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Python Data Science Toolbox (Part 2)

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4 hours
3,800 XP
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Course Description

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.
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  1. 1

    Using iterators in PythonLand


    You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you’ll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data—in this case, data from Twitter that you will load in chunks using iterators.

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    Introduction to iterators
    50 xp
    Iterators vs. Iterables
    50 xp
    Iterating over iterables (1)
    100 xp
    Iterating over iterables (2)
    100 xp
    Iterators as function arguments
    100 xp
    Playing with iterators
    50 xp
    Using enumerate
    100 xp
    Using zip
    100 xp
    Using * and zip to 'unzip'
    100 xp
    Using iterators to load large files into memory
    50 xp
    Processing large amounts of Twitter data
    100 xp
    Extracting information for large amounts of Twitter data
    100 xp
    50 xp
  2. 2

    List comprehensions and generators

    In this chapter, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists—and lists of lists—in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python data science toolbox. You'll then learn about generators, which are extremely helpful when working with large sequences of data that you may not want to store in memory, but instead generate on the fly.

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In the following tracks

Associate Data Scientist Python DeveloperPython Fundamentals


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Yashas Roy
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Francisco Castro
Hugo Bowne-Anderson HeadshotHugo Bowne-Anderson

Data Scientist

Hugo is a data scientist, educator, writer and podcaster formerly at DataCamp. His main interests are promoting data & AI literacy, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. If you want to know what he likes to talk about, definitely check out DataFramed, the DataCamp podcast, which he hosted and produced.
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