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AI+ Data™

Mastering AI, Maximizing Data: Your Path to Innovation
Artikelnummer voorraad referentie: AT-120
Verkoper: Train It Now
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Event kalender
€3450,00
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Course Overview
  1. Course Introduction Preview
  2. 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 and 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
    Optional Module: AI Agents for Data Analysis
    • 1. Understanding AI Agents
    • 2. Case Studies
    • 3. Hands-On Practice with AI Agents
    Tools you will explore
    • Google Colab
    • MLflow
    • Alteryx
    • KNIME
Course Overview
  1. Course Introduction Preview
  2. 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 and 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
    Optional Module: AI Agents for Data Analysis
    • 1. Understanding AI Agents
    • 2. Case Studies
    • 3. Hands-On Practice with AI Agents
    Tools you will explore
    • Google Colab
    • MLflow
    • Alteryx
    • KNIME