Python for Data Science

Curriculum

Module 01 – An Introduction to PYTHON

• Introductory Remark about Python
• A Brief History of Python
• How Python is different from other languages
• Python Version
• Installing Python
• IDLE
• Getting Help
• How to execute a Python program?
• Writing your first program

Module 02 – Python Basics

• Introduction
• Python keywords and Identifiers
• Python statements
• Comments in python
• Basic Syntax
• Printing on screen 
• Getting user input -Reading data from keyboard 
• Exercise
• Key Takeaways

Module 03 – Variables and data types

• Introduction
• Variables
• Data types
• Numbers
• Strings

Module 04 – Arrays in Python

• Lists
• Tuples
• Dictionary
• Exercise

Module 05 – Decision making & Loops

• Introduction
• Control flow and syntax
• The if statement
• Python operators
• The while Loop
• Break and continue
• The for Loop
• Pass statement
• Exercise

Module 06 – Function

• Introduction of Function
• Calling a function
• Function arguments
• Built in function
• Scope of variables
• Decorators
• Passing function to a function

Module 07 – Modules and Packages

• Introduction of Modules and Packages
• Importing Modules
• Standard Modules- sys
• Standard Modules- OS
• The dir() Function
• Packages

Module 08 – Exception Handling

• Introduction of Exception Handling
• Errors
• Run Time Errors
• Handling IO Exception
• Try….except statement
• Raise
• Assert

Module 09 – File Handling in Python

• Introduction of Exception Handling
• Introduction to File Handling in Python
• Files and Directories
• Writing Data to a file
• Reading data from a file
• Additional file methods
• Working with files 
• Working with Directories
• The pickle Module

Module 10 – Mathematical Computing using NumPy

• Learning objectives
• Introduction of NumPy in Python
• Install NumPy
• NumPy Creating Arrays
• Operations Using NumPy
• NumPy Data Types
• NumPy – Array Creation Routines
• NumPy – Indexing & Slicing
• NumPy – Advanced Indexing
• NumPy Integer Indexing
• NumPy Boolean Array Indexing
• NumPy – Iterating Over Array
• NumPy – Broadcasting
• NumPy – Array Manipulation
• NumPy – Mathematical Functions
• Vectorization for fast computation
• Matrix operations (dot(), det(), inv())
• Random sampling (np.random.choice(), np.random.rand())

Module 11 – Data visualization using Matplotlib

• Learning objectives
• Data Visualization
• Considerations of Data Visualization
• Factors of Data Visualization
• Python Libraries
• Create Your First Plot Using Matplotlib
• Line Properties
• Create a Line Plot for Football Analytics
• Multiple Plots and Subplots
• Create a Plot with Annotation
• Create Multiple Plots to Analyze the Skills of the Players
• Create Multiple Subplots Using plt.subplots
• Types of plots
• Create a Stacked Histogram
• Create a Scatter Plot of Pretest scores and Posttest Scores
• Create a Pie Chart
• Create a Bar Chart
• Create Box Plots
• Analyzing Variables Individually
• Key Takeaways

Module 12 – Pandas

• Learning Objectives
• Introduction to Pandas
• Data structures of Pandas 
• Pandas Series
• Pandas DataFrames
• Pandas Object creation
• Viewing Pandas data
• Selection on Pandas Data
• Operations on Pandas Data
• Essential basic functionality on Pandas DataFrame
• Head and tail on Pandas DataFrame
• Attributes and underlying data
• Grouping
• Sorting
• Importing and exporting data
• Indexing and selecting data
• Different choices for indexing
• Attribute access
• Slicing ranges
• Selecting random samples

Module 13- Data Preprocessing & Cleaning

• Handling missing values (dropna(), fillna())
• Handling duplicate data
• String operations for text cleaning (strip(), lower(), replace())
• Working with timestamps (pd.to_datetime())
• Feature scaling (StandardScaler, MinMaxScaler)

Module 14-Exploratory Data Analysis (EDA) 

• Understanding distributions using histograms & box plots
• Correlation analysis (corr())
• Detecting outliers
• Visualizing relationships using pair plots & scatter plots
• Grouping and aggregation

Module 15-Working with APIs & Web Scraping (After File Handling)

• Calling APIs with Python (requests)
• Parsing JSON/XML data
• Web Scraping with BeautifulSoup
• Handling API rate limits

Capstone Project: Data Analysis  Using Python