Interactive Course

Intermediate Spreadsheets for Data Science

Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.

  • 4 hours
  • 12 Videos
  • 48 Exercises
  • 9,371 Participants
  • 4,150 XP

Loved by learners at thousands of top companies:

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Course Description

This course will expand your Google Sheets vocabulary. You'll dive deeper into data types, practice manipulating numeric and logical data, explore missing data and error types, and calculate some summary statistics. As you go, you'll explore datasets on 100m sprint world records, asteroid close encounters, benefit claims, and butterflies.

  1. 1

    What's in a cell?

    Free

    In which you learn to interrogate cells to determine the data type of their contents, and to convert between data types.

  2. Working with numbers

    In which you learn to apply log and square root transformations to numbers, round them up and down, and generate random numbers.

  3. Logic & Errors

    In which you learn how to work with logical data consisting of TRUE and FALSE values, and how to handle missing values and errors.

  4. Positional Matching

    In which you learn about cell addresses, advanced matching, sorting and filtering, and simple imputation.

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

Richie Cotton
Richie Cotton

Head of Content Quality at DataCamp

Richie runs the Content Quality team at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.

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