Data Science Course Syllabus in Chennai at Payilagam

Python in Data Science Course

Module 1: Python Introduction, Installation

• ➤ Python Introduction
• ➤ Download Python, Installing Python
• ➤ Verify the Installation
• ➤ Install a Text Editor or IDE (Optional)

Module 2: Data Types

• ➤ Numeric Types
• ➤ Text Type
• ➤ Boolean Type
• ➤ None Type

Module 3: Operators

• ➤ Arithmetic Operators
• ➤ Comparison Operators
• ➤ Logical Operators
• ➤ Assignment Operators
• ➤ Membership Operators
• ➤ Identity Operators

Module 4: Functions

• ➤ Function Call
• ➤ Return Statement
• ➤ Types of Parameters – Default Parameters, Variable Length, Arguments
• ➤ Variable-Length Argument Lists, Lambda Functions, Recursion

Module 5: Flow Control Statements

• ➤ Looping Statements: for, while
• ➤ Conditonal Statements: if, elif, else
• ➤ Exception Handling: try, except, finally
• ➤ Pass Statement
• ➤ Project: Calculator Application

Module 6: List

• ➤ Creating A list
• ➤ Accessing Elements
• ➤ Slicing
• ➤ Modifying Elements
• ➤ Adding Elements
• ➤ Removing Elements
• ➤ Sorting: Bubble Sort, Searching: Binary Search
• ➤ Project: Phone Book Application

Module 7: Tuple

• ➤ Creating a Tuple
• ➤ Accessing Elements
• ➤ Slicing
• ➤ Tuple Packing and Unpacking
• ➤ Immutable Nature
• ➤ Project: Inventory Management Application

Module 8: Set

• ➤ Creating a Set
• ➤ Accessing Elements, Adding Elements, Removing Elements
• ➤ Set Operations
• ➤ Other Set Operations
• ➤ Project: Unique words from a book

Module 9: Dictionary

• ➤ Creating a Dictionary, Accessing Values, Modifying Values
• ➤ Adding New Key-Value Pairs, Removing Key-Value Pairs
• ➤ Dictionary Operations, Nested Dictionaries
• ➤ Project: Student management system

Module 10: Package

• ➤ Creating a Package
• ➤ Importing Modules from a Package
• ➤ Importing the Whole Package
• ➤ Subpackages

Module 11: Python OOPs Introduction

• ➤ Class, object
• ➤ Attributes and Methods
• ➤ Encapsulation
• ➤ Inheritance
• ➤ Polymorphism
• ➤ Abstraction
• ➤ Project: Employee Management System

Module 12: Types of Methods

• ➤ Instance Methods
• ➤ Class Methods
• ➤ Static Methods
• ➤ Special Methods (Magic Methods or Dunder Methods)

Module 13: Exception Handling

• ➤ Try-Except Block
• ➤ Handling Specific Exceptions
• ➤ Else and Finally Blocks
• ➤ Raising and Custom Exceptions

Module 14: File Handling

• ➤ Opening, Reading, Writing from a File, Appending to a File
• ➤ Using with Statements, File Modes
• ➤ Exception Handling for File Operations, Working with Paths
• ➤ Project: CSV File parser application

Module 15: Regular Expression

• ➤ Basics of Regular Expressions
• ➤ Using Regular Expressions in Python
• ➤ Projects:
  • ⭐ Email Validator
  • ⭐ Mobile Number Extractor
  • ⭐ Password Validator

Module 16: Multithreading

• ➤ Creating Threads
• ➤ Thread Synchronization
• ➤ Thread Communication
• ➤ Daemon Threads

Module 17: Using SQLite as an Example

• ➤ Install SQLite
• ➤ Import the SQLite Library
• ➤ Connect to a Database
• ➤ Create a Cursor Object
• ➤ Execute SQL Queries
• ➤ Querying Data
• ➤ Closing the Connection

Module 18: Decorator, Generator Functions

• ➤ Generator Functions
• ➤ Decorator Functions

Data Wrangling(Cleaning) in Data Science Course

Module 1: Introduction to Pandas and NumPy

• ➤ What is Data Wrangling?, Introduction to NumPy arrays
• ➤ Introduction to Pandas Series and DataFrames
• ➤ Reading CSV/Excel/JSON files using Pandas
• ➤ Project: Explore a simple sales dataset, clean null values, and convert it to a report

Module 2: Working with APIs and Databases

• ➤ What is an API?, Fetching data from an API (using requests)
• ➤ Basics of SQL and SQLite
• ➤ Connecting Pandas with SQL (read_sql, to_sql)
• ➤ Project: Fetch data from OpenWeatherMap API, clean it, and save it to a database

Module 3: Multi-dimensional Arrays with NumPy

• ➤ Creating arrays (1D, 2D, 3D)
• ➤ Array indexing, slicing, reshaping, Mathematical operations and broadcasting
• ➤ Aggregations: sum, mean, std, etc.
• ➤ Project: Simulate student marks for 3 subjects using a 2D array and calculate averages

Module 4: Manipulating DataFrames with Pandas

• ➤ Filtering, sorting, grouping
• ➤ Handling missing values
• ➤ Merging and joining DataFrames
• ➤ pivot tables and Crosstab
• ➤ Project: Clean and analyze a messy Titanic dataset or an HR employee dataset

Data Visualization in Data Science Course

Module 1: Introduction to Data Visualization

• ➤ Importance of visualizing data
• ➤ Types of visualizations (bar, line, pie, scatter)
• ➤ Project: Visualize product sales over months using bar and line charts (matplotlib)

Module 2: Matplotlib and Seaborn

• ➤ Customizing charts (labels, legends, colors)
• ➤ Multiple plots in one figure
• ➤ Heatmaps, pairplots (with seaborn)
• ➤ Project: Plot correlation heatmap for student performance dataset

Module 3: Power BI

• ➤ Introduction to Power BI Desktop
• ➤ Importing data from Excel/CSV
• ➤ Creating basic dashboards
• ➤ Projects:
  • ⭐ Restaurant Sales Analysis
  • ⭐Data Science Job Trends Analysis
  • ⭐ TN Election Results Analysis
  • ⭐ Global GDP & So On

Module 4: Tableau

• ➤ Introduction to Tableau Public
• ➤ Connecting data
• ➤ Building basic dashboards and charts
• ➤ Project: Visualize employee attrition or sales funnel using Tableau

Machine Learning and AI in Data Science

Module 1: Introduction to ML & AI

• ➤ Difference between AI, ML, and DL
• ➤ ML workflow (data → model → prediction)
• ➤ Supervised vs Unsupervised Learning
• ➤ Project: Use scikit-learn to predict house prices

Module 2: Linear Regression with scikit-learn

• ➤ What is linear regression?
• ➤ Training and testing data, using train_test_split, fit, predict
• ➤ Projects: Predict student scores based on hours studied

Module 3: Multiple & Polynomial Regression

• ➤ Multiple regression (multi-variable input)
• ➤ Polynomial regression using PolynomialFeatures
• ➤ Project: Predict car price based on mileage, age, and brand

Module 4: Unsupervised Learning: K-Means Clustering

• ➤ What is clustering?, K-Means with scikit-learn
• ➤ Choosing the number of clusters
• ➤ Project: Cluster customers by shopping patterns

Module 5: TensorFlow or PyTorch Basics

• ➤ Introduction to Neural Networks, What are tensors?
• ➤ TensorFlow vs PyTorch overview
• ➤ Basic model building with PyTorch
• ➤ Project: Digit recognition with MNIST (simplified)