SEMESTER-1

1. Programming on Core Python

• An Introduction to Python & Basic Syntax
• Language Components & Loop
• List, Tuple, Set & Dictionary
• Functions, Regular Expressions
• Classes, Objects & Method
• Inheritance, Overloading, Encapsulation
• Modules & Packages, Database/File
• Data Structures & Algorithms

2. SQL Database Design and Develop

• Introduction, RDBMS, Normalization, SQL Command, No SQL
• Primary key & foreign key, Constraint, Select record
• SQL Operators, Group By, SQL Joining
• String Functions, Aggregate function. Scalar function. Date Functions, Wildcards
• Temporary Table, Clone Table, Sub Query, Views, Trigger
• Stored Procedures, Parameters, Output parameters, Return values, Advantage
• Indexing, Clustered, Non Clustered, Transactions, DeadLock, Prevent Deadlock
• SQL Injection, Prevent Injection, Cursor, Backup/Restore, Maintenance, Scheduling

3. Data Analysis & Data Visualizations

• Introductions & Statistics Revisited
• Data Visualization with Python
• Missing value handling, Data Normalization
• Working with numerical & categorical data
• Working with text data
• Exploratory data analysis part 1
• Exploratory data analysis part 2
• Data Visualization & reporting using Power BI

SEMESTER-2

4. Machine Learning using Python

• Introduction to Machine Learning
• NumPy arrays, Built-in methods, Indexing, slicing, Pandas, Series, DataFrame
• Linear Regression Theory, Algorithm, Exercise, Model
• K Nearest Neighbors Theory, Algorithm, Exercise, Model
• Decision Tree Theory, Algorithm, Exercise,
• Random Forests, Splitting, Entropy, IG, Bootstrap, Bagging
• Support Vector Machines Theory, Algorithm
• K Means Clustering Theory, Algorithm, Exercise

5. Deep Learning using Python

• Introduction to Neural Networks & Neural Networks Basics
• Perceptron & Multilayer Neural Network
• Activation Functions, Optimization Algorithms & others
• Regression using Deep Neural Networks
• Classification using Deep Neural Networks
• Convolutional Neural Network (CNN) & Case Studies
• Sequence Models
• Transfer Learning

6. Data Science Project

• Project Selection & Introduction to MLOPs
• Tracking and model management
• Models and data Pipelines
• Model Deployment
• Project update & evaluation
• Model Monitoring
• Best Practices
• Project presentation & final evaluation

After Completing This PGD You Will Be Able To:

• Coding in Python
• Getting data from database using SQL
• Data analysis using Python
• Feature Engineering
• Machine Learning
• Training Deep Learning Model
• Deploy your Machine Learning Model
• Create dashboards using BI tools like PowerBI
• Best practices in Data-driven product development