Python for Data Science Intermediate

Continue your Python for data science journey with this intermediate course. This course will help you level up your data science skills by introducing you to more advanced techniques that are essential for data analysis and cleaning.

Explore real-world data about artwork at the Museum of Modern Art, and learn to manipulate text, clean messy data, and more. You will unlock the true power of Python as we dive into object-oriented programming (OOP) and how it relates to data science.

This course focuses on the following:

  • Developing intermediate Python techniques to clean and analyze text data
  • Introducing key Python concepts like object-oriented programming, classes, instances, attributes, and methods
  • Building proficiency in data analysis, loops, and working with dates and times in Python

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What You’ll Learn

In this intermediate Python course, you'll learn the necessary techniques to correctly perform routine tasks like cleaning, preparing, analyzing, and manipulating data in Python. You’ll also get to practice summarizing numeric data and formatting strings in Python.

In addition, you’ll discover the importance of object-oriented programming in Python, classes, instances, attributes, methods, and more.

You’ll also discover new Python capabilities by creating your own custom class and using new libraries like datetime and strftime to clean, format, and organize troublesome dates and times in Python.

At the end of the course, you will combine all the skills you learned to complete a portfolio project involving Hacker News post titles to determine which types of posts are most likely to be successful at which times.

Enroll in this skill path if you want to learn how to do the following:

  • Work with and prepare text data in Python.
  • Create your own custom class
  • Format strings in Python
  • Discover object-oriented programming and how it relates to the data science workflow
  • Understand key concepts, including classes, instances, attributes, and methods
  • Perform basic data analysis and cleaning techniques and troubleshoot common errors
  • Parse dates from strings using the datetime library
  • Implement techniques for data and time analysis
  • Format dates using strftime
  • Leverage loops to explore CSV data
Data Scientist in Python Salary Increase

Data scientists command an average salary of $115k, according to Indeed.

Data Scientist in Python Job Openings

WEF Future of Jobs Report 2020 ranks data analysts as the #1 in-demand job.

Data Scientist In Python Job Growth

Glassdoor and Indeed alone report over 30,000 job openings for remote Python Devs.

How Our Python for Data Science Intermediate Course Works

The data scientist career path starts you out at the beginning, which means that it can be for anyone. There is no experience required to start.

This career path is for individuals who are ready for an exciting career switch, data professionals who are looking to advance in their field, college students pursuing data science who want to get job-ready, and more!

Take this intermediate course only after you’ve mastered all the basics covered in our Python fundamentals courses. Alternatively, if you already have some knowledge of Python you can try this course out and be on your way to becoming a data scientist.

Through comprehensive and hands-on learning, this intermediate course gives you the opportunity to work with object-oriented programming concepts as well as dates and times in Python.

We believe the best way to learn data skills is by application. This is why, starting day one, you’ll write real code. Instead of learning with hundreds of hours of videos, you’ll be learning by writing hundreds of lines of code and solving problems. 

Throughout this course, we’ll teach you the essential techniques for preparing, cleaning, analyzing, and manipulating data in Python. In addition, you’ll hone your skills by formatting strings, working with loops, and managing dates and times with powerful Python libraries. 

Along with these new techniques, you’ll also discover new concepts that you’ll undoubtedly need as a successful data scientist. For example, you’ll become familiar with object-oriented programming, an essential aspect of coding with Python. 

Another great thing about Dataquest is its focus on guided projects to help build compelling portfolios. If you want to get hired fast, a strong portfolio is at the top of the list of things hiring managers look for in a qualified candidate. That’s why virtually every Dataquest course contains a challenging project, which, once complete, will make a great addition to your portfolio.

Additionally, thanks to our vibrant and supportive community of students and professionals, you’ll never learn alone with Dataquest. Our community is always ready to lend a helping hand. And if you’re ever in a bind or need a hand from our powerful support tools, we’re only a click away.

Here’s a glance at our Python for Data Science Intermediate course:

  • This course is the third and final course in the Python Basics for Data Analysis Skill Path. It consists of the five lessons listed below, which cover intermediate data science techniques in Python. 
  • You’ll write real code with dozens of practice problems to validate and apply your skills.
  • At the end of each course, you’ll complete a guided project to reinforce your new knowledge and expand your portfolio.
  • When you complete this course, you’ll receive a certificate that you can share with your professional network.
  • Once you complete this course, you’ll be ready for more advanced Python courses.
  • Engage with our friendly community of data professionals, get feedback on your projects, and keep building your skills. 

Enroll in this Course to learn intermediate data science skills in Python!

Python for Data Science Intermediate Course Lesson List

Cleaning and Preparing Data in Python
Learn how to clean and prepare text data in Python.

Python Data Analysis Basics
Learn the fundamentals of data analysis in Python and how to format text data.

Object-Oriented Python
Learn about using objects, classes, methods, and attributes.

Working With Dates And Times In Python
Learn how to work with and analyze date and time data.

Exploring Hacker News Posts
Practice using loops, cleaning strings, and working with dates in Python.

Who Is The Python for Data Science Intermediate Course for?

Whether you're a beginner or an up-and-coming data professional seeking to advance your skills, we created this course with you in mind.

Here are a few people who would benefit from this Python course:

  • Intermediate Python users looking to advance fundamental data science knowledge
  • People who want a career as a data analyst or data scientist
  • People who want a career as a Python programmer
  • People seeking remote work
  • Anyone who works with data in telecommunication, finance, education, and healthcare
  • Junior data scientists or data analysts who want to advance in their current positions
  • Anyone who wants to be able to capture, process, and interpret data
  • Students who want to develop a competitive portfolio

Students Who Enrolled in this Course Also Enrolled in These Paths:

If you’re learning Python to begin a career in data science, we strongly recommend you enroll in the following paths — they’ll take you from beginner level to job-ready in less than a year!

On the other hand, if you’re already on a data science career path and want to improve your data skills to enhance your work performance and earn a well-deserved promotion, we recommend the following skill paths:

Qualify for In-demand Jobs in Data Science with Python

Proficiency with Python is one of the most in-demand skills of the current job market, and there are no signs that that demand is going away any time soon. The skills you’ll learn in this course are critical components of these careers:

  • Python developer
  • Data analyst
  • Data scientist
  • Data engineer
  • Business analyst
  • Quality assurance engineer
  • Financial analyst
  • Software developer
  • Python full stack developer
  • GIS analyst
  • Machine learning engineer
  • Statistician
  • Software engineer
  • Biotech analyst