Data Science Master Program
Industry-oriented 4-month intensive Data Science program designed to transform beginners into job-ready professionals through strong fundamentals, hands-on practice, machine learning, deep learning, modern AI tools, deployment skills, and real-world capstone projects.
Syllabus
13 MODULESIntroduction to Data Science
Week 1. Learn what Data Science is, lifecycle of data science projects, roles in industry including Analyst, Scientist, Engineer, real-world applications, developer toolkit overview, VS Code, Jupyter, PyCharm, environment setup, Anaconda installation, Conda vs Miniconda, virtual environments, Jupyter Notebook and JupyterLab deep dive.
Python for Data Science
Week 2. Master Core Python including variables, data types, operators, loops, conditional statements, functions, lambda expressions, OOP, exception handling, file handling, advanced collections, comprehensions, iterators, generators, modules, packages, and working with APIs.
NumPy Numerical Computing
Week 3. Learn NumPy arrays, indexing, slicing, multidimensional arrays, axis concepts, data types, broadcasting, mathematical functions, statistical operations, random module, linear algebra operations, and performance optimization.
Pandas Data Analysis
Week 4. Learn Series, DataFrames, CSV/Excel/JSON import-export, filtering, handling missing data, data cleaning, transformation, aggregation, GroupBy, merging, joining, pivot tables, melt operations, and time series data analysis.
Data Visualization
Week 5. Master Matplotlib and Seaborn including line charts, bar charts, pie charts, histograms, scatter plots, stack plots, subplots, customization, distribution plots, categorical plots, heatmaps, pairplots, and advanced styling.
Data Collection Techniques
Week 6. Learn data collection workflows, web scraping fundamentals, HTML basics for scraping, Requests library, BeautifulSoup parsing, API-based data collection, data ethics, and legal considerations.
SQL for Data Science
Week 7. Learn database fundamentals, MySQL setup, CRUD operations, constraints, keys, joins (Inner, Left, Right, Full), aggregations, GROUP BY, subqueries, indexes, optimization, views, stored procedures, transactions, and date-time functions.
Mathematics for Data Science
Week 8. Learn probability basics, conditional probability, Bayes theorem, probability distributions (Normal, Binomial, Poisson), descriptive statistics, inferential statistics, hypothesis testing, confidence intervals, correlation, covariance, vectors, matrices, eigenvalues, and eigenvectors.
Machine Learning
Weeks 9-10. Learn ML fundamentals, supervised, unsupervised, reinforcement learning, model training process, Linear Regression, Logistic Regression, Decision Trees, Random Forest, KNN, K-Means clustering, Scikit-learn training, evaluation metrics (RMSE, MAE), feature engineering, scaling, pipelines, ColumnTransformer, and deployment with Joblib.
Deep Learning
Week 11. Learn neural network basics, perceptron, activation functions, loss functions, TensorFlow, Keras, training neural networks, MNIST implementation, and model evaluation.
Web Development for Data Science
Week 12. Learn HTML, CSS basics, Flask framework, routing, templates, forms handling, Jinja templates, API development, and deploying machine learning models through web applications.
Modern AI & Industry Tools
Week 13. Learn Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Git & GitHub workflows, version control best practices, AI tools for data scientists, code assistants, data analysis tools, and automation platforms.
Capstone Project
Month 4. Build an end-to-end industry project including multi-source data collection, data cleaning, preprocessing, exploratory data analysis, machine learning model building, optimization, deployment using Flask API, and AI/LLM feature integration.