POST GRADUATE PROGRAM IN DATA SCIENCE WITH
GENERATIVE AI

Overview of Data Analytics

1. Explanation of quantitative and qualitative data
2. Role of data analytics in decision-making processes
3. Real expectations of a Data Scientist role

Introduction to AI

1. Differentiating AI, ML, and Deep Learning
2. Role of AI in predictive analytics

GenAI Tools & Platforms

1. Tools like GPT, DALL-E
2. Automating tasks with GenAI
3. From Scratch to GenAI Roadmap

Data Collection & Cleaning

1. GIGO Concept, Reliability of sources
2. EDA - Basic Statistics
3. Handling outliers, missing data (Imputation)
4. Dimensionality Reduction

Core Python

• Variables, Loops, Functions, Data Types

Statistics & EDA

• Box plots, histograms, summaries

Inferential Statistics

• Hypothesis testing, Chi-Square, Sampling

Principles & Tools

• Color theory, Cognitive load
• Tableau (Calculations, Parameters)
• Power BI Dashboards
• Storytelling with Data

ML Overview

• Bias, Variance, Overfitting
• Supervised vs Unsupervised

Algorithms

• Regression (Logistic/Linear)
• Decision Trees, SVM, KNN, K-Means
• Model Validation (ROC, Cross-validation)

Building AI

• Unstructured data, Image/Text embeddings
• NLP (NLTK, spaCy) - Sentiment Analysis
• Deep Learning with TensorFlow & PyTorch

Real World Applications

• Reporting Automation
• Generating Assets with Co-pilot/Perplexity
• Real-time Analytics & IoT
• AI Ethics & Privacy

Big Data Tech

• Scalability Challenges
• AI with Big Data
• Hadoop Ecosystem

Final Project

• Project Scoping & Objectives
• Comprehensive Analysis using AI Tools
• Final Presentation of Findings