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AI+ Data™ Spanish
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Introduction
Course Introduction
Lab Instructions
Audio Book: Introduction AI+ Data
Module 1: Foundations of Data Science
1.1 What is Data Science?
1.2 What Exactly is a 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 Applications of Data Science
1.10 Industry Case Studies: Demonstrating the Case Studies: Showcasing Data Science Impact
Module 1: Foundations of Data Science
Audio Book: Foundations of Data Science
Podcast: Foundations of Data Science
Activity: True False
Activity: Hotspot
Activity: Problem Statement
Activity: Knowledge checks
Quiz
Lab Practice 1.1
Python File Download
Module 2: Foundations of Statistics
2.1 Descriptive Statistics
2.2 Inferential Statistics
2.3 Probability Distributions
2.4 Central Limit Theorem
2.5 Hypothesis Testing
2.6 Confidence Intervals
Module 2: Foundations of Statistics
Audio Book: Foundations of Statistics
Podcast: Foundations of Statistics
Activity: Accordion
Activity: Case Study
Activity: Sequence
Activity: Knowledge checks
Quiz
Lab Practice 2.1
Python File Download
Module 3: Data Sources and Types
3.1 Structured Data
3.2 Unstructured Data
3.3 Semi structured Data
3.4 DataBases
3.5 Web Scraping
3.6 Applications Programming Interfaces(APIs)
3.7 Data Mining
3.8 Relational Databases, NoSQL Database
Module 3: Data Sources and Types
Audio Book: Data Sources and Types
Podcast: Data Sources and Types
Activity: Popup
Activity: True False
Activity: Drag and Drop
Activity: Knowledge checks
Quiz
Lab Practice 3.1
Python File Download
Module 4: Programming Skills for Data Science
4.1 Python Basics: Syntax, Data Types & Variables
4.2 Python Libraries for Data Science
4.3 Basics of R Programming
4.4 R Libraries for Data Science
Module 4: Programming Skills for Data Science
Audio Book: Programming Skills for Data Science
Podcast: Programming Skills for Data Science
Activity: Carousel/ Slideshow
Activity: Hands-On (Type 1)
Activity: Knowledge checks
Quiz
Lab Practice 4.1
Python File Download
Module 5: 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
5.5 Identifying and Handling Outliers
5.6 Data Transformation Techniques
Module 5: Data Wrangling and Preprocessing
Audio Book: Data Wrangling and Preprocessing
Podcast: Data Wrangling and Preprocessing
Activity: Sequence
Activity: Multiple Choice with image
Activity: Knowledge checks
Quiz
Lab Practice 5.1
Python File Download
Module 6: 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
6.5 Visualization: Histograms, Scatter & Box
6.6 Choosing the Right Visualization for Data
Module 6: Exploratory Data Analysis (EDA)
Audio Book: Exploratory Data Analysis (EDA)
Podcast: Exploratory Data Analysis (EDA)
Activity: Multiple Response
Activity: Tab Type 1
Activity: Knowledge checks
Quiz
Lab Practice 6.1
Python File Download
Module 7: Generative AI Tools for Deriving Insights
7.1 Overview of Generative AI Techniques
7.2 Synthesis, Augmentation & Anomaly Detection
Module 7: Generative AI Tools for Deriving Insights
Audio Book: Generative AI Tools for Deriving Insights
Podcast: Generative AI Tools for Deriving Insights
Activity: Scenario Activity
Activity: Knowledge checks
Quiz
Lab Practice 7.1
Python File Download
Module 8: 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
8.5 K – Means Clustering
8.6 Hierarchical Clustering
Module 8: Machine Learning Refresher
Audio Book: Machine Learning Refresher
Podcast: Machine Learning Refresher
Activity: Multiple choice with image
Activity: Problem Statement
Activity: Knowledge checks
Quiz
Module 9: 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
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
Module 9: Advance Machine Learning
Audio Book: Advance Machine Learning
Podcast: Advance Machine Learning
Activity: Drag and Drop
Activity: Tab type 2
Activity: Knowledge checks
Quiz
Module 10: 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
10.5 Apache Superset
10.6 Redash
10.7 Pentaho
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: Using Machine Learning Algorithms for Sales Forecasting and Customer Segmentation = Title = Building Predictive Models
10.12 Visualization and Interpretation: Creating Interactive Dashboards in Power BI to Derive Insights from Adidas Sales Data = Title = Visualization and Interpretation
10.13 Decision Making: Using Data-Driven Insights to Make Informed Business Decisions for Adidas = Title = Data-Driven Insights Shaping Adidas's Decisions
10.14 Case Study Discussion
Module 10: Data-Driven Decision-Making
Audio Book: Data-Driven Decision-Making
Podcast: Data-Driven Decision-Making
Activity: Hotspot
Activity: Accordion
Activity: Case Study
Activity: Knowledge checks
Quiz
Lab Practice 10.1
Python File Download
Module 11: Data Storytelling
11.1 Introduction to Data Storytelling
11.2 The Importance of Storytelling in Data Analysis and Communication (Title: The Role of Data Storytelling)
11.3 The Psychology of Storytelling: Why Stories Resonate with Audiences (Title: The Psychology Behind Storytelling)
11.4 Real-Life Examples of Successful Data Stories and Their Impact on Decision-Making (Title: Impactful Data Stories in Action)
11.5 Identifying Data-Driven Use Cases in Business
11.6 Understanding the Business Context: Goals, Challenges, and Opportunities (Title: Business Context: Goals and Challenges)
11.7 Selecting Relevant Data Sources and Metrics for the Use Case (Title: Choosing Relevant Data and Metrics)
11.8 Defining the Audience: Stakeholders, Decision Makers, and End Users (Title: Identifying Audience: Stakeholders and Users)
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, Connection, and Call to Action (Title: Engaging the Audience: Emotion and Action)
11.13 Choosing the Right Visualizations for the Story
11.14 Data Visualization Best Practices: Clarity, Simplicity, and Relevance (Title: Best Practices in Data Visualization)
11.15 Creating Engaging Visuals: Charts, Graphs, Maps, and Infographics (Title: Engaging Visuals: Charts, Maps & Infographics)
11.16 Using Interactive Elements to Enhance Understanding and Engagement (Title: Enhancing Engagement with Interactivity)
Module 11: Data Storytelling
Audio Book: Data Storytelling
Podcast: Data Storytelling
Activity: True/False
Activity: Drag and Drop
Activity: Carousel/ Slideshow
Activity: Multiple Response
Activity: Knowledge checks
Quiz
Module 12: Capstone Project - Employee Attrition Prediction
12.1 Data Collection and Preparation
12.2 Data Analysis and Modeling
12.3 Crafting a Compelling Nonerrative Around the Project Findings (Title: Crafting Compelling Project Narratives)
12.4 Presenting the Capstone Project to Peers and Stakeholders (Title: Presenting Capstone Projects to Stakeholders)
Module 12: Capstone Project - Employee Attrition Prediction
Audio Book: Capstone Project - Employee Attrition Prediction
Podcast: Capstone Project - Employee Attrition Prediction
Activity: Flip Card
Activity: Scenario Activity
Activity: Knowledge checks
Quiz
Lab Practice 12.1
Python File Download
Course Summary
Course Summary
AI Agents Data
1.1 What Are AI Agents?
1.2 Applications and Trends for AI Agents in Data
1.3 How Does an AI Agent Work?
1.4 Core Characteristics of AI Agents
1.5 Types of AI Agents
1.6 Case Study: Aible – AI Data Analytics Agent
Graded Assessment- 5 Questions
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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