Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators.
Data Science with NumPy, Sets, and Dictionaries
Data Science with NumPy, Sets, and Dictionaries
Instructors: Genevieve M. Lipp
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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
1 quiz, 3 assignments
Taught in English
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There are 4 modules in this course
This module, 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 module, 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 assignments3 ungraded labs
This module, 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
In this module, you will learn 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 assignment2 ungraded labs
Offered by
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