AT-120
AI+ Data
.png){kind=small}
Price:
Duration:
Please Call
5 Days
Prerequisites
• Basic knowledge of computer science and statistics (beneficial but not mandatory) Keen interest in data analysis
Keen interest in data analysis
• Willingness to learn programming languages such as Python and R
What you’ll learn in this course
The AI+ Data certification equips professionals with vital skills for data science. It covers key concepts like Data Science Foundations, Statistics, Programming, and Data Wrangling. Participants delve into advanced topics such as Generative AI and Machine Learning, preparing them for complex data challenges.
The program includes a hands-on capstone project focusing on Employee Attrition Prediction. Emphasis is placed on Data-Driven Decision-Making and Data Storytelling for actionable insights. Personalized mentorship, immersive projects, and cutting-edge resources ensure a transformative learning journey, preparing individuals for success in AI and data science.
Course Objectives
• Gain a comprehensive understanding of data science, including foundational concepts, data preparation, exploratory data analysis, and advanced topics such as Generative AI and ML algorithms: this certification ensures proficiency in both fundamental and cutting-edge techniques, enabling professionals to tackle complex data challenges effectively.
• Acquire hands-on skills in Python and R programming for data science, focusing on data manipulation, visualization, and advanced ML applications: learners will be equipped to handle data preprocessing, cleaning, and transformation using essential libraries and tools, enhancing their ability to perform robust data analysis.
• Learn to implement and evaluate a range of ML algorithms, from basic regression to advanced ensemble methods and dimensionality reduction techniques: the program includes practical exercises and real-world case studies, such as predicting employee attrition, to develop and refine predictive models and derive actionable insights.
• Explore strategies for effective data-driven decision-making and storytelling, including the creation of interactive dashboards and visualizations: the certification emphasizes translating data insights into compelling narratives and actionable business strategies, preparing professionals to communicate findings and drive organizational success through data-driven approaches.
Course Outline
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 Refresher
• 8.1 Introduction to Supervised Learning Algorithms
• 8.2 Introduction to Unsupervised Learning
• 8.3 Different Algorithms for Clustering
• 8.4 Association Rule Learning
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
Further information
If you would like to know more about this course please contact us
.png){kind=small}