6 Months Certification Program in Data science & Applied Machine Learning.
Program Overview
Applied Data Science & Generative AI Certification
Duration: 6 Months | Format: Online (Weekend-Only) | Certification by SkillExpress
This intensive 6-month program is designed to transform serious beginners, early professionals, and career switchers into job-ready Data Science and AI practitioners. Built by industry experts and academic mentors, it combines foundations, advanced ML, deployment, and Generative AI into one career-focused journey.
Over 24 weeks, learners progress from Excel, Python, SQL, and statistics to core machine learning, time series forecasting, NLP, and cutting-edge generative AI. The program emphasizes real-world application through 12+ applied projects, 3 mini capstones, 2 hackathons, and a final industry-level capstone. With a guaranteed internship and deep focus on explainable AI (SHAP, LIME) and MLOps, graduates walk away with deployable projects and professional portfolios.
By the end of the course, you’ll be confident in solving real business problems—from churn prediction and fraud detection to stock forecasting and NLP-driven insights—using tools like Python, Scikit-learn, SQL, Docker, MLflow, Tableau, and GitHub. You’ll graduate career-ready for roles such as Data Analyst, ML Engineer, or Junior Data Scientist.
What Makes This Program a Smart Career Investment
Job-Ready Curriculum with AI & Deployment
Covers the full stack: Excel, SQL, Python, ML, Generative AI, Explainable AI, Cloud, and MLOps.
Hackathons + Internship Guarantee
Experience 2 real-world hackathons plus a 3-month paid internship with top partners like Tata, Citi, BCG, and Quantium.
Capstone + Project Portfolio
Build a GitHub-ready portfolio with 12+ projects and a multi-domain capstone to showcase your skills to employers.
Explainable & Responsible AI Training
Master SHAP, LIME, and bias detection—skills that set you apart in ethical and explainable AI practices.
Career Support Built-In
Resume, LinkedIn, and interview prep, combined with 1:1 mentorship, ensure you’re market-ready on Day 1.
Ideal for Beginners & Career Switchers
Whether you’re a fresher, analyst, or self-taught coder, this program provides the structure, mentorship, and outcomes you need.
Phase 1: Data Foundations (Weeks 1–5)
Build strong foundations in Excel, Python, SQL, and Statistics.
Week 1: Excel – Basics for Analytics
• Navigating Excel interface.
• Data entry, formatting, formula basics.
• Functions: SUM, AVERAGE, IF.
• Charts and tables.
Week 2: Excel – Advanced Analytics
• VLOOKUP, HLOOKUP, INDEX, MATCH.
• Pivot tables & Power Pivot.
• Conditional formatting, What-If Analysis.
• Dashboard creation.
Mini Project: Build a Sales Dashboard.
Week 3: Python Programming Fundamentals
• Python setup (Jupyter/VS Code).
• Variables, operators, loops, conditionals.
• Functions & list comprehensions.
• NumPy basics for array operations.
Week 4: Python for Data Science & SQL Basics
• Pandas for data manipulation (merge, groupby, pivot).
• Data cleaning (missing values, duplicates).
• SQL basics: SELECT, WHERE, GROUP BY.
• Joins (INNER, LEFT, RIGHT).
Mini Project: COVID Data Analysis + SQL Retail Queries.
Week 5: Statistics & Probability for DS
• Descriptive stats (mean, median, variance).
• Probability distributions: Normal, Binomial, Poisson.
• Hypothesis testing: z-test, t-test, chi-square.
• A/B testing & correlation.
Mini Project: Marketing Campaign Effectiveness Study.
Phase 2: Applied Analytics (Weeks 6–8)
Data wrangling, visualization, and business storytelling.
Week 6: Data Wrangling & Feature Engineering
• Handling missing data & outliers.
• Encoding categorical variables.
• Scaling & normalization.
• Feature selection techniques.
Week 7: Visualization & BI Tools
• Matplotlib & Seaborn basics.
• Advanced plots: heatmaps, pairplots, boxplots.
• Tableau/Power BI dashboards.
• Data storytelling principles.
Week 8: Mini Capstone I
End-to-End Project: Churn Prediction Dashboard (EDA + Visualization + BI).
Phase 3: Core Machine Learning (Weeks 9–12)
Supervised & unsupervised learning with real-world projects.
Week 9: Regression Models
• Linear & Multiple Regression.
• Polynomial Regression.
• Regularization: Ridge, Lasso.
• Evaluation: RMSE, R².
Week 10: Classification Models
• Logistic Regression.
• Decision Trees, KNN, Naive Bayes.
• Evaluation metrics: Precision, Recall, F1, ROC-AUC.
Week 11: Unsupervised Learning
• Clustering: KMeans, hierarchical.
• PCA & dimensionality reduction.
• Anomaly detection.
Week 12: Mini Capstone II
End-to-End Project: Loan Default Prediction.
Phase 4: Advanced ML & Generative AI (Weeks 13–16)
Learn ensembles, time series, NLP, and generative AI.
Week 13: Ensemble Learning
• Bagging, Random Forest.
• Boosting: XGBoost, LightGBM.
• Hyperparameter tuning (GridSearchCV, RandomizedSearchCV).
Week 14: Time Series Forecasting
• Stationarity, seasonality, trends.
• ARIMA, SARIMA, Prophet.
• Intro to LSTMs for time series.
Project: Stock Price Forecasting.
Week 15: Natural Language Processing (NLP)
• Text preprocessing: tokenization, stemming, lemmatization.
• Feature extraction: TF-IDF, word embeddings.
• Sentiment analysis.
Week 16: Generative AI
• Introduction to ChatGPT, Gemini, DALL·E.
• Prompt engineering basics.
• Applications in text, images & automation.
Hackathon 1 (End of Month 4): Retail + Social Media Data Hackathon.
Phase 5: Deployment & MLOps (Weeks 17–20)
Deployment, pipelines, and monitoring for production readiness.
Week 17: Model Deployment Basics
• Flask & FastAPI for ML APIs.
• Streamlit for interactive dashboards.
Week 18: Version Control & Containers
• Git & GitHub for code management.
• Docker for containerization.
• CI/CD pipeline basics.
Week 19: Cloud for ML Deployment
• AWS S3, EC2 basics.
• GCP alternatives.
• Hosting models as APIs.
Week 20: ML Pipelines & Model Management (NEW)
• Scikit-learn pipelines.
• Feature engineering with FunctionTransformer.
• Model persistence (Pickle, Joblib).
• MLflow for tracking & versioning.
Mini Capstone III: Recommendation System Deployed on Streamlit/Docker.
Phase 6: Explainable AI, Capstone & Career (Weeks 21–24)
Explainability, responsible AI, capstone, and career prep.
Week 21: Explainable AI (XAI) – Part I
• SHAP, LIME.
• PDPs, feature importance.
• Fairness & bias in ML.
Week 22: Explainable AI (XAI) – Part II
• Case studies on XAI.
• Implementing responsible AI practices.
Weeks 23: Final Capstone Project
• Choose domain: Finance, Retail, Healthcare, Social Media.
• Deliverables: Data Cleaning → ML → Deployment → Presentation.
Week 24: Hackathon + Career Prep
• Hackathon 2: Grand Capstone Hackathon (multi-domain).
• Resume & LinkedIn optimization.
• Mock technical & HR interviews.
• Building GitHub & Tableau portfolios.
Internship Track
• Guaranteed Internship.
• Partner companies: Tata, Citi, BCG, Quantium, Geostat, etc.
• First-come-first-serve basis, non-transferable.
• Internship project contributes to portfolio.
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6 Months Certification Program in Data science & Applied Machine Learning.
Course Fees: ₹₹1,20,000/- + 18% GST (EMI Options Available)
Buy Now- Duration 6 months
- Modules 6