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.
Certification Duration: 40 hours (5 Days)
Prerequisites
- 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 R.
Exam Details
- Modules – 12
- Examination – 1
- 50 MCQs - 90 Minutes
- Passing Score – 70%
Certification Modules
- 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 Preprocessing
- Module 6: Exploratory Data Analysis (EDA)
- Module 7: Generative AI Tools for Deriving Insights
- Module 8: Machine Learning
- Module 9: Advance Machine Learning
- Module 10: Data-Driven Decision-Making
- Module 11: Data Storytelling
- Module 12: Capstone Project - Employee Attrition Prediction
Tools
- Google Colab
- MLflow
- Alteryx
- KNIME
Exam Objectives
- 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.