongoDB

• CRUD, aggregation pipeline, indexes for semi-structured data

Tools (with purpose)

• SQL Workbench/SSMS for IDE workflows; SQLAlchemy+pandas for Python-DB pipelines; cloud warehouses (BigQuery/Redshift/Snowflake) for large-scale analytics
• Power BI/Tableau to visualise SQL outputs; Airflow to schedule SQL/ETL

Project: SQL feature-store + automated daily refresh feeding a production model

10. Automation in Data Science

• Pipelines: Airflow/Luigi for ETL; cron jobs for retraining/refresh
• AutoML: TPOT/Auto-sklearn to try many models/features automatically
• Tracking & Reporting: MLflow/DVC for runs/data; Papermill for parameterised reporting notebooks; scheduled email/HTML reports