🔵 Module 1: Introduction to Data Science

• What is Data Science?
  
  

• Roles: Data Analyst, Data Scientist, ML Engineer
  
  

• Real-world applications
  
  

• Tools overview: Python, SQL, Power BI, ML
  
  

🟦 Module 2: Python Programming (Beginner → Intermediate)

Basics

• Python installation & environment setup
  
  

• Variables, data types, operators
  
  

• If-else, loops
  
  

• Functions & modules
  
  

• Error handling
  
  

Data Handling

• Lists, tuples, dictionaries
  
  

• Working with files
  
  

• Libraries: NumPy, Pandas
  
  

• DataFrames, missing values, filtering
  
  

Mini Projects

• Basic calculator
  
  

• Student marks analyzer
  
  

• Simple data cleaning tasks
  
  

🟩 Module 3: Statistics & Mathematics for Data Science

• Descriptive statistics (mean, median, mode, SD)
  
  

• Probability basics
  
  

• Distribution types
  
  

• Hypothesis testing
  
  

• Correlation & covariance
  
  

• Z-score, t-test, chi-square test
  
  

• ANOVA
  
  

Mini Project

• Statistical analysis of real dataset
  
  

🟨 Module 4: Data Wrangling & Cleaning

• Handling missing values
  
  

• Outlier detection
  
  

• Data transformation
  
  

• Feature scaling
  
  

• Encoding (Label, One-Hot)
  
  

Mini Project

• Cleaning a large dataset for analysis
  
  

🟧 Module 5: Exploratory Data Analysis (EDA)

• Univariate & bivariate analysis
  
  

• Data visualization basics
  
  

• Matplotlib & Seaborn plots
  
  

• Pairplots, heatmaps, histograms
  
  

• Business insights creation
  
  

Mini Project

• EDA on sales or customer dataset
  
  

🟪 Module 6: SQL & Databases

• Basic SQL commands
  
  

• Joins (inner, left, right, full)
  
  

• Subqueries
  
  

• Window functions
  
  

• Aggregations
  
  

• Connecting Python with SQL
  
  

Mini Project

• SQL queries for an e-commerce dataset
  
  

🟥 Module 7: Data Visualization Tools

Choose any one tool for dashboards:

• Power BI OR Tableau
  
  

Topics

• Importing datasets
  
  

• Creating dashboards
  
  

• KPIs, slicers, filters
  
  

• Publishing dashboards
  
  

• Storytelling with data
  
  

Mini Project

• Sales dashboard / HR dashboard
  
  

🔶 Module 8: Machine Learning (Beginner → Advanced)

Supervised Learning

• Linear Regression
  
  

• Logistic Regression
  
  

• Decision Trees
  
  

• Random Forest
  
  

• KNN
  
  

• SVM
  
  

• Naive Bayes
  
  

Unsupervised Learning

• K-Means Clustering
  
  

• PCA
  
  

• Hierarchical clustering
  
  

Model Evaluation

• Accuracy, Precision, Recall, F1
  
  

• Confusion matrix
  
  

• ROC-AUC
  
  

Model Tuning

• GridSearchCV
  
  

• Cross-validation
  
  

Mini Projects

• House price prediction
  
  

• Customer segmentation
  
  

• Spam classification
  
  

🟫 Module 9: Time Series Analysis (Optional Advanced)

• Time series basics
  
  

• Trends, seasonality
  
  

• ARIMA
  
  

• Forecasting
  
  

Mini Project

• Sales forecasting for 3 months
  
  

🟩 Module 10: NLP (Natural Language Processing)

• Text cleaning
  
  

• Tokenization
  
  

• TF-IDF
  
  

• Word Embeddings
  
  

• Sentiment Analysis
  
  

• Basic Chatbot logic
  
  

Mini Project

• Review sentiment analyzer
  
  

🟦 Module 11: Git & GitHub (Portfolio Building)

• Git basics
  
  

• Create repo
  
  

• Push projects
  
  

• Upload notebooks & dashboards
  
  

• Build professional portfolio
  
  

🟪 Module 12: Capstone Project

A full end-to-end industry-level project:

• Data collection
  
  

• Data cleaning
  
  

• EDA
  
  

• ML model building
  
  

• Optimization
  
  

• Deployment-ready structure
  
  

• Final report + presentation
  
  

🎓 Course Completion Deliverables

• Course Certificate
  
  

• 10+ Mini Projects
  
  

• 1 Major Capstone Project
  
  

• GitHub Portfolio
  
  

• Resume and LinkedIn optimization
  
  

• Interview preparation