The AI+ Data™ certification provides professionals with cutting-edge skills in Data Science and Artificial Intelligence (AI). Covering key concepts like Data Science Foundations, Statistics, Python Programming, and Data Wrangling, participants gain practical knowledge to excel in a data-driven world. Advanced topics such as Generative AI, Machine Learning, and Predictive Analytics prepare learners for solving complex challenges. This program includes a capstone project on Employee Attrition Prediction, emphasizing Data-Driven Decision-Making and Compelling Data Storytelling for actionable business insights. With personalized mentorship, hands-on projects, and immersive resources, learners are equipped for success in AI and Data Science careers.
Advanced Data Analysis Techniques: Learn to manage, preprocess, and analyze data using statistical methods and exploratory techniques to uncover insights.
Generative AI and Machine Learning: Employ advanced AI tools and machine learning algorithms for deriving deeper insights and developing predictive models.
Programming and Machine Learning Proficiency: Build strong programming skills in Python and R, applying them to foundational and advanced machine learning techniques.
Data Storytelling and Decision-Making: Master the art of presenting data effectively and making informed, data-driven business decisions.
Data Analysts and Scientists: Deepen your understanding of advanced data science techniques and data-driven decision-making.
Business Analysts and Managers: Leverage data for strategic advantage to gain insights and drive informed decision-making.
IT Professionals and Software Developers: Enhance your skill set with practical knowledge in programming with Python and R.
Academics and Researchers: Explore the intersection of AI and data science to contribute to academic research and develop innovative solutions.
Entrepreneurs and Startup Founders: Use data for business growth and optimize operations, improve products, and make data-informed strategicdecisions
Course Introduction
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