Introduction to Data Science with Python

DQLAB Summary

DQLAB: Python Preparation Class

Picture by Glenn Carstens-Peters from Unsplash

Contents

• Chapter 1: Basics in Python #1
• Chapter 2: Basics in Python #2

Chapter 1: Basics in Python #1

Theory

Python description

• Python was created by Guido van Rossum in 1991 as an open-source high-level programming language for general-purposes programming.

Python libraries that are usually used in data science

• Numpy
• Scipy
• Pandas
• Matplotlib
• Scikit-learn
• Seaborn

Python coding/syntax elements/structures

• Statements
• Variables
• Literals
• Operators
• Reserved Words
• Whitespace
• Comments

Coding Practice

• Using function print() to print out texts on console window.
• Storing texts and numbers to variables.
• Writing comments using # (text) and ``` (text) ```.

Chapter 2: Basics in Python #2

Theory

Data types in Python

• None
• Numeric (int, float)
• Boolean (bool)
• Sequence (str, list, tuple)
• Set
• Map (dict)

Coding Practice

• Printing out a variable as different data types.
• Importing Python libraries using import … (as ….).

This is my notes on what I’ve learned on dqlab, an online platform where people can take courses related to data science. The courses are delivered as theories, quizzes, and (most of them) coding exercises in R, Python, and SQL.