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Before you can analyze data, you first have to acquire it. This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas, a major Python library for analytics, to get data from a variety of sources, from spreadsheets of survey responses, to a database of public service requests, to an API for a popular review site. Along the way, you’ll learn how to fine-tune imports to get only what you need and to address issues like incorrect data types. Finally, you’ll assemble a custom dataset from a mix of sources.
Importing Data from Flat FilesFree
Practice using pandas to get just the data you want from flat files, learn how to wrangle data types and handle errors, and look into some U.S. tax data along the way.
Importing Data From Excel Files
Automate data imports from that staple of office life, Excel files. Import part or all of a workbook and ensure boolean and datetime data are properly loaded, all while learning about how other people are learning to code.Introduction to spreadsheets50 xpGet data from a spreadsheet100 xpLoad a portion of a spreadsheet100 xpGetting data from multiple worksheets50 xpSelect a single sheet100 xpSelect multiple sheets100 xpWork with multiple spreadsheets100 xpModifying imports: true/false data50 xpSet Boolean columns100 xpSet custom true/false values100 xpModifying imports: parsing dates50 xpParse simple dates100 xpGet datetimes from multiple columns100 xpParse non-standard date formats100 xp
Importing Data from Databases
Combine pandas with the powers of SQL to find out just how many problems New Yorkers have with their housing. This chapter features introductory SQL topics like WHERE clauses, aggregate functions, and basic joins.Introduction to databases50 xpConnect to a database100 xpLoad entire tables100 xpRefining imports with SQL queries50 xpSelecting columns with SQL100 xpSelecting rows100 xpFiltering on multiple conditions100 xpMore complex SQL queries50 xpGetting distinct values100 xpCounting in groups100 xpWorking with aggregate functions100 xpLoading multiple tables with joins50 xpJoining tables100 xpJoining and filtering100 xpJoining, filtering, and aggregating100 xp
Importing JSON Data and Working with APIs
Learn how to work with JSON data and web APIs by exploring a public dataset and getting cafe recommendations from Yelp. End by learning some techniques to combine datasets once they have been loaded into data frames.Introduction to JSON50 xpLoad JSON data100 xpWork with JSON orientations100 xpIntroduction to APIs50 xpGet data from an API100 xpSet API parameters100 xpSet request headers100 xpWorking with nested JSONs50 xpFlatten nested JSONs100 xpHandle deeply nested data100 xpCombining multiple datasets50 xpAppend dataframes100 xpMerge dataframes100 xpWrap-up50 xp
DatasetsVermont tax return data by ZIP codeFreeCodeCamp New Developer Survey response subsetNYC weather and 311 housing complaints
Data scientist via spatial analytics and geography.
A geographer by training, Amany drifted into data science via spatial analytics. These days, they spend a lot of time thinking about how best to structure data and streamline acquisition processes for reporting and analytics, mostly for government agencies and nonprofits. They enjoy demystifying data science and coding concepts.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA