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
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1 个测验,3 项作业
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该课程共有4个模块
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.
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10个视频5篇阅读材料4个编程作业
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.
涵盖的内容
1个视频9篇阅读材料2个作业3个非评分实验室
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.
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1个视频14篇阅读材料1个测验3个非评分实验室
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).
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1个视频12篇阅读材料1个作业2个非评分实验室
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已于 Aug 16, 2025审阅
The course was really very interesting and Was very Useful and The concepts was clear with Implementation
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Logical Operations

University of Michigan








