Kickstart your Python learning and get started with data science at the same time with this beginner-friendly, self-paced course. No prior programming skills are required. Python is the standard programming language used by data scientists and the demand for skilled individuals who can apply their Python knowledge to data science has never been higher.

You will learn basic programming concepts using Python such as native data types; data structures like lists, tuples, and dictionaries; programming logic including conditions, branching, and loops; and the gears that make up the engine of your application: functions and classes, those chunks of executable, reusable code that control the behavior of your application.

You will use a variety of Python libraries commonly used in data science such as Pandas, Numpy, and BeautifulSoup. You’ll also automate data collection with Python using a technique called web scraping and application programming interfaces, or APIs. This course uses Jupyter Notebooks for your hands-on labs, another popular data science tool. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating tasks using Python by producing several artifacts for your portfolio to prove it.

What You'll Learn

Combine fundamental programming concepts such as data types, data structures, expressions, and variables to write Python code that executes simple tasks
Write Python code using programming logic including branching, loops, functions, objects, and classes
Construct data science models using Python libraries including Pandas, Numpy, and BeautifulSoup
Import web data into a Python application using APIs and web scraping

Prerequisites

Basic computer skills

Course Syllabus:

Module 1: Python Basics

Types
Expressions and Variables
Hands-On Lab: Your First Program, Types, Expressions, and Variables
String Operations
Hands-On Lab: Strings

Module 2: Python Data Structures

List and Tuples
Hands-On Lab: Lists
Hands-On Lab: Tuples
Dictionaries
Hands-On Lab: Dictionaries
Sets
Hands-On Lab: Sets
Python Data Structure

Module 3: Python Programming Fundamentals

Conditions and Branching
Hands-On Lab: Conditions and Branching
Loops
Hands-On Lab: Loops
Functions
Hands-On Lab: Functions
Exception Handling
Objects and Classes

Module 4: Working with Data in Python

Reading Files with Open
Hands-On Lab: Reading Files with Open
Writing Files with Open
Hands-On Lab: Writing Files with Open
Loading Data with Pandas
Pandas: Working with and Saving Data
Practice Lab: Selecting data in a Dataframe
Hands-on Lab: Loading Data with Pandas
One Dimensional Numpy
Hands-On Lab: One-Dimensional Numpy
Hands-On Lab: Two-Dimensional Numpy

Module 5: APIs and Data Collection

Simple APIs
Hands-On Lab: Introduction to API
REST APIs & HTTP Requests
Hands-on Lab: Access REST APIs & Request HTTP
Hands-On Lab: API Examples
Webscraping
Hands-on Lab: Webscraping
Working with different file formats
Hands-on Lab: Working with different file formats

Python Fundamentals for Beginners

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