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Module 1: Foundations of Data Science
1.0 Introduction: Foundations of Data Science
1.1 What is Data Science?
1.2 Role of Data Scientist.
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
1.9 Introduction: Applications of Data Science
1.10 Applications of Data Science
1.11 Industry Case Studies Demonstrating the Significance of Data Science
1.12 Summary: Foundations of Data Science
Module 1: E-Book
Quiz
Lab Practice 1.1
Python File Download
Module 2: Foundations of Statistics
2.0 Introduction: Foundations of Statistics
2.1 Introduction: Basic Concepts of Statistics
2.2 Descriptive Statistics
2.3 Inferential Statistics
2.4 Probability Distributions
2.5 Central Limit Theorem
2.6 Hypothesis Testing
2.7 Confidence Intervals
2.8 Summary: Foundations of Statistics
Module 2: E-Book
Quiz
Lab Practice 2.1
Python File Download
Module 3: Data Sources and Types
3.0 Introduction: Data Sources and Types
3.1 Introduction: Types of Data
3.2 Structured Data
3.3 Unstructured Data
3.4 Semi structured Data
3.5 DataBases
3.6 Webscraping
3.7 Applications Programming Interfaces(APIs)
3.8 Overview of Data Sources
3.9 Data Mining
3.10 Relational Databases (SQL), NoSQL Databases (MongoDB)
3.11 MongoDB
3.12 Hands-on Exercise: Querying Structured Data and Handling Semi-Structured Data
3.13 Summary: Data Sources and Types
Module 03: E-Book
Quiz
Lab Practice 3.1
Python File Download
Module 4: Programming Skills for Data Science
4.0 Introduction: Programming Skills for Data Science
4.1 Introduction to Python for Data Science
4.2 Basics of Python Programming: Syntax, Data Types, Variables
4.3 Python Libraries for Data Science
4.4 Introduction to R for Data Science
4.5 Basics of R Programming
4.6 R Libraries for Data Science
4.7 Hands-on Exercise: Data Manipulation and Visualization with Python and R Libraries
4.8 Summary: Programming Skills for Data Science
Module 4: E-Book
Quiz
Lab Practice 4.1
Python File Download
Module 5: Data Wrangling and Preprocessing
5.0 Introduction: Data Wrangling and Preprocessing
5.1 Introduction to Data Imputation Techniques
5.2 Overview of Data Imputation
5.3 Multiple Imputation
5.4 Overview of Missing Data
5.5 Deletion
5.6 Overview of Imputation
5.7 Common Imputation Techniques
5.8 Introduction: Handling Outliers and Data Transformation
5.9 Identifying and Handling Outliers
5.10 Data Transformation Techniques
5.11 Hands-on Exercise
5.12 Summary: Data Wrangling and Preprocessing
Module 5: E-Book
Quiz
Lab Practice 5.1
Python File Download
Module 6: Exploratory Data Analysis (EDA)
6.0 Introduction: Exploratory Data Analysis (EDA)
6.1 Introduction to EDA
6.2 Data Overview
6.3 Steps involved in Exploratory Data Analysis
6.4 Purpose and Goals of Exploratory Data Analysis
6.5 Common Techniques: Summary Statistics, Data Visualization
6.6 Data Distribution Types
6.7 Introduction: Data Visualization
6.8 Types of Visualizations: Histograms, Scatter Plots, Box Plots
6.9 Choosing the Right Visualization for Different Types of Data
6.10 Summary: Exploratory Data Analysis (EDA)
Module 6: E-Book
Quiz
Lab Practice 6.1
Python File Download
Module 07: Generative AI Tools for Deriving Insights
7.0 Introduction: Generative AI Tools for Deriving Insights
7.1 Introduction to Generative AI Tools
7.2 Overview of Generative AI Techniques
7.3 Hands-on Exercise for Various Gen AI Tools
7.4 Applications of Generative AI
7.5 Application in Data Synthesis, Augmentation, and Anomaly Detection
7.6 Summary: Generative AI Tools for Deriving Insights
Module 07: E-Book
Quiz
Lab Practice 7.1
Python File Download
Module 8: Machine Learning Refresher
8.0 Introduction: Machine Learning Refresher
8.1 Introduction to Supervised Learning Algorithms
8.2 Simple, Multiple, and Polynomial Regression
8.3 Logistic Regression
8.4 K-Nearest Neighbors (KNN)
8.5 Decision Tree
8.6 Support Vector Machine (SVM)
8.7 Naive Bayes Classification
8.8 Introduction to Unsupervised Learning
8.9 Types of Unsupervised Learning
8.10 Introduction: Different Algorithms for Clustering
8.11 K – Means Clustering
8.12 Hierarchical Clustering
8.13 Introduction: Association Rule Learning
8.14 Association Rule Learning
8.15 Hands On
8.16 Summary: Machine Learning Refresher
Module 8: E-Book
