Topics:

• What is Data Science? Why it matters today

• Data Science lifecycle: Collect → Clean → Analyze → Model → Deploy

• Real-world applications in industries (Finance, Healthcare, Retail)

• Overview of tools: Python, Jupyter, NumPy, Pandas, Matplotlib, Scikit-learn

• Setting up your environment (Anaconda, Jupyter Notebook, VS Code)

Practice:

• Install Anaconda & create your first Jupyter Notebook

• Write a simple Python program to print dataset stats

Topics:

• Python Basics: Variables, Data Types, Loops, Functions

• Working with Lists, Tuples, Dictionaries

• File Handling & Exception Handling

• Lambda, Map, Filter, and List Comprehensions

• Introduction to Libraries: NumPy & Pandas

Practice:

• Create a Python program to analyze basic student data

• Perform addition and mean operations using NumPy arrays

Topics:

• Data Importing (CSV, Excel, JSON)

• Data Cleaning: Handling Missing Values, Duplicates, Outliers

• Data Transformation: GroupBy, Merge, Pivot Tables

• Descriptive Statistics & Correlation

• Working with Time Series Data

Practice:

• Analyze a sample “Sales” dataset using Pandas

• Clean, merge, and summarize data insights

Topics:

• Why Visualization Matters

• Using Matplotlib & Seaborn for plots

• Histograms, Boxplots, Heatmaps, Pairplots

• Plotly for Interactive Dashboards

• Storytelling with Data

Practice:

• Visualize correlations in a dataset using heatmaps

• Create a bar graph showing top-selling products

Topics:

• Descriptive & Inferential Statistics

• Probability Distributions (Normal, Binomial, Poisson)

• Hypothesis Testing (t-test, Chi-square, ANOVA)

• Correlation vs Causation

• Sampling, Confidence Intervals, and Z-scores

Practice:

• Perform hypothesis testing on a marketing dataset

• Calculate probability distributions using NumPy

Topics:

• Understanding Dataset Structure

• Detecting Outliers & Patterns

• Data Profiling & Feature Selection

• Univariate, Bivariate & Multivariate Analysis

• Summarizing Insights from Data

Practice:

• Perform EDA on Titanic or Iris dataset

• Detect outliers using boxplots

Project Title:
📊 “Retail Sales Analysis”

Objective:
Perform EDA on a retail sales dataset to uncover trends, customer patterns, and product performance.

Tasks:

• Clean & preprocess data

• Perform visualization with Seaborn/Plotly

• Present insights with summary report

Topics:

• What is Machine Learning?

• Types of ML: Supervised, Unsupervised, Reinforcement

• ML Workflow: Train → Test → Evaluate

• Introduction to Scikit-learn

• Feature Scaling, Normalization, and Train-Test Split

Practice:

• Load a dataset & split for training/testing

• Train your first Linear Regression model

Topics:

• Linear Regression, Logistic Regression

• Decision Trees, Random Forests

• K-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

• Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)

Practice:

• Predict house prices using Linear Regression

• Build a classification model for customer churn

Topics:

• Clustering: K-Means, Hierarchical

• Dimensionality Reduction: PCA

• Association Rules (Apriori)

• Anomaly Detection

Practice:

• Perform customer segmentation using K-Means

• Visualize clusters in 2D

Topics:

• Encoding Categorical Variables (Label, One-Hot)

• Handling Imbalanced Data

• Feature Scaling & Normalization

• Hyperparameter Tuning (GridSearchCV, RandomSearchCV)

• Cross-Validation Techniques

Practice:

• Tune Random Forest for optimal accuracy

• Handle categorical variables for model readiness

Topics:

• Introduction to Neural Networks

• Understanding Perceptrons & Activation Functions

• Building Models with TensorFlow & Keras

• CNN & RNN Overview

• Model Evaluation and Optimization

Practice:

• Build a simple Neural Network for digit recognition (MNIST)

Topics:

• Saving & Loading Models (Pickle, Joblib)

• Deploying Models using Flask / FastAPI

• Model Versioning & Monitoring Basics

• Introduction to Cloud Platforms (AWS, Azure, GCP)

• Continuous Integration/Continuous Deployment (CI/CD) for ML

Practice:

• Deploy a small model API using Flask

Project Title:
💼 “Customer Behavior Prediction System”

Objective:
Build a complete ML pipeline from raw data to model deployment.

Tasks:

• Data Cleaning & EDA

• Model Training & Optimization

• Model Deployment using Flask

• Create a Dashboard for insights presentation