Interactive Course

Data Types for Data Science

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

  • 4 hours
  • 18 Videos
  • 58 Exercises
  • 14,006 Participants
  • 4,850 XP

Loved by learners at thousands of top companies:

rei-grey.svg
ea-grey.svg
deloitte-grey.svg
forrester-grey.svg
3m-grey.svg
intel-grey.svg

Course Description

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.

  1. 1

    Fundamental data types

    Free

    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!

  2. 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.

  3. Answering Data Science Questions

    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. Have fun!

  4. 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 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.

  5. Handling Dates and Times

    Handling times can seem daunting at time, 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.

  1. 1

    Fundamental data types

    Free

    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!

  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 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.

  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.

  4. Handling Dates and Times

    Handling times can seem daunting at time, 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.

  5. Answering Data Science Questions

    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. Have fun!

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Jason Myers
Jason Myers

Co-Author of Essential SQLAlchemy and Software Engineer

Jason Myers is a software engineer and author. His area of expertise is in developing data analytics platforms. He has also written the Essential SQLAlchemy book, co-authored with Rick Copeland, that introduces you to working with relational databases in Python.

See More
Icon Icon Icon professional info