AI+ Data (AI+ Data)

• Acquire a strong foundation in data science fundamentals, including an overview of the field, its project lifecycle, and essential statistical principles.
• Develop proficiency in handling and processing data by learning about various data types, storage technologies, and methods for data wrangling, imputation, and transformation.
• Enhance analytical capabilities through exploratory data analysis using data visualization tools and techniques, and explore the use of Generative AI for deeper insights.
• Master machine learning by understanding and applying both supervised and unsupervised algorithms, and explore complex problem-solving through advanced techniques like clustering and ensemble learning.
• Apply data-driven decision-making and storytelling skills in real-world scenarios, learning to extract and communicate insights effectively, culminating in a capstone project focused on employee attrition prediction.

Required

• Basic knowledge of computer science and statistics (beneficial but not mandatory).

• Keen interest in data analysis.

• Willingness to learn programming languages such as Python and R.

Recommended

• AI+ Executive
• AI+ Everyone

• Database Analyst

• Data Analyst

• Database Technician

• Power BI Data Analyst

• IT Professional

• Data Engineer

Foundations of Data Science

• Introduction to Data Science
• Data Science Life Cycle
• Applications of Data Science

Foundations of Statistics

• Basic Concepts of Statistics
• Probability Theory
• Statistical Inference

Data Sources and Types

• Types of Data
• Data Sources
• Data Storage Technologies

Programming Skills for Data Science

• Introduction to Python for Data Science
• Introduction to R for Data Science

Data Wrangling and Preprocessing

• Data Imputation Techniques
• Handling Outliers and Data Transformation

Exploratory Data Analysis (EDA)

• Introduction to EDA
• Data Visualization

Generative AI Tools for Deriving Insights

• Introduction to Generative AI Tools
• Applications of Generative AI

Machine Learning

• Introduction to Supervised Learning Algorithms
• Introduction to Unsupervised Learning
• Different Algorithms for Clustering
• Association Rule Learning with Implementation

Advance Machine Learning

• Ensemble Learning Techniques
• Dimensionality Reduction
• Advanced Optimization Techniques

Data-Driven Decision-Making

• Introduction to Data-Driven Decision Making
• Open Source Tools for Data-Driven Decision Making
• Deriving Data-Driven Insights from Sales Dataset

Data Storytelling

• Understanding the Power of Data Storytelling
• Identifying Use Cases and Business Relevance
• Crafting Compelling Narratives
• Visualizing Data for Impact

Capstone Project - Employee Attrition Prediction

• Project Introduction and Problem Statement
• Data Collection and Preparation
• Data Analysis and Modeling
• Data Storytelling and Presentation

Optional Module: AI Agents for Data Analysis

• Understanding AI Agents
• Case Studies
• Hands-On Practice with AI Agents