• Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling

• Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytics

• Capstone Application: Solve real-world problems like employee attrition with AI

• Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship

Course Overview

1. Course Introduction Preview

Module 1: Foundations of Data Science

1. 1.1 Introduction to Data Science
2. 1.2 Data Science Life Cycle
3. 1.3 Applications of Data Science

Module 2: Foundations of Statistics

1. 2.1 Basic Concepts of Statistics
2. 2.2 Probability Theory
3. 2.3 Statistical Inference

Module 3: Data Sources and Types

1. 3.1 Types of Data
2. 3.2 Data Sources
3. 3.3 Data Storage Technologies

Module 4: Programming Skills for Data Science

1. 4.1 Introduction to Python for Data Science
2. 4.2 Introduction to R for Data Science

Module 5: Data Wrangling and Preprocessing

1. 5.1 Data Imputation Techniques
2. 5.2 Handling Outliers and Data Transformation

Module 6: Exploratory Data Analysis (EDA)

1. 6.1 Introduction to EDA
2. 6.2 Data Visualization

Module 7: Generative AI Tools for Deriving Insights

1. 7.1 Introduction to Generative AI Tools
2. 7.2 Applications of Generative AI

Module 8: Machine Learning

1. 8.1 Introduction to Supervised Learning Algorithms
2. 8.2 Introduction to Unsupervised Learning
3. 8.3 Different Algorithms for Clustering
4. 8.4 Association Rule Learning with Implementation

Module 9: Advance Machine Learning

1. 9.1 Ensemble Learning Techniques
2. 9.2 Dimensionality Reduction
3. 9.3 Advanced Optimization Techniques

Module 10: Data-Driven Decision-Making

1. 10.1 Introduction to Data-Driven Decision Making
2. 10.2 Open Source Tools for Data-Driven Decision Making
3. 10.3 Deriving Data-Driven Insights from Sales Dataset

Module 11: Data Storytelling

1. 11.1 Understanding the Power of Data Storytelling
2. 11.2 Identifying Use Cases and Business Relevance
3. 11.3 Crafting Compelling Narratives
4. 11.4 Visualizing Data for Impact

Module 12: Capstone Project - Employee Attrition Prediction

1. 12.1 Project Introduction and Problem Statement
2. 12.2 Data Collection and Preparation
3. 12.3 Data Analysis and Modeling
4. 12.4 Data Storytelling and Presentation

Optional Module: AI Agents for Data Analysis

1. 1. Understanding AI Agents
2. 2. Case Studies
3. 3. Hands-On Practice with AI Agents

AI Data Scientist

Analyzes complex data to extract insights, builds predictive models, employs statistical methods, and communicates findings to influence decision-making.

AI Machine Learning Engineer

Designs and develops machine learning systems, implements algorithms, optimizes data pipelines, and integrates models into scalable, production-ready applications.

AI Engineer

Develops artificial intelligence solutions, programs neural networks, optimizes AI algorithms, ensures ethical AI deployment, and troubleshoots AI systems.

AI Data Analyst

Interprets data, generates reports, identifies trends, supports business decisions with actionable insights, and utilizes visualization tools to present data.