Module 1 AI and ML Fundamentals

• Overview of Data Science
• Basics of AI and ML
• Statistical Analysis
• R Programming

Module 2 Python for AI

• Basics of Python
• Setup Python Environment 
• Python Looping and Control Statements
• Python Data Types
• OOP Concepts
• Database Connection
• Python Libraries: Numpy, Scipy, Pandas, MatPlot

Module 3 SAS-Data Analytics

• Introduction to SAS 
• SAS Functions & Operators
• Graphs & Macros
• SAS Procedures & Format

Module 4 Concepts of Data Management

• Data Acquisition, Pre-processing and Preparation
• Data Transformation and Quality
• Managing Text Data
• Big Data Fundamentals and Frameworks

Module 5 Predictive Analytics

• Linear and Non-Linear Regression
• Multiple Linear Regression
• Forecasting Models

Module 6 Concepts of Machine Learning

• Basics of ML 
• Understanding Clustering
• Association Rule Mining
• Classification (Naive Bayes Classifier, K-Nearest Neighbors)

Module 7 Statistical Decision-Making

• Data Visualization
• Inferential Statistics
• Sampling & Estimation

Module 8 Understanding AI

• AI Basics
• Neural Networks
• Convolution and Recurrent Neural Networks

Module 9 Advanced Deep Learning & Computer Vision

• Understand Distributed & Parallel Computing
• Reinforcement Learning
• Deploy Deep Learning Models
• Producing Images with Neural Style
• Object Detection through (CNN) Convolutional Neural Networks

Module 10 Concepts of Deep Learning using TensorFlow & Keras

• Libraries of Deep Learning 
• Keras API
• Deep Learning Algorithms
• TensorFlow concepts

Module 11 Tableau 10

• Overview of Data Visualisation
• Tableau Architecture
• Working with Data Blending
• Creation of Sets
• Stories, Dashboards, and Filters
• Expression, Calculations, and Parameters
• Tableau Prep

Module 12 Cloud Computing and AWS

• Overview of Cloud Computing 
• Introduction to AWS
• Storage Volumes
• Elastic Compute
• VPC-Virtual Private Cloud
• S3-Simple Storage Services
• Concepts of AWS Lambda
• Amazon (ML) Machine Learning