AI for Data Practitioner

The AI for Data Practitioner Course offers a transformative learning experience, equipping professionals with essential data science skills. The program covers foundational concepts such as statistics, programming, and data wrangling, and advances to generative Artificial Intelligence (AI) and Machine Learning (ML) modules. A key feature is the focus on data-driven decision-making and storytelling, which enhances both analytical and communication skills.

The hands-on capstone project and personalized mentorship provide practical experience, applying data science techniques to real-world challenges. By integrating theoretical knowledge with practical exercises in Python, R, and advanced technologies, this course prepares professionals to excel in the dynamic field of data science, driving innovation and strategic decision-making in their organizations.

The following aspects are covered in the course:

• Foundations of Data Science
• Foundations of Statistics
• Data Sources and Types
• Programming Skills for Data Science
• Data Wrangling and Pre-processing
• Exploratory Data Analysis (EDA)
• Generative AI Tools for Deriving Insights
• Machine Learning Refresher
• Advance Machine Learning
• Data-Driven Decision-Making
• Data Storytelling
• Capstone Project – Employee Attrition Prediction

Prerequisites

• Domain Awareness: Basic knowledge of computer science and statistics.
• Curiosity about Data Analysis: Keen interest in the tools and processes of data analysis.
• Learner Mindset: Willingness to learn programming languages such as Python and R.

Who Should Attend?

• Data Analysts and Scientists
• Business Analysts and Managers
• IT Professionals and Software Developers
• Academics and Researchers
• Entrepreneurs and Start-up Founders

Course Objectives

By the end of this course, you will be able to:

• 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 course 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 pre-processing, 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 course 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
• Module 2: Foundations of Statistics
• Module 3: Data Sources and Types
• Module 4: Programming Skills for Data Science
• Module 5: Data Wrangling and Pre-processing
• Module 6: Exploratory Data Analysis (EDA)
• Module 7: Generative AI Tools for Deriving Insights
• Module 8: Machine Learning Refresher
• Module 9: Advanced Machine Learning
• Module 10: Data-Driven Decision-Making
• Module 11: Data Storytelling
• Module 12: Capstone Project: Employee Attrition Prediction