Quiz
Module 9: Machine Learning
9.0 Introduction: Advanced Machine Learning
9.1 Ensemble Learning Techniques
9.2 Overview of Ensemble Learning
9.3 Bagging (Bootstrap Aggregating)
9.4 Random Forest with Code and Real-Life Example
9.5 Boosting (AdaBoost, Gradient Boosting, XGBoost)
9.6 XGBoost with Code and Real-Life example
9.7 Stacking
9.8 Ensemble Learning Applications and Case Studies
9.9 Dimensionality Reduction
9.10 Introduction to Advanced Optimization
9.11 Principal Component Analysis (PCA)
9.12 Linear Discriminant Analysis (LDA)
9.13 t-Distributed Stochastic Neighbor Embedding (t-SNE)
9.14 Autoencoders for Dimensionality Reduction
9.15 Summary: Advanced Machine Learning
Quiz
Module 10: Data-Driven Decision-Making
10.0 Introduction: Data-Driven Decision-Making
10.1 Introduction to Data-Driven Decision Making
10.2 Understanding the Importance of Data-Driven Decision Making
10.3 Overview of the Decision-Making Process
10.4 Role of Data in Decision Making
10.5 Benefits and Challenges of Data-Driven Decision Making
10.6 Open Source Tools for Data-Driven Decision Making
10.7 Apache Superset
10.8 Redash
10.9 Pentaho
10.10 Deriving Data-Driven Insights from Sales Dataset
10.11 Introduction to Case Study
10.12 Data Collection and Preparation: Understanding the Adidas Sales Dataset
10.13 Exploratory Data Analysis (EDA): Identifying Key Patterns and Trends in Adidas Sales
10.14 Building Predictive Models: Using Machine Learning Algorithms for Sales Forecasting and Customer Segmentation
10.15 Visualization and Interpretation: Creating Interactive Dashboards in Power BI to Derive Insights from Adidas Sales Data
10.16 Decision Making: Using Data-Driven Insights to Make Informed Business Decisions for Adidas
10.17 Case Study Discussion
10.18 Summary: Data-Driven Decision-Making
Quiz
Lab Practice 10.1
Python File Download
Module 11: Data Storytelling
11.0 Introduction: Data Storytelling
11.1 Understanding the Power of Data Storytelling
11.2 Introduction to Data Storytelling
11.3 The Importance of Storytelling in Data Analysis and Communication
11.4 The Psychology of Storytelling: Why Stories Resonate with Audiences
11.5 Real-Life Examples of Successful Data Stories and Their Impact on Decision-Making
11.6 Identifying Use Cases and Business Relevance
11.7 Identifying Data-Driven Use Cases in Business
11.8 Understanding the Business Context: Goals, Challenges, and Opportunities
11.9 Selecting Relevant Data Sources and Metrics for the Use Case
11.10 Defining the Audience: Stakeholders, Decision Makers, and End Users
11.11 Crafting Compelling Narratives
11.12 Structuring a Data Story: Beginning, Middle, End
11.13 Developing a Clear Message and Storyline
11.14 Incorporating Data Insights into the Narrative Flow
11.15 Engaging the Audience: Emotion, Connection, and Call to Action
11.16 Visualizing Data for Impact
11.17 Choosing the Right Visualizations for the Story
11.18 Data Visualization Best Practices: Clarity, Simplicity, and Relevance
11.19 Creating Engaging Visuals: Charts, Graphs, Maps, and Infographics
11.20 Using Interactive Elements to Enhance Understanding and Engagement
11.21 Summary: Data Storytelling
Quiz
Module 12: Capstone Project - Employee Attrition Prediction
12.0 Introduction: Capstone Project - Employee Attrition Prediction
12.1 Project Introduction and Problem Statement
12.2 Introduction to the Capstone Project: Employee Attrition Prediction
12.3 Identifying Data Sources: HR Records, Employee Surveys, Performance Metrics
12.4 Data Collection and Preparation
12.5 Collecting and Acquiring Relevant Data
12.6 Data Analysis and Modeling
12.7 Introduction to Data Analysis and Modeling
12.8 Choosing Suitable Data Analysis and Modeling Techniques
12.9 Evaluating Model Performance
12.10 Introduction: Data Storytelling and Presentation
12.11 Crafting a Compelling Narrative Around the Project Findings
12.12 Visualizing Key Insights Using Data Storytelling Techniques
12.13 Presenting the Capstone Project to Peers and Stakeholders
12.14 Summary: Capstone Project - Employee Attrition Prediction
Quiz
Lab Practice 12.1
Python File Download
Resources
AI+ Data Tools
AI+ Data Blueprint
AI+ Data Detailed Curriculum
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
