About This Program
Comprehensive Data Science program from basics to advanced ML/DL. Work on real datasets, build ML models, deploy to production. Perfect for career switchers and graduates.
What You'll Get
30+ Real Datasets
ML Model Deployment
Big Data with Spark
Statistics & Mathematics
Kaggle Competition Training
Program Curriculum
1Python, SQL & Reproducible Data Science3 Weeks
Key Topics:
Python Programming, Data Structures and Functions
NumPy, Pandas and Vectorized Data Manipulation
SQL Joins, Aggregations, Window Functions and CTEs
Jupyter, Virtual Environments and Dependency Management
Git, Unit Testing and Reproducible Analytical Workflows
Data Science Code Quality, Documentation and Peer Review
2Mathematics, Probability & Statistical Inference4 Weeks
Key Topics:
Linear Algebra, Matrix Operations and Decomposition
Calculus, Gradients and Numerical Optimization
Probability Distributions and Random Variables
Sampling, Estimation and Confidence Intervals
Frequentist and Bayesian Statistical Reasoning
Assumptions, Uncertainty and Statistical Error
3Data Acquisition, Quality & Analytical Engineering3 Weeks
Key Topics:
Data Collection from Databases, APIs and Files
Relational Modeling and Analytical Data Structures
ETL/ELT, Incremental Loads and Pipeline Fundamentals
Missing Data, Outliers, Duplicates and Measurement Error
Data Validation, Lineage and Quality Monitoring
Privacy, Consent, Governance and Responsible Data Access
4Exploratory Analysis, Visualization & Data Storytelling3 Weeks
Key Topics:
Univariate, Bivariate and Multivariate Exploration
Distribution, Correlation, Segmentation and Cohort Analysis
Feature Diagnostics and Leakage Detection
Statistical Graphics with Matplotlib, Seaborn and Plotly
Dashboard Design with Power BI or Tableau
Narrative Structure, Decision Memos and Executive Communication
5Experimental Design & Causal Inference4 Weeks
Key Topics:
Hypothesis Testing, Multiple Comparisons and Effect Size
A/B Testing, Randomization and Statistical Power
Quasi-Experiments and Threats to Validity
Causal Diagrams, Confounding and Selection Bias
Matching, Regression Adjustment and Propensity Scores
Difference-in-Differences and Experiment Readouts
6Machine Learning Foundations5 Weeks
Key Topics:
Business Problem Framing, Baselines and Success Metrics
Linear, Polynomial and Regularized Regression
Logistic Regression, Decision Trees and Support Vector Machines
Preprocessing, Encoding and Feature Engineering Pipelines
Train, Validation and Test Design with Cross-Validation
Classification, Regression, Ranking and Calibration Metrics
7Advanced Machine Learning & Model Interpretation4 Weeks
Key Topics:
Random Forests, Gradient Boosting and XGBoost
Clustering, Segmentation and Mixture Models
Dimensionality Reduction and Representation Learning
Anomaly Detection and Imbalanced Classification
Recommendation Systems and Ranking
Hyperparameter Optimization, SHAP, Fairness and Error Analysis
8Time-Series Analysis & Forecasting3 Weeks
Key Topics:
Temporal Data, Trend, Seasonality and Stationarity
Exponential Smoothing, ARIMA and Classical Forecasting
Feature-Based Machine Learning for Time Series
Probabilistic, Hierarchical and Intermittent Forecasting
Backtesting, Leakage Prevention and Forecast Metrics
Forecast Monitoring, Recalibration and Business Planning
9Natural Language Processing & Text Analytics3 Weeks
Key Topics:
Text Cleaning, Tokenization and Linguistic Features
TF-IDF, Topic Models and Document Similarity
Word, Sentence and Contextual Embeddings
Sentiment, Classification and Named Entity Recognition
Transformers, Fine-Tuning and NLP Evaluation
LLM-Assisted Analytics, Retrieval and Human Verification
10Deep Learning & Multimodal Analysis4 Weeks
Key Topics:
Neural Networks, Backpropagation and Computational Graphs
PyTorch and TensorFlow/Keras Training Workflows
Optimization, Regularization and Model Diagnostics
Convolutional Networks and Visual Data Analysis
Sequence Models, Attention and Transformer Architectures
Transfer Learning, Multimodal Models and Responsible Use
11Big Data, Spark & Cloud Analytics3 Weeks
Key Topics:
Distributed Computing and Data Partitioning Fundamentals
Apache Spark, Spark SQL and PySpark
Columnar Storage, Data Lakes and Lakehouse Architecture
Cloud Warehouses with BigQuery, Redshift or Synapse
Scalable Feature Pipelines and Analytical Workloads
Performance, Cost and Reliability Trade-Offs
12MLOps, Responsible Data Science & Deployment4 Weeks
Key Topics:
Experiment Tracking, Data Versioning and Model Registry
Automated Training, Validation and Reproducibility
Batch Scoring, FastAPI Serving and Containerization
Cloud Deployment, CI/CD and Release Controls
Data Drift, Model Performance and Operational Monitoring
Model Cards, Fairness, Privacy and Governance Reviews
13Research Capstone, Portfolio & Career Readiness4 Weeks
Key Topics:
Problem Selection, Literature Review and Analytical Proposal
Data Collection, Quality Assessment and Reproducible Methodology
Statistical Analysis, Modeling and Comparative Evaluation
Deployed Model, Dashboard or Decision-Support Deliverable
Limitations, Ethics, Uncertainty and Stakeholder Recommendations
Faculty Review, Research Presentation and Oral Defense
Portfolio, Case Studies, SQL/Python and Data Science Interviews
Course Fee
₹1,10,000/ 11 Months
Limited seats available for next batch.