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About this course

In this course, you’ll continue to learn the fundamentals of Python for data science. We divided these courses into four parts to make mastering the fundamentals quicker and easier. This third part builds on the knowledge you acquired in our Variables, Data Types, and Lists in Python course and our For Loops and Conditional Statements in Python course.

The demand for data science has never been higher — take advantage of this opportunity, and level up your career with Dataquest.

This course focuses on the following:

  • Developing additional data science fundamentals in Python like dictionaries and functions
  • Discovering how to build frequency tables
  • Performing real-world data analysis tasks in an interactive coding environment

Lessons in this course

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How Our Dictionaries, Frequency Tables, and Functions in Python Course Works

Through hands-on coding lessons, learners will master the basics of Python programming for data science. Most platforms teach via video-based lessons, especially at the beginner level. At Dataquest, we understand that in order to gain a truly competitive edge, you must begin coding at the start.

In order to take this course, learners must first complete our Variables, Data Types, and Lists in Python course and our For Loops and Conditional Statements in Python course. This will allow you to master key concepts such as defining default arguments, mutable and immutable data, and other tasks data analysts perform on a regular basis.

Another advantage of the Dataquest teaching method is our unique guided projects and practice problems. Both of these assets help improve your skills and expand your portfolio, which will improve your chances of impressing a hiring manager.

Finally, with Dataquest, you’ll never learn alone. We have a vast network of students and professionals who are eager to lend a helping hand. If our robust support tools and active community aren’t able to resolve your problems, we have a team of community managers on standby, ready to serve.

Here’s a glance at part three of our Python for Data Science Fundamentals course:

  • This course consists of the four lessons listed below, which provide an introduction to data science using Python.
  • You’ll write real code with dozens of practice problems to validate and apply your skills.
  • At the end of the 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.

Who Is This Course for?

This course can benefit virtually anyone. Whether you’re a beginner looking to get into the field or an intermediate user who wants to brush up on the fundamentals, you’ll find what you’re looking for right here.

Here are a few examples of people who might want to take advantage of this Python course:

  • Students who just finished the first and second course
  • College students in the data science field who want to be one step ahead of their peers
  • People who want a career as a data analyst or data scientist
  • People seeking remote work in one of the most in-demand industries in the world
  • Anyone who works with data in telecommunication, finance, education, and healthcare
  • Junior data scientists or data analysts who want to advance in their current position
  • Students who want to develop a competitive portfolio to help them get hired
  • Python users who want to fill in the blanks and brush up on the basics

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