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Manipulating Time Series Data in R: Case Studies

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

  • 4 hours
  • 12 Videos
  • 50 Exercises
  • 5,417 Participants
  • 4,000 XP

This course is part of these tracks:

Lore Dirick
Lore Dirick

Senior Data Science Curriculum Writer at Flatiron School

Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is now a senior Data Science Curriculum Writer at Flatiron School, a coding bootcamp in NYC.

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Matt Isaacs
Matt Isaacs

Political Science PhD interested in data science in defense, security, and international relations

Matt Isaacs is a former Course Development Intern at DataCamp . Matt holds a PhD in Political Science from Brandeis University and has extensive experience in applied data science across the public sector with a focus on analytics in defense, security, and international relations.

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Course Description

This follow-up course on manipulating time series data in R does not cover new data manipulation concepts. Instead, you will strengthen your knowledge of the topics covered in Manipulating Time Series Data in R with xts & zoo using new exercises and interesting datasets.

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

Course Outline

  1. 1

    Flight Data

    Free

    You've been hired to understand the travel needs of tourists visiting the Boston area. As your first assignment on the job, you'll practice the skills you've learned for time series data manipulation in R by exploring data on flights arriving at Boston's Logan International Airport (BOS) using xts & zoo.

  2. Weather Data

    In this chapter, you'll expand your time series data library to include weather data in the Boston area. Before you can conduct any analysis, you'll need to do some data manipulation, including merging multiple xts objects and isolating certain periods of the data. It's a great opportunity for more practice!

  3. Economic Data

    Now it's time to go further afield. In addition to flight delays, your client is interested in how Boston's tourism industry is affected by economic trends. You'll need to manipulate some time series data on economic indicators, including GDP per capita and unemployment in the United States in general and Massachusetts (MA) in particular.

  4. Sports Data

    Having exhausted other options, your client now believes Boston's tourism industry must be related to the success of local sports teams. In your final task on this project, your supervisor has asked you to assemble some time series data on Boston's sports teams over the past few years.

Course Instructor

Lore Dirick
Lore Dirick

Senior Data Science Curriculum Writer at Flatiron School

Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is now a senior Data Science Curriculum Writer at Flatiron School, a coding bootcamp in NYC.

See More
Matt Isaacs
Matt Isaacs

Political Science PhD interested in data science in defense, security, and international relations

Matt Isaacs is a former Course Development Intern at DataCamp . Matt holds a PhD in Political Science from Brandeis University and has extensive experience in applied data science across the public sector with a focus on analytics in defense, security, and international relations.

See More

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