Introduction to Python and Anaconda environment

Data types, variables, control structures (if, for, while)

Functions and Lambda expressions

Working with lists, tuples, dictionaries

Reading and writing files

Introduction to NumPy:

• Arrays, array operations, indexing

Introduction to Pandas:

• Series and DataFrame objects

• Reading data from CSV, Excel, SQL

• Data selection, filtering, sorting

• Handling missing values and duplicates

• GroupBy operations

• Merging and joining datasets

Exploratory Data Analysis (EDA)

Data visualization with:

• Matplotlib: Line plots, bar charts, histograms

• Seaborn: Pairplots, heatmaps, boxplots

Automating Excel/CSV reports with Python

Overview of Machine Learning in Business

Supervised vs Unsupervised Learning

Linear Regression:

• Model creation, evaluation (R-squared, MAE, RMSE)

Logistic Regression:

• Probability interpretation, ROC Curve, AUC

Decision Trees and Random Forests

K-Means Clustering

Model evaluation metrics:

• Confusion Matrix, Accuracy, Precision, Recall, F1 Score

Overfitting and Underfitting

Train-test split and Cross-validation

Feature selection and engineering basics

Business case applications:

• Customer churn prediction

• Sales forecasting

• Market segmentation

Choosing a real-world capstone project (Retail, Finance, HR, Marketing, etc.)

Defining the business problem statement and KPIs

Data collection and preparation

Exploratory Data Analysis and visualization

Building dashboards and simple predictive models

Final project presentation with business insights

Peer and mentor reviews

Resume building tailored to Business Analyst roles

Interview preparation: