Post Graduate Program in Data Science, Generative AI & Cloud Computing (Live Online)

About Course

The Post Graduate Program in Data Science & Generative AI is a cutting-edge program designed to equip learners with expertise in Data Science, Artificial Intelligence, and Generative AI. This comprehensive course provides hands-on experience with real-world projects, covering essential concepts like Machine Learning, Deep Learning, Natural Language Processing, and Cloud-based AI solutions. Whether you are a fresh graduate or a working professional, this program ensures career growth in the rapidly evolving AI landscape.

Why Choose the PG Program in Data Science and AI?

Career Opportunities After the Program

Graduates of this program will be prepared for various high-demand roles, including:

Data Scientist – Analyze and interpret complex data to drive business insights.
Machine Learning Engineer – Develop and deploy machine learning models to solve real-world problems.
AI Research Scientist – Conduct research and innovate in the field of Artificial Intelligence.
Big Data Engineer – Work with large-scale data processing frameworks such as Hadoop and Spark.
Cloud AI Engineer – Deploy AI solutions using cloud platform like Azure.
Business Intelligence Analyst – Utilize data visualization tools like Power BI and Tableau to extract actionable insights.
Data Engineer – Design, build, and maintain scalable data pipelines for businesses.
NLP Engineer – Develop AI solutions for text analysis, chatbot development, and voice recognition.
Deep Learning Engineer – Work on advanced AI models such as CNNs, RNNs, and Transformer networks.
MLOps Engineer – Automate machine learning workflows and manage model deployment.

With the rapid expansion of AI and data-driven industries, graduates can expect strong job prospects and opportunities for career advancement in various sectors, including finance, healthcare, e-commerce, marketing, and technology.

Roles Offered

8-in-1 Learning Excellence with KAE Education

Our Hiring Partners

Students Profile

Sample Certificate

Post Graduate Program in Data Science, Generative AI & Cloud - Sample Certificate

Master SQL & NoSQL databases for efficient data manipulation and querying.
Gain expertise in Python & R programming for data analysis and machine learning.
Understand statistical concepts & hypothesis testing for data-driven decision-making.
Learn Machine Learning algorithms like Decision Trees, Random Forest, and SVM.
Explore Deep Learning & Generative AI using TensorFlow, Keras, and BERT.
Develop skills in Natural Language Processing (NLP) for text analytics and chatbot development.
Work with Big Data tools like Apache Spark, Hadoop, and PySpark for large-scale data processing.
Implement Cloud Computing & MLOps on Azure for scalable AI deployment.
Create dynamic Data Visualizations using Power BI or Tableau for insightful analytics.
Build an end-to-end Capstone Project applying Data Science, AI, and Cloud concepts.

Course Content

Module 1. SQL (RDBMS / NoSQL)

SQL Manipulations (DDL, DML, DQL)
SQL Joins
String, Mathematical & Date-Time Functions
NoSQL Databases & Querying

Module 2. Data Science with Python

Module 3. Data Science with R

Module 4. Statistics for Machine Learning

Module 5. Mathematics Foundation for AI & ML

Module 6. Machine Learning (ML)

Module 7. Exploratory Data Analysis (EDA)

Module 8. Clustering & Dimensionality Reduction

Module 9. Association Rules & Recommendation Engine

Module 10. Text Mining & Natural Language Processing (NLP)

Module 11. Advanced Machine Learning Techniques

Module 12. Hypothesis Testing & Confidence Intervals

Module 13. Regression Techniques

Module 14. Time Series Forecasting & Survival Analytics

Module 15. Machine Learning Model Evaluation & Deployment

Module 16. Deep Learning & Generative AI

Fundamentals of Generative AI
Introduction to TensorFlow & Keras
Neural Networks (ANN, CNN, RNN, LSTM)
Artificial Neural Networks (ANN) & Perceptron Models
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Transformer Models & BERT
Generative AI: GANs & Variational Autoencoders
Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
Prompt Engineering for AI Optimization

