Have you got your basic Python programming chops down for Data Science but are yearning for more? Then this is the course for you. Herein, you'll consolidate and practice your knowledge of lists, dictionaries, tuples, sets, and date times. You'll see their relevance in working with lots of real data and how to leverage several of them in concert to solve multistep problems, including an extended case study using Chicago metropolitan area transit data. You'll also learn how to use many of the objects in the Python Collections module, which will allow you to store and manipulate your data for a variety of Data Scientific purposes. After taking this course, you'll be ready to tackle many Data Science challenges Pythonically.
Data Types for Data Science features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will make you a master in data science with Python!
What you'll learn:
Chapter 1: Fundamental data types
This chapter will introduce you to the fundamental Python data types - lists, sets, and tuples. These data containers are critical as they provide the basis for storing and looping over ordered data. To make things interesting, you'll apply what you learn about these types to answer questions about the New York Baby Names dataset!
Chapter 2: Dictionaries - the root of Python
At the root of all things Python is a dictionary. Herein, you'll learn how to use them to safely handle data that can be viewed in a variety of ways to answer even more questions about the New York Baby Names dataset. You'll explore how to loop through data in a dictionary, access nested data, add new data, and come to appreciate all of the wonderful capabilities of Python dictionaries.
Chapter 3: Meet the collections module
The collections module is part of Python's standard library and holds some more advanced data containers. You'll learn how to use the Counter, defaultdict, OrderedDict and namedtuple in the context of answering questions about the Chicago transit dataset.
Chapter 4: Handling Dates and Times
Handling times can seem daunting at times, but here, you'll dig in and learn how to create datetime objects, print them, look to the past and to the future. Additionally, you'll learn about some third party modules that can make all of this easier. You'll continue to use the Chicago Transit dataset to answer questions about transit times.
Chapter 5: Answering Data Science Questions
Finally, time for a case study to reinforce all of your learning so far! You'll use all the containers and data types you've learned about to answer several real world questions about a dataset containing information about crime in Chicago.
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