Introduction to Data Science & Python Programming

• What is Data Science? Career Paths and Applications

• Setting up Python (Anaconda, Jupyter Notebooks)

• Basic Python: Variables, Data Types, Control Structures

• Functions and Libraries Overview

Outcome: Write Python scripts and understand the basics of data science.

Data Handling with Pandas & NumPy

• Data Frames and Series in Pandas

• Importing, Cleaning, and Exporting Data

• Indexing, Filtering, and Aggregation

• Handling Missing Values and Data Types

 Outcome: Perform effective data wrangling using Pandas and NumPy.

 Data Visualization & Storytelling

• Matplotlib Basics: Plots, Charts, and Graphs

• Seaborn for Advanced Visualization

• Customizing Graphs for Clarity

• Data Storytelling Techniques

Outcome: Create compelling visualizations and present insights effectively.

 Statistics & Exploratory Data Analysis

• Descriptive Statistics and Probability Concepts
• Distribution Analysis and Correlation
• Hypothesis Testing (t-test, Chi-square)
• EDA Best Practices

Outcome: Conduct statistical analyses and derive actionable insights from dataset

Intro to Machine Learning + Capstone Project

• Overview of Machine Learning Types (Supervised vs Unsupervised)

• Model Building with scikit-learn (Linear Regression, KNN)

• Model Evaluation Metrics

• Capstone Project: Analyse and model a dataset (e.g., sales, healthcare, finance)

Outcome: Build and evaluate basic predictive models.

HR & Career Skills Week

• Professional Communication & Email Etiquette
• Interview Prep & Mock Interviews
• LinkedIn & Resume Optimization
• Workplace Ethics & Time Management
• HR Session: Understanding Roles in Tech Teams

Outcome: Build soft skills and career readiness for entry-level Salesforce roles.