Advance Data Science Training for Professionals

Course Overview | Instructor Name- Shuvam Chakraborty, IIT DELHI 
This training Program provides a comprehensive pathway from fundamentals of Python, mathematics, and statistics to applied machine learning and data science.The course emphasizes practical, hands-on learning with 3 minor projects and 2 major projects. Students will build skills in Python, SQL, data analysis, visualization, dashboards, and machine learning.
Projects: 2 Minor + 3 Major (1 PowerBI Dashboard, 1 ML, 1 Full Pipeline)
Tools: Python, SQL, Pandas, Matplotlib/Seaborn, Power BI (free version), Stream
lit/Dash for dashboarding.

1. Module 1: Python Foundations (Classes 1–6)
   Class 1: Course introduction, overview of analytics and data science, setting up Python
   environment.
   Class 2: Python basics: variables, data types, operators.
   Class 3: Control structures: conditionals and loops.
   Class 4: Functions, modules, file handling.
   Class 5: Data structures: lists, tuples, dictionaries, sets.
   Class 6: Error handling and basic OOP concepts in Python.
2. Module 2: Mathematics and Statistics for Data Science (Classes
   7–10)
   Class 7: Descriptive statistics: mean, median, variance, standard deviation.
   Class 8: Probability basics and distributions (normal, binomial, etc.).
   Class 9: Inferential statistics: hypothesis testing, p-values, confidence intervals.
   Class 10: Linear algebra essentials: vectors, matrices, dot product, eigenvalues.
3. Module 3: SQL for Data Analytics (Classes 11–13)
   Class 11: Introduction to databases, SQL basics: SELECT, WHERE, ORDER BY.
   Class 12: Joins, GROUP BY, aggregate functions.
   Class 13: Subqueries, views, window functions. Minor Project 1: SQL Analysis Project.
4. Module 4: Exploratory Data Analysis (Classes 14–18)
   Class 14: Data cleaning: missing values, duplicates, outliers.
   Class 15: Data wrangling with Pandas.
   Class 16: Data visualization: Matplotlib and Seaborn basics.
   Class 17: Correlation analysis, feature engineering.
   Class 18: Comprehensive practice session. Minor Project 2: EDA Project.
5. Module 5: Dashboards and Visualization Tools (Classes 19–21)
   Class 19: Introduction to Power BI (free desktop version).
   Class 20: Creating reports, dashboards, and visual storytelling.
   Class 21: Publishing and sharing dashboards. Major Project 1: Power BI Dashboard Project.
6. Module 6: Machine Learning Foundations (Classes 22–30)
   Class 22: Introduction to ML: types of learning, ML pipeline.
   Class 23: Linear regression, model evaluation (MSE, RMSE, R2).
   Class 24: Logistic regression, classification metrics (accuracy, precision, recall, F1).
   Class 25: Decision trees and random forest.
   Class 26: k-NN, Na¨ıve Bayes.
   Class 27: Support Vector Machines.
   Class 28: Ensemble learning (Bagging, Boosting).
   Class 29: Model selection and hyperparameter tuning.
   Class 30: Major Project 2: Machine Learning Project (dataset + applying ML
   algorithms)
7. Module 7: Introduction to Deep Learning (Classes 31–33)
   Class 31: Basics of neural networks: perceptron, activation functions.
   Class 32: Introduction to TensorFlow/Keras; building a simple NN.
   Class 33: Training and evaluating small DL models.
8. Module 8: End-to-End Data Science Pipeline (Classes 34–40)
   Class 34: Web scraping with BeautifulSoup/Requests.
   Class 35: Data cleaning and preprocessing pipeline.
   Class 36: Feature engineering and EDA integration.
   Class 37: Building predictive models from scraped data.
   Class 38: Deploying results with Streamlit/Dash dashboards.
   Class 39: Final review and Q&A. Major Project 3: Full Pipeline Project (Web scrap
   ing → Cleaning → ML → Dashboard).
   Class 40: Project presentations and wrap-up.
9. Project Overview
   • Minor Project 1 (SQL): Perform structured queries on a sample database to
   answer business questions.
   • Minor Project 2 (EDA): Clean and analyze a real dataset, visualize distribu
   tions, correlations, and insights.
   • Major Project 1 (Power BI): Create an interactive dashboard to present data
   insights visually.
   • Major Project 2 (ML): Use a dataset to apply regression/classification algo
   rithms, evaluate, and present results.
   • Major Project 3 (Full Pipeline): Scrape data from a website, clean and pre
   process, perform EDA, build predictive models, and present results on a dashboard
   (Streamlit/Dash).