Course

Intermediate Python for Data Science

This course continues your Python for Data Science journey. You’ll learn intermediate Python techniques to clean and analyze text data, and you’ll learn Python concepts such as object-oriented programming. By the end, you’ll have the essential skills you need to perform data analysis and data cleaning.

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Part of the Data Analyst (Python), and Data Scientist (Python) paths.

4.8 (359 reviews)

70,640 learners enrolled in this course.

Beginner friendly
8 hours
Self paced
5 lessons
1 project

“I like that Dataquest doesn’t leave out visual learners. It’s text-based, but it’s not just text — concepts are explained using illustrations and animations.”

Antoine Eripret

SEO Lead @Liligo.com

Intermediate Python for Data Science

Enroll For Free

Course overview

This course builds upon Introduction to Python Programming, For Loops and Conditional Statements in Python, Dictionaries, Frequency Tables, and Functions in Python, and Python Functions and Jupyter Notebook. You’ll not only learn how to manipulate text, clean messy data, and more but also how to work with object-oriented programming concepts, dates, and times in Python.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.

Key skills

Cleaning and analyzing text data
Processing dates and times
Defining object-oriented programming in Python

Course outline

Intermediate Python for Data Science [5 lessons]

Cleaning and Preparing Data in Python 2h

Lesson Objectives

Prepare text data in Python
Clean data
Resolve errors during data cleaning

Python Data Analysis Basics 2h

Lesson Objectives

Analyze data
Summarize numeric data
Format strings in Python

Object-Oriented Python 2h

Lesson Objectives

Define object-oriented programming for data science
Define classes, instances, attributes, and methods.
Create your own class

Working with Dates and Times in Python 2h

Lesson Objectives

Parse dates from strings using the datetime library
Analyze dates and times
Format dates using strftime

Guided Project: Exploring Hacker News Posts 2h

Lesson Objectives

Employ loops to explore data CSV data
Clean string methods for data analysis
Process dates in python

Projects in this course

Exploring Hacker News Posts

For this project, we’ll be data analysts exploring Hacker News posts. We’ll use Python string manipulation, OOP, and date handling to analyze trends driving post popularity. Check out our Jupyter Notebook Guided Project if needed.

View Project

The Dataquest guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

Master skills faster with Dataquest

Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Share the evidence of your hard work with your network and potential employers.

Grow your career with
Dataquest.

98%

of learners recommend

Dataquest for career advancement

4.85

Dataquest rating

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$30k

Average salary boost

for learners who complete a path

Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

Victoria E. Guzik

Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”