Build In-Demand Data Science and AI Skills

Foundations of Data Science and AI 

Begin your learning journey with foundational concepts in data, Python programming, and Generative AI. This is a pre module to prepare you for the advanced modules on Data Science and AI, reinforcing essential mathematical and statistical principles needed for the weeks ahead. 

• Introduction to the World of Data 
• Introduction to Python 
• Introduction to Generative AI 
• Applications of Data Science and AI 
• Data Science Lifecycle 
• Mathematics and Statistics behind Data Science and AI 
• History of Data Science and AI

In this module, you will:

• Understand the end-to-end lifecycle of an AI application
• Analyze real-world case studies to explore business impact
• Learn how data-driven decisions are made in different industries
• Explore how AI enables innovation, efficiency, and value creation
• Prepare for hands-on learning with a strategic view of AI’s role in business

This module is focussed on building your foundations of AI, you will learn: 

Python for Data Science 

• NumPy 
• Pandas 
• Data Visualization 

Stats for Data Science 

• Descriptive Statistics 
• Inferential Statistics

In this Generative AI masterclass taken by experts, you will explore the use cases of Generative AI. Learn practical techniques to integrate GenAI into your data workflows.

Supervised & Unsupervised Learning 

• Understand the fundamental differences between supervised and unsupervised learning. 
• Learn the key concepts of classification and clustering techniques 
• Identify suitable methods based on the nature of the data and the problem context 

Dimensionality Reduction Techniques 

•  Master Principal Component Analysis (PCA) for simplifying high-dimensional data 
•  Explore t-SNE for visualizing complex datasets effectively 
•  Learn when and why dimensionality reduction is essential for pattern recognition  

• Explore the core principles and steps involved in the K-Means Clustering algorithm 
• Learn how to determine the optimal number of clusters 
• Understand the strengths and limitations of this algorithm in real-world scenarios 

Applications and Analysis Techniques 

• Discover how to identify hidden patterns in unstructured data 
• Select appropriate analysis methods to solve diverse business problems

This week, you will be involved in a hands-on project focused on clustering and PCA techniques. Attend a specialized Generative AI masterclass on learning from Text Data. 

•  Project on Clustering and PCA 
• Masterclass on Learning from Text Data

Classical Regression Techniques 

• Understand the fundamentals of linear and non-linear regression 
• Learn how to apply regression models for both prediction and inference 
• Explore how regression techniques can reveal trends and forecast outcomes 

Modern Regression for High-Dimensional Data 

• Learn to build accurate models using high-dimensional datasets 
• Apply regularization techniques like Lasso and Ridge to avoid overfitting 
•  Evaluate regression models using appropriate performance metrics 

Causal Inference in Predictive Modeling 

• Understand the principles of causal inference 
• Learn to differentiate between manipulation effects and observational correlations 
• Explore how to incorporate causal thinking into your regression models

In this module, you will master hypothesis testing for making data-driven decisionsYou will learn classification algorithms and data categorization. Evaluate Classification Models, explore Ensemble Techniques and Decision Trees to enhance predictive accuracy and robustness. 

Hypothesis Testing for Data-Driven Inference 

• Explore hypothesis testing frameworks to draw meaningful conclusions from data 
• Learn to make informed inferences about population parameters using statistical tests 

Classification Algorithms and Data Categorization 

• Understand core classification techniques used to determine class membership 
• Implement algorithms for effective categorization across varied datasets 

Evaluating Classification Models 

• Use performance metrics such as accuracy, precision, and recall to evaluate model effectiveness
• Enhance model performance through iterative evaluation 

Ensemble Learning for Robust Predictions 

• Learn how combining multiple models improves accuracy 
• Apply ensemble techniques like Random Forests to boost model robustness

Tree-Based Methods: Decision Trees and Random Forests 

• Discover how Decision Trees structure decision-making processes 
• Leverage the power of Random Forests to improve classification outcomes

This week, you will be involved in a project where you will apply your understanding of machine learning classification. Attend a masterclass on AI-powered text labeling that covers its practical implementation using Generative AI techniques.

•  Project on Machine Learning Classification 
• Masterclass on AI-Powered Text Labeling

This week, you will explore the fundamentals of Deep Learning, the concept of neurons and Artificial Neural Networks (ANNs) function. This module will also introduce you to Computer Vision and CNN Architecture and Transfer Learning.

• Introduction to Deep Learning 
• The Concept of Neurons 
• Artificial Neural Networks (ANNs) 
• Introduction to Computer Vision 
• CNN Architecture and Transfer Learning

This module of data science and machine learning program will introduce you to Recommendation Systems, Statistical and Machine Learning approaches. You will explore Collaborative Filtering Techniques and learn to enhance recommendation accuracy using Data Science techniques. 

Introduction to Recommendation Systems 

• Understand the purpose and real-world applications of Recommender Systems 
• Explore how personalization enhances user satisfaction and engagement 
• Gain experience in designing recommendation pipelines using real-world datasets 
• Build scalable and efficient Recommender Systems through practical exercises 

Statistical and Machine Learning Approaches

• Learn basic statistical techniques for generating recommendations 
• Apply Machine Learning algorithms to predict user preferences  

Collaborative Filtering Techniques 

• Dive into user-based and item-based Collaborative Filtering 
• Understand how user behavior and preferences drive model performance 

Personalization and Pattern Recognition 

• Discover common design patterns and frameworks used in recommender engines 
•  Learn how to enhance recommendation accuracy using Data Science techniques

This week will introduce you to the ethical implications of AI by exploring concepts such as bias, causality, and privacy. Learn about the AI lifecycle, feedback loops, and interdependencies to ensure responsible and fair AI system development and deployment.

•  Introduction to AI Lifecycle 
• Introduction to Bias and Its Examples 
•  Introduction to Causality and Privacy 
• Interconnections and Domains 
•  Interdependency and Feedback in AI Systems

This week, you will involved in a project based on Recommendation Systems using real-world data. 

•  Project on Recommendation System

This Data Science and Machine Learning program will help you deepen your expertise through these self-paced modules: