Start your data science journey with Python. Learn practical Python programming skills for basic data manipulation and analysis.

- Home
- Python Essentials for Data Analysis IToggle Dropdown
- 1.1 Getting started - Hello, World!
- 1.2 Variables
- 1.3 Data types
- 1.4 Printing
- 1.5 Lists
- 1.6 Dictionaries
- 1.7 Input function
- 1.8 Arithmetic operators
- 1.9 Comparison operators
- 1.10 Logical operators
- 1.11 Identity operators
- 1.12 Membership operators
- 1.13 Conditional statements (if-elif-else)
- 1.14 Importing modules
- 1.15 For loops
- 1.16 While loops

- Python Essentials for Data Analysis IIToggle Dropdown
- 2.1 Introduction to Functions in Python
- 2.2 Functions - Arguments
- 2.3 Functions with Return Values
- 2.4 Functions - A Fun Exercise!
- 2.5 Functions - Arbitrary Arguments (*args)
- 2.6 Functions - Arbitrary Keyword Arguments (**kwargs)
- 2.7 Recursive Functions
- 2.8 Lambda Expressions
- 2.9 Functions - More Exercises

- Data Analysis with PandasToggle Dropdown
- PD.1 Introduction to Pandas
- PD.2 Basics of Pandas
- PD.3 Finding and Describing data
- PD.4 Assigning Data
- PD.5 Manipulating Data
- PD.6 Handling Missing Data
- PD.7 Removing and adding data
- PD.8 Renaming data
- PD.9 Combining data
- PD.10 Using Pandas with other functions/mods
- PD.11 Data classification and summary
- PD.12 Data visualisation

- Data Analysis with NumPyToggle Dropdown
- NP.1 Introduction to NumPy
- NP.2 Create Arrays Using lists
- NP.3 Creating Arrays with NumPy Functions
- NP.4 Array Slicing
- NP.5 Array Reshaping
- NP.6 Math with NumPy I
- NP.7 Combining 2 arrays
- NP.8 Adding elements to arrays
- NP.9 Inserting elements into arrays
- NP.10 Deleting elements from arrays
- NP.11 Finding unique elements and sorting
- NP.12 Math with NumPy II
- NP.13 Analysing data across arrays
- NP.14 NumPy Exercises

- Learning Resources
- Contact Us

**Examples of Introductory Python Online Courses**

- LinkedIn Learning via NTULearn – Python Essential Training

Instructor: Bill Weinman

Duration: 4h 45m

Komodo platform

It covers the basics of the language syntax and usage, as well as advanced features such as objects, generators, and exceptions. Learn how types and values are related to objects; how to use control statements, loops, and functions; and how to work with generators and decorators.

- Coursera - Python for Everybody

Offered by University of Michigan

Instructor: Charles R. Severance Duration:

Approx. 19 hours to complete

Python playground – environment

Text editor – Atom

variables, if-else, loops, functions

It covers Chapters 1-5 of the textbook “Python for Everybody” (which is free to download from https://www.py4e.com/).

Once a student completes this course, they will be ready to take more advanced programming courses.

- Kaggle – Python

This online course, complete with exercises, covers Python syntax, variable assignment, functions, Booleans, Conditionals, Lists, Loops, Strings, Dictionaries and Working with external libraries.

**Activities for Beginners in Python**

- CodingBat
- Hour of Code
- Build an animal classifier

This course assumes you already understand:- variables
- strings
- using print
- asking for user input
- conditionals: if elif else

- Code Like a Girl: A Storyteller (via Trinket)
- Write a story
- Ask for input
- String methods: capitalize strip upper
- Conditionals

- The Dark Tunnel
- Input
- Loops
- Conditionals

- Build an animal classifier
- Practice Python
- Real Python
- Hitchhiker’s Guide to Python

- Last Updated: Jun 24, 2024 9:14 AM
- URL: https://libguides.ntu.edu.sg/python
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