Python

SIGMA DHYANA

Python EDA (Exploratory Data Analysis)

Python track covering programming foundations, statistics, NumPy, Pandas, visualization and full EDA workflow.

Module 1: Introduction to Python
Objective: To introduce Python, emphasizing its advantages and core concepts, particularly in data analytics.
Topics and Sub-Topics: ▼
  • Overview of Data Analytics
  • Introduction to Data Analytics
  • Importance of Python in Data Analytics
  • Real-world Applications of Python in Data Analytics
  • Python Programming History & Features
  • History of Python
  • Key Features of Python
  • Setting Up a Python Environment
  • Installing Anaconda
  • Introduction to Jupyter Notebooks
  • Setting Up Visual Studio Code for Python
  • Introduction to PyCharm
  • Python Syntax Overview
  • Basic Syntax
  • Indentation
  • Comments and Docstrings
Hands-on Exercise:
  • Install Anaconda and set up Jupyter Notebooks.
  • Write a simple Python program using Jupyter Notebook.
  • Set up Visual Studio Code and PyCharm for Python development.
Home
Module 2: Basic Python
Objective: To master the fundamental elements of Python programming.
Topics and Sub-Topics: ▼
  • Identifiers and Variables
  • Naming Conventions
  • Assigning Values
  • Dynamic Typing
  • Keywords
  • List of Python Keywords
  • Reserved Words
  • Operators
  • Arithmetic Operators
  • Comparison Operators
  • Logical Operators
  • Bitwise Operators
  • Assignment Operators
  • Identity Operators
  • Membership Operators
  • Data Types
  • Primitive Data Types: Integer, Float, String, Boolean
  • Non-Primitive Data Types: List, Tuple, Dictionary, Sets
  • Comprehensions in Python
  • List Comprehensions
  • Dictionary Comprehensions
  • Set Comprehensions
  • Nested Comprehensions
  • Control Flow
  • Conditional Statements: If, If-else, If-elif-else, Nested if
  • Loops: While Loop, For Loop, Break, Continue, Pass
Hands-on Exercise:
  • Write programs demonstrating variable assignments, operators, and control flow.
  • Use comprehensions to create lists, dictionaries, and sets.
Home
Module 3: Functions and Modules
Objective: To define and use functions and modules to create modular code.
Topics and Sub-Topics: ▼
  • User Defined Functions
  • Defining Functions
  • Function Arguments
  • Return Statement
  • Built-in Functions
  • Common Built-in Functions
  • Using Built-in Functions
  • Lambda Functions
  • Anonymous Functions
  • Syntax and Usage
  • Map, Filter, Reduce
  • Map: Applying a function to all items in an input list
  • Filter: Constructing a list from elements of the input list that return true for a function
  • Reduce: Applying a rolling computation to sequential pairs of values in a list
Hands-on Exercise:
  • Write functions to perform simple tasks.
  • Use lambda functions with map, filter, and reduce.
Home
Module 4: File Handling
Objective: To perform file operations in Python for reading and writing data.
Topics and Sub-Topics: ▼
  • File Operations
  • Overview of File Handling in Python
  • Importance of File Handling in Programming
  • File Types: CSV, Excel, Text, PDF, JSON
  • Opening Files
  • Using the open() Function
  • Different Modes for Opening Files (r, w, a, x)
  • Creating Files
  • Creating a New File Using 'w', 'a', or 'x' Mode
  • Reading Files
  • Reading the Entire Content Using read()
  • Reading Line by Line Using readline()
  • Reading All Lines into a List Using readlines()
  • Writing to Files
  • Writing a String to a File Using write()
  • Writing Multiple Lines Using writelines()
  • Deleting Files
  • Using the os Module to Delete Files
Hands-on Exercise:
  • Create, read, write, and delete files using Python.
  • Perform file operations with CSV and JSON files.
Home
Module 5: Exception Handling
Objective: To handle exceptions and errors gracefully in Python.
Topics and Sub-Topics: ▼
  • Types of Errors
  • Syntax Errors
  • Runtime Errors
  • Logical Errors
  • Exception Handling
  • try … except Block
  • try … except … finally Block
  • try … except … else Block
  • Handling Multiple Exceptions
  • Raising Exceptions
Hands-on Exercise:
  • Write programs to demonstrate exception handling.
  • Create custom exceptions and handle them appropriately.
Home
Module 6: Regular Expressions
Objective: To use regular expressions for pattern matching in strings.
Topics and Sub-Topics: ▼
  • Python re Module
  • Functions in re-Module
  • Compiling Regular Expressions
  • Methods with Regex Usage
  • match()
  • search()
  • findall()
  • sub()
  • split()
Hands-on Exercise:
  • Use regular expressions to search, match, and manipulate strings.
Home
Module 7: Object-Oriented Programming (OOP) in Python
Objective: To master the core concepts of OOP in Python for designing modular code.
Topics and Sub-Topics: ▼
  • Classes and Objects
  • Defining Classes
  • Creating Objects
  • Class Attributes and Methods
  • OOP Principles
  • Polymorphism
  • Encapsulation
  • Inheritance
Hands-on Exercise:
  • Create classes and objects.
  • Implement OOP principles in Python programs.
Home
Module 8: Statistics with Python
Objective: To understand basic statistical concepts and perform statistical analysis using Python.
Topics and Sub-Topics: ▼
  • Introduction to Statistics
  • Importance of Statistics in Data Analysis
  • Types of Statistics: Descriptive and Inferential
  • Descriptive Statistics
  • Measures of Central Tendency: Mean, Median, Mode
  • Measures of Dispersion: Range, Variance, Standard Deviation
  • Skewness and Kurtosis
  • Probability
  • Basic Probability Concepts
  • Probability Distributions: Normal, Standard Normal Distribution
  • Correlation and Regression
  • Correlation Coefficient
  • Coefficient of Determination
  • Simple Linear Regression
Hands-on Exercise:
  • Implement statistical measures using NumPy, SciPy, and StatsModels.
  • Perform linear regression and correlation analysis.
Home
Module 9: NumPy
Objective: To introduce NumPy for numerical operations.
Topics and Sub-Topics: ▼
  • NumPy Basics
  • Difference Between NumPy and List
  • Introduction to NumPy
  • NumPy Array
  • Array Operations
  • numpy.random Module
  • Array Operations
  • Vector Operations
  • Statistical Functions
  • Array Manipulation
  • Array Indexing
  • Array Manipulation
  • Array Broadcasting
Hands-on Exercise:
  • Practice with NumPy arrays and perform mathematical operations.
  • Manipulate and index arrays.
Home
Module 10: Data Pre-processing with Pandas
Objective: To manipulate and preprocess data using Pandas.
Topics and Sub-Topics: ▼
  • Introduction to Pandas Library
  • Series and DataFrame
  • Data Structures in Pandas
  • Working with Series and DataFrames
  • Creating Series and DataFrames
  • Basic Operations on Series and DataFrames
  • Indexing and Selecting Data
  • Selecting Rows and Columns
  • Filtering Data
  • Data Cleaning and Preprocessing
  • Dealing with Duplicate Data
  • Handling Outliers
  • Feature Scaling and Normalization
  • Encoding Categorical Variables
  • Pandas Methods
  • Creating DataFrames from various sources
  • Viewing Data
  • Selecting Data
  • Filtering Data
  • Adding/Modifying Columns
  • Removing Data
  • Handling Missing Data
  • Detecting and Dropping Duplicates
  • Aggregation and Grouping
  • String Methods
  • Merging and Joining
  • Date and Time Handling
  • Pivot Tables
  • Exporting Data
Hands-on Exercise:
  • Create and manipulate DataFrames.
  • Clean and preprocess data using Pandas methods.
Home
Module 11: Data Visualization with Matplotlib and Seaborn
Objective: To create visualizations using Matplotlib and Seaborn.
Topics and Sub-Topics: ▼
  • Introduction to Data Visualization
  • Importance of Data Visualization
  • Types of Data Visualization
  • Matplotlib for Basic Plotting
  • Line Plot
  • Bar Plot
  • Histogram
  • Scatter Plot
  • Pie Chart
  • Box and Whiskers Plot
  • Seaborn for Statistical Data Visualization
  • Line Plot
  • Barplot
  • Boxplot
  • Heatmap
  • Pairplot
  • Countplot
  • Regplot
  • Scatterplot
  • Hueplot
  • Violin plot
  • Swarmplot
  • Stripplot
  • Customizing Plots and Charts
  • Choosing Axis
  • Adding Grids
  • Customizing Axis Values
  • Adding Titles and Labels
  • Customizing Colors and Styles
  • Adding Legends
Hands-on Exercise:
  • Create various types of plots using Matplotlib and Seaborn.
  • Customize plots for better visualization.
Home
Module 12: Exploratory Data Analysis (EDA)
Objective: To perform exploratory data analysis (EDA) to summarize the main characteristics of a dataset and uncover patterns, spot anomalies, test hypotheses, and check assumptions using visualizations and summary statistics.
Topics and Sub-Topics: ▼
  • Introduction to EDA
  • Introduction to EDA
  • Tools and Libraries for EDA
  • Loading Data
  • Data Cleaning and Preparation
  • Identifying and Handling Missing Data
  • Identifying and Handling Duplicates
  • Identifying and Handling Outliers
  • Feature Engineering
  • One Hot Encoding
  • Label Encoding
  • Range Categorization
  • Univariate Analysis
  • Summary Statistics
  • Visualizations for Univariate Analysis
  • Distribution Analysis
  • Bivariate Analysis
  • Summary Statistics for Bivariate Analysis
  • Visualizations for Bivariate Analysis
  • Categorical vs. Numerical Analysis
  • Multivariate Analysis
  • Summary Statistics for Multivariate Analysis
  • Visualizations for Multivariate Analysis
  • Exploratory Data Analysis (EDA) Practice
  • Case Study
  • Reporting and Presenting EDA Findings
  • Hands-on
  • Conducting a complete EDA on a given dataset
  • Creating and presenting an EDA report
  • Mentored EDA Projects Hands-On
  • Analyzing Diwali Sales Trends
  • IPL Match Performance Analysis
  • Sales Insights from Euromart Data
  • Car Manufacturing and Pricing Analysis
  • Titanic Survival Data Analysis
Hands-on Exercise:
  • Conduct a complete EDA on a given dataset.
  • Create and present an EDA report.
Home Next Subject: SQL for Data Analysis & Database Management →