Course Overview
The AI+ Data certification equips professionals with vital skills for data science. It covers key concepts like Data Science Foundations, Statistics, Programming, and Data Wrangling. Participants delve into advanced topics such as Generative AI and Machine Learning, preparing them for complex data challenges. The program includes a hands-on capstone project focusing on Employee Attrition Prediction. Emphasis is placed on Data-Driven Decision-Making and Data Storytelling for actionable insights. Personalized mentorship, immersive projects, and cutting-edge resources ensure a transformative learning journey, preparing individuals for success in AI and data science.
Prerequisites
- 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
Course Objectives
- Advanced Data Analysis Techniques
- Learners will acquire skills in managing, preprocessing, and analyzing data using statistical methods and exploratory techniques to uncover insights and patterns.
- Programming and Machine Learning Proficiency
- Students will develop strong programming skills necessary for data science, along with foundational and advanced machine learning techniques to build predictive models.
- Application of Generative AI and Machine Learning
- Learners will learn to employ generative AI tools and machine learning algorithms to derive deeper insights from data, enhancing their analytical capabilities.
- Data-Driven Decision Making and Storytelling
- Students who goes through this course will get the ability to make informed decisions based on data analysis and effectively communicate findings through compelling data storytelling.
Outline: AI+ Data (AIDATA)
Module 1: Foundations of Data Science
- 1.1 Introduction to Data Science
- 1.2 Data Science Life Cycle
- 1.3 Applications of Data Science
Module 2: Foundations of Statistics
- 2.1 Basic Concepts of Statistics
- 2.2 Probability Theory
- 2.3 Statistical Inference
Module 3: Data Sources and Types
- 3.1 Types of Data
- 3.2 Data Sources
- 3.3 Data Storage Technologies
Module 4: Programming Skills for Data Science
- 4.1 Introduction to Python for Data Science
- 4.2 Introduction to R for Data Science
Module 5: Data Wrangling an Preprocessing
- 5.1 Data Imputation Techniques
- 5.2 Handling Outliers and Data Transformation
Module 6: Exploratory Data Analysis (EDA)
- 6.1 Introduction to EDA
- 6.2 Data Visualization
Module 7: Generative AI Tools for Deriving Insights
- 7.1 Introduction to Generative AI Tools
- 7.2 Applications of Generative AI
Module 8: Machine Learning
- 8.1 Introduction to Supervised Learning Algorithms
- 8.2 Introduction to Unsupervised Learning
- 8.3 Different Algorithms for Clustering
- 8.4 Association Rule Learning with Implementation
Module 9: Advance Machine Learning
- 9.1 Ensemble Learning Techniques
- 9.2 Dimensionality Reduction
- 9.3 Advanced Optimization Techniques
Module 10: Data-Driven Decision-Making
- 10.1 Introduction to Data-Driven Decision Making
- 10.2 Open Source Tools for Data-Driven Decision Making
- 10.3 Deriving Data-Driven Insights from Sales Dataset
Module 11: Data Storytelling
- 11.1 Understanding the Power of Data Storytelling
- 11.2 Identifying Use Cases and Business Relevance
- 11.3 Crafting Compelling Narratives
- 11.4 Visualizing Data for Impact
Module 12: Capstone Project - Employee Attrition Prediction
- 12.1 Project Introduction and Problem Statement
- 12.2 Data Collection and Preparation
- 12.3 Data Analysis and Modeling
- 12.4 Data Storytelling and Presentation