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Database Clinic: SQLite

Overview of solution

From the course: Database Clinic: SQLite

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  • Course details

    SQLite is a powerful embedded database engine that's a core storage technology in Android and iOS applications. In this installment of the Database Clinic series—in which experts and their databases of choice are pitted against a series of the same challenges— Mark Niemann-Ross demonstrates how to leverage SQLite to solve common database problems. After providing a brief overview of the strengths and weaknesses of SQLite, Mark explains how to create a database and populate it using a public dataset. He also shows how to use a SQLite database with programming languages such as Python and R, handle subqueries and queries in SQLite, and more.

    Instructor

    • Click here to view Mark Niemann-Ross’ instructor page

      Mark Niemann-Ross

      Science fiction author and educator at LinkedIn learning

      • Mark Niemann-Ross is a technologist with experience in hardware, software, and science fiction.

        Mark has been helping developers navigate APIs for almost 30 years, and has been responsible for third-party programs at Quark and Adobe. In addition to hands-on technology, he's also been involved in technology education, starting with a degree in industrial education and most recently working as a content manager for LinkedIn Learning.

        Mark's science fiction has most recently appeared in Analog Science Fiction and Fact. He's currently working on a murder mystery solved by a refrigerator.

    Skills covered in this course

  • Welcome

    - [Instructor] This last problem is the most difficult of the database clinic problem set. It involves not only database manipulation but also some data science forecasting. At this point, I assume that you understand basics of SQL such as SELECT and FROM, JOIN, and GROUP BY, so I won't spend a lot of time explaining those parts of the code. When I first considered this problem, I found it useful to carefully think about what it was I was being asked to do. That sounds obvious, but it's easy to get diverted away from the actual problem. When you're doing data science, be sure you know what you're being asked to discover. In this case, we're being asked for a forecast of an educational demand for California up to the year of 2060. To do this, we're given two datasets. We've seen the first dataset before, the California Population Projection, and that provides population forecasts up to 2060. This is a large file, almost 250 megabytes and 3.5 million records. The size of this data is…

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Contents