Duke University
Data Science with NumPy, Sets, and Dictionaries
Duke University

Data Science with NumPy, Sets, and Dictionaries

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

30 hours to complete
3 weeks at 10 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Assessments

4 quizzes

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There are 4 modules in this course

This week, you will learn the basics of object oriented programming as well as how to use sets and dictionaries to store and work with data in Python. You will apply these concepts with Python to perform some mathematical operations and analytical tasks, including solving geometric problems with circles and counting words in a document.

What's included

10 videos4 readings4 programming assignments

This week, you will learn how to utilize NumPy--one of the most useful Python packages we use in data science--as well as learn additional data structures, arrays, beginning with the simplest type of an array, a vector. With NumPy and your new understanding of vectors, you will develop histograms as well as analyze household income distribution data in the United States, drawing your own data-driven conclusions.

What's included

1 video9 readings2 quizzes3 ungraded labs

This week, you will first learn how NumPy handles data in your program using views and copies of your data. You will then learn how to work with more complex arrays called matrices, as well as how you can subset, filter, and modify data in matrices. Finally, you will write your own programs to manipulate data matrices and report your results for a given dataset.

What's included

1 video14 readings1 quiz3 ungraded labs

You will learn this week how to use NumPy to summarize data from matrices (e.g., calculating averages, minimums, maximums, etc.) as well as how to begin to analyze and manipulate image data. You will also explore two new data science techniques: how to make your analysis of data matrices more computationally efficient (vectorization) and how to randomize data (randomization).

What's included

1 video11 readings1 quiz2 ungraded labs

Instructors

Genevieve M. Lipp
Duke University
8 Courses249,599 learners

Offered by

Duke University

Recommended if you're interested in Data Analysis

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