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AI+ Data™ Self-Paced Learning V3_2025
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Introduction
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Course Introduction
Lab Instructions
Audio Book: Introduction AI+ Data
Module 1: Foundations of Data Science
Module 1: Foundations of Data Science
Audio Book: Foundations of Data Science
Podcast: Foundations of Data Science
1.1 What is Data Science? Importance of Data Science
1.2 What Exactly is a Data Scientist?
Activity: True False
1.3 Understanding the Business Problem
1.4 Data Preparation
1.5 Exploratory Data Analysis (EDA)
1.6 Modeling the Data
1.7 Evaluating the Model
1.8 Deploying the Model
Activity: Hotpsot
1.9 Applications of Data Science
1.10 Case Studies: Showcasing Data Science Impact
Activity: Problem Statement
Activity: Knowledge checks
Quiz
Lab Practice 1.1
Python File Download
Module 2: Foundations of Statistics
Module 2: Foundations of Statistics
Audio Book: Foundations of Statistics
Podcast: Foundations of Statistics
2.1 Descriptive Statistics
2.2 Inferential Statistics
Activity: Accordion
2.3 Probability Distributions
2.4 Central Limit Theorem
Activity: Case Study
2.5 Hypothesis Testing
2.6 Confidence Intervals
Activity: Sequence
Activity: Knowledge checks
Quiz
Lab Practice 2.1
Python File Download
Module 3: Data Sources and Types
Module 3: Data Sources and Types
Audio Book: Data Sources and Types
Podcast: Data Sources and Types
3.1 Structured Data
3.2 Unstructured Data
3.3 Semi structured Data
Activity: Popup
3.4 DataBases
3.5 Web Scraping
Activity: True False
3.6 Applications Programming Interfaces(APIs)
3.7 Data Mining
3.8 Relational Databases, NoSQL Database
Activity: Drag and Drop
Activity: Knowledge checks
Quiz
Lab Practice 3.1
Python File Download
Module 4: Programming Skills for Data Science
Module 4: Programming Skills for Data Science
Audio Book: Programming Skills for Data Science
Podcast: Programming Skills for Data Science
4.1 Python Basics: Syntax, Data Types & Variables
4.2 Python Libraries for Data Science
Activity: Carousel/ Slideshow
4.3 Basics of R Programming
4.4 R Libraries for Data Science
Activity: Hands-On (Type 1)
Activity: Knowledge checks
Quiz
Lab Practice 4.1
Python File Download
Module 5: Data Wrangling and Preprocessing
Module 5: Data Wrangling and Preprocessing
Audio Book: Data Wrangling and Preprocessing
Podcast: Data Wrangling and Preprocessing
5.1 Overview of Data Imputation
5.2 Overview of Missing Data
5.3 Overview of Imputation
5.4 Common Imputation Techniques
Activity: Sequence
5.5 Identifying and Handling Outliers
5.6 Data Transformation Techniques
Activity: Multiple Choice with image
Activity: Knowledge checks
Quiz
Lab Practice 5.1
Python File Download
Module 6: Exploratory Data Analysis (EDA)
Module 6: Exploratory Data Analysis (EDA)
Audio Book: Exploratory Data Analysis (EDA)
Podcast: Exploratory Data Analysis (EDA)
6.1 Exploratory Data Analysis (EDA)
6.2 Steps involved in Exploratory Data Analysis?
6.3 Purpose and Goals of Exploratory Data Analysis
6.4 Key Techniques: Summary Stats & Visualization
Activity: Multiple Response
6.5 Visualization: Histograms, Scatter & Box
6.6 Choosing the Right Visualization for Data
Activity: Tab Type 1
Activity: Knowledge checks
Quiz
Lab Practice 6.1
Python File Download
Module 7: Generative AI Tools for Deriving Insights
Module 7: Generative AI Tools for Deriving Insights
Audio Book: Generative AI Tools for Deriving Insights
Podcast: Generative AI Tools for Deriving Insights
7.1 Overview of Generative AI Techniques
7.2 Synthesis, Augmentation & Anomaly Detection
Activity: Scenario Activity
Activity: Knowledge checks
Quiz
Lab Practice 7.1
Python File Download
Module 8: Machine Learning Refresher
Module 8: Machine Learning Refresher
Audio Book: Machine Learning Refresher
Podcast: Machine Learning Refresher
8.1 Simple, Multiple, and Polynomial Regression
8.2 Logistic Regression
8.3 K-Nearest Neighbors (KNN)
8.4 Types of Unsupervised Learning
Activity: Multiple choice with image
8.5 K – Means Clustering
8.6 Hierarchical Clustering
Activity: Problem Statement
Activity: Knowledge checks
Quiz
Module 9: Advanced Machine Learning
Module 9: Machine Learning
Audio Book: Machine Learning
Podcast: Advanced Machine Learning
9.1 Overview of Ensemble Learning
9.2 Bagging (Bootstrap Aggregating)
9.3 Random Forest with Code and Real-Life Example
9.4 Boosting (AdaBoost, Gradient Boosting, XGBoost)
9.5 XGBoost with Code and Real-Life example
9.6 Stacking
9.7 Ensemble Learning Applications and Case Studies
Activity: Drag and Drop
9.8 Dimensionality Reduction
9.9 Principal Component Analysis (PCA)
9.10 Linear Discriminant Analysis (LDA)
9.11 t-Distributed Stochastic Neighbor Embedding (t-SNE)
9.12 Autoencoders for Dimensionality Reduction
Activity: Tab type 2
Activity: Knowledge checks
Quiz
Module 10: Data-Driven Decision-Making
Module 10: Data-Driven Decision-Making
Audio Book: Data-Driven Decision-Making
Podcast: Data-Driven Decision-Making
10.1 Understanding the Importance of Data-Driven Decision Making
10.2 Overview of the Decision-Making Process
10.3 Role of Data in Decision Making
10.4 Benefits and Challenges of Data-Driven Decision Making
Activity: Hotspot
10.5 Apache Superset
10.6 Redash
10.7 Pentaho
Activity: Accordion
10.8 Introduction to Case Study
10.9 Data Collection and Preparation: Understanding the Adidas Sales Dataset
10.10 Exploratory Data Analysis (EDA): Identifying Key Patterns and Trends in Adidas Sales
10.11 Building Predictive Models
10.12 Visualization and Interpretation
10.13 Data-Driven Insights Shaping Adidas's Decisions
10.14 Case Study Discussion
Activity: Case Study
Activity: Knowledge checks
Quiz
Lab Practice 10.1
Python File Download
Module 11: Data Storytelling
Module 11: Data Storytelling
Audio Book: Data Storytelling
Podcast: Data Storytelling
11.1 Introduction to Data Storytelling
11.2 The Role of Data Storytelling
11.3 The Psychology Behind Storytelling
11.4 Impactful Data Stories in Action
Activity: True/False
11.5 Identifying Data-Driven Use Cases in Business
11.6 Business Context: Goals and Challenges
11.7 Choosing Relevant Data and Metrics
11.8 Identifying Audience: Stakeholders and Users
Activity: Drag and Drop
11.9 Structuring a Data Story: Beginning, Middle, End
11.10 Developing a Clear Message and Storyline
11.11 Incorporating Data Insights into the Narrative Flow
11.12 Engaging the Audience: Emotion and Action
Activity: Carousel/ Slideshow
11.13 Choosing the Right Visualizations for the Story
11.14 Best Practices in Data Visualization
11.15 Engaging Visuals: Charts, Maps & Infographics
11.16 Enhancing Engagement with Interactivity
Activity: Multiple Response
Activity: Knowledge checks
Quiz
Module 12: Capstone Project - Employee Attrition Prediction
Module 12: Capstone Project - Employee Attrition Prediction
Audio Book: Capstone Project - Employee Attrition Prediction
Podcast: Capstone Project - Employee Attrition Prediction
12.1 Data Collection and Preparation
12.2 Data Analysis and Modeling
Activity: Flip Card
12.3 Crafting Compelling Project Narratives
12.4 Presenting Capstone Projects to Stakeholders
Activity: Scenario Activity
Activity: Knowledge checks
Quiz
Lab Practice 12.1
Python File Download
Additional Module - AI Agents For Data
AI+ Agents Data
Resources
AI+ Data Blueprint
AI+ Data Detailed Curriculum
AI+ Data Tools
AI+ Data Resources and References
AI CERTs Exam Guidelines
AI CERTs Exam Guidelines
AI+ Data Examination
AI+ Data Examination
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AI+ Data™ Self-Paced Learning V3_2025
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