Module 17. Model Deployment & MLOps

Module 18. Cloud Computing for AI & Data Science

ML & AL on the Cloud:
Cloud Fundamentals & Virtualization
Cloud-Based Data Storage & Management
AI & ML Services in (Azure)
Serverless Computing & Function-as-a-Service (FaaS)
Big Data & Cloud Integration:
Introduction to Big Data & MapReduce
Apache Spark & Hadoop for Big Data Processing
PySpark for Scalable Data Analytics
Kafka for Real-Time Data Streaming
Handling Live Data on Cloud

Module 19. Power BI & Tableau for Data Visualization

Power BI:
BI Concepts & Data Modeling
Data Preparation, Transformation & Modeling
DAX Calculations, Interactive Dashboards & Reports
Security & Performance Optimization
Tableau:
Data Modeling, Data Blending
Advanced Visualizations & Dashboards
Dashboard Development & Report Automation

About Course

Why Choose the PG Program in Data Science and AI?

Career Opportunities After the Program

8-in-1 Learning Excellence with KAE Education

Our Hiring Partners

Students Profile

Sample Certificate

What Will You Learn?

Course Content

Module 1. SQL (RDBMS / NoSQL)

SQL Manipulations (DDL, DML, DQL)

SQL Joins

String, Mathematical & Date-Time Functions

NoSQL Databases & Querying

Module 2. Data Science with Python

Python Basics & Fundamentals

Data Structures (Lists, Tuples, Dictionaries, Sets)

Functions, Modules, File Handling

Object-Oriented Programming with python

NumPy, Pandas, & Multi-Dimensional Arrays

Connecting to Databases

Module 3. Data Science with R

R Basics & Data Structures

R Programming Fundamentals

Data Management & Analysis

Advanced Data Visualization

Descriptive Statistics in R

Regression Analysis & Decision Trees

K-Means & Hierarchical Clustering

Association Rules Analysis

Module 4. Statistics for Machine Learning

Data-Driven Decision Making

Measures of Central Tendency (Mean, Median, Mode)

Measures of Variability (Range, Variance, Standard Deviation)

Probability Rules (Addition & Multiplication)

Normal & Standard Distribution

Binomial & Poisson Distribution

Descriptive & Inferential Statistics

Hypothesis Testing & Confidence Intervals

Module 5. Mathematics Foundation for AI & ML

Optimization & Derivatives

Linear Algebra & Matrix Operations

Module 6. Machine Learning (ML)

Introduction to Machine Learning & Applications

CRISP-ML(Q) Project Management Methodology

Data Types: Nominal, Ordinal, Interval, Ratio

Supervised & Unsupervised Learning

Data Cleaning Techniques & Feature Engineering

Data Visualization with Matplotlib & Seaborn

ML Algorithms: Decision Trees, Random Forest, SVM, Naive Bayes, KNN

Hyperparameter Tuning & Model Evaluation

Module 7. Exploratory Data Analysis (EDA)

Business Moments & Graphical Representation

Data Preprocessing Techniques

Feature Engineering & Selection

Module 8. Clustering & Dimensionality Reduction

K-Means, DBScan & Hierarchical Clustering

Elbow Curve & Silhouette Score

Curse of Dimensionality

PCA, SVD, Eigen Values & Eigen Vectors

N-Components & Variance Ratio

Module 9. Association Rules & Recommendation Engine

Market Basket Analysis

Association Rules Performance Measures

User-Based & Item-Based Filtering

Collaborative Filtering & Performance Metrics

Recommendation Engine Vulnerabilities

Module 10. Text Mining & Natural Language Processing (NLP)

Text Mining & Text Preprocessing

Text Cleaning & Tokenization

Bag of Words(BOW), TF-IDF, Word Embeddings, DTM and TDM

Corpus-Level Analysis & Word Clouds

Sentiment Analysis & Chatbot Development

Social Media Web Scraping

Module 11. Advanced Machine Learning Techniques

Cross-Validation & Parameter Tuning

Data Scaling Techniques

Handling Imbalanced Datasets

Ensemble Methods (Boosting, Stacking, Bagging)

Module 12. Hypothesis Testing & Confidence Intervals