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Become a master at building complex reports! In this course, you will apply all the SQL concepts and functions you have learned in previous courses to build out your very own dashboard. By navigating through an Olympics database, you will become an expert data explorer and learn how to understand novel database quickly and effectively. Since data is never perfect, you will gain valuable strategies to deal with real-world issues commonly found with SQL, including how to remove data duplication and how to turn messy data into clean, organized reports. Lastly, you’ll conquer complex calculations using window functions and layered calculations, all within the same report. This is a perfect class for anyone who will be commonly pulling data from databases and is a great complement for those who use R or Python for data science.
Exploring the Olympics DatasetFree
Before you can start building out reports to answer specific questions, you should get familiar with the data. In this chapter, you will learn how to use E:R diagrams and data exploration techniques to get a solid understanding of the data to better answer business-related questions.Case study50 xpIdentifying the base report50 xpBuilding the base report100 xpAthletes vs events by sport100 xpThe Olympics dataset50 xpPlanning queries with an E:R diagram50 xpAge of oldest athlete by region100 xpNumber of events in each sport100 xpExploring our data50 xpExploring summer_games100 xpValidating our query100 xpReport 1: Most decorated summer athletes100 xp
Queries can get large, fast. It's important to take a logical approach when building more complicated queries. In this chapter, you will take a step-by-step approach to plan and build a complex query that requires you to combine tables in multiple ways and create different types of fields.Planning the query50 xpPlanning the SELECT statement50 xpPlanning the filter100 xpCombining tables50 xpJOIN then UNION query100 xpUNION then JOIN query100 xpCreating custom fields50 xpCASE statement refresher100 xpBMI bucket by sport100 xpTroubleshooting CASE statements100 xpFiltering and finishing touches50 xpFiltering with a JOIN100 xpFiltering with a subquery100 xpReport 2: Top athletes in nobel-prized countries100 xp
Cleaning & Validation
Although it would be nice, data in the real-world is rarely stored in an ideal way. Simply put: data can get messy. In chapter 3, you will learn how to deal with this messy data by fixing data type issues, cleaning messy strings, handling nulls, and removing duplication.Converting data types50 xpIdentifying data types100 xpInterpreting error