Duke University

Pandas for Data Science

Taught in English

Course

Gain insight into a topic and learn the fundamentals

Genevieve M. Lipp
Nick Eubank
Kyle Bradbury

Instructors: Genevieve M. Lipp

Beginner level

Recommended experience

41 hours to complete
3 weeks at 13 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • How and when to leverage the Pandas library for your data science projects

  • Best practices for cleaning, manipulating, and optimizing data with Pandas

Details to know

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Recently updated!

April 2024

Assessments

1 quiz, 8 assignments

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There are 4 modules in this course

This week, you will learn how to read data from files into your python program, and write that corresponding data to a file. We’ll be working primarily with string-type data in this unit and will give special attention to the way that python handles strings. Additionally we’ll go over some basic debugging in python using exception traces, and you’ll leverage these to create your own python program that is capable of reading and writing to a file.

What's included

5 videos7 readings3 assignments3 programming assignments

This Week, you’ll learn how to begin to utilize Pandas, one of the most commonly used libraries in Data Science with python. Pandas is predominantly used for working with tabular data. By the end of this week you’ll be able to identify the hallmarks and quirks of working with tabular data, describe the benefits and limitations of using Pandas, and be able to perform some basic data manipulation techniques in Pandas.

What's included

1 video9 readings2 assignments3 ungraded labs

This week, you will learn how to perform basic file operations in Pandas, as well as how to clean up large datasets. You’ll learn to read and write from common tabular file formats, and Pandas-specific intricacies for working with that data. Additionally, you’ll learn best practices for cleaning your data.

What's included

1 video13 readings1 quiz2 assignments4 ungraded labs

This week you will learn how to combine datasets from different sources. Pandas has different methods of combining data depending on your preferred outcome, and you’ll be able to differentiate between when to use each kind. Additionally, we’ll go over computationally efficient ways of querying your data, which, while similar to selecting data via subsetting in its outcomes, has a distinct set of advantages.

What's included

1 video11 readings1 assignment5 ungraded labs

Instructors

Genevieve M. Lipp
Duke University
9 Courses251,908 learners

Offered by

Duke University

Recommended if you're interested in Data Analysis

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