AIDATAAHAI CertsAH-AIDATA1.0<ul> <li><strong>Advanced Data Analysis Techniques</strong><ul> <li>Learners will acquire skills in managing, preprocessing, and analyzing data using statistical methods and exploratory techniques to uncover insights and patterns.</li></ul></li><li><strong>Programming and Machine Learning Proficiency</strong><ul> <li>Students will develop strong programming skills necessary for data science, along with foundational and advanced machine learning techniques to build predictive models.</li></ul></li><li><strong>Application of Generative AI and Machine Learning</strong><ul> <li>Learners will learn to employ generative AI tools and machine learning algorithms to derive deeper insights from data, enhancing their analytical capabilities.</li></ul></li><li><strong>Data-Driven Decision Making and Storytelling</strong><ul> <li>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.</li></ul></li></ul><ul> <li>Basic knowledge of computer science and statistics (beneficial but not mandatory)</li><li>Keen interest in data analysis</li><li>Willingness to learn programming languages such as Python and R</li></ul><h4>Module 1: Foundations of Data Science</h4><ul> <li>1.1 Introduction to Data Science</li><li>1.2 Data Science Life Cycle</li><li>1.3 Applications of Data Science</li></ul><h4>Module 2: Foundations of Statistics</h4><ul> <li>2.1 Basic Concepts of Statistics</li><li>2.2 Probability Theory</li><li>2.3 Statistical Inference</li></ul><h4>Module 3: Data Sources and Types</h4><ul> <li>3.1 Types of Data</li><li>3.2 Data Sources</li><li>3.3 Data Storage Technologies</li></ul><h4>Module 4: Programming Skills for Data Science</h4><ul> <li>4.1 Introduction to Python for Data Science</li><li>4.2 Introduction to R for Data Science</li></ul><h4>Module 5: Data Wrangling an Preprocessing</h4><ul> <li>5.1 Data Imputation Techniques</li><li>5.2 Handling Outliers and Data Transformation</li></ul><h4>Module 6: Exploratory Data Analysis (EDA)</h4><ul> <li>6.1 Introduction to EDA</li><li>6.2 Data Visualization</li></ul><h4>Module 7: Generative AI Tools for Deriving Insights</h4><ul> <li>7.1 Introduction to Generative AI Tools</li><li>7.2 Applications of Generative AI</li></ul><h4>Module 8: Machine Learning</h4><ul> <li>8.1 Introduction to Supervised Learning Algorithms</li><li>8.2 Introduction to Unsupervised Learning</li><li>8.3 Different Algorithms for Clustering</li><li>8.4 Association Rule Learning with Implementation</li></ul><h4>Module 9: Advance Machine Learning</h4><ul> <li>9.1 Ensemble Learning Techniques</li><li>9.2 Dimensionality Reduction</li><li>9.3 Advanced Optimization Techniques</li></ul><h4>Module 10: Data-Driven Decision-Making</h4><ul> <li>10.1 Introduction to Data-Driven Decision Making</li><li>10.2 Open Source Tools for Data-Driven Decision Making</li><li>10.3 Deriving Data-Driven Insights from Sales Dataset</li></ul><h4>Module 11: Data Storytelling</h4><ul> <li>11.1 Understanding the Power of Data Storytelling</li><li>11.2 Identifying Use Cases and Business Relevance</li><li>11.3 Crafting Compelling Narratives</li><li>11.4 Visualizing Data for Impact</li></ul><h4>Module 12: Capstone Project - Employee Attrition Prediction</h4><ul> <li>12.1 Project Introduction and Problem Statement</li><li>12.2 Data Collection and Preparation</li><li>12.3 Data Analysis and Modeling</li><li>12.4 Data Storytelling and Presentation</li></ul>- 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.- 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 RModule 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 Presentation5 Tage3450.003450.003450.003995.003450.005515.003450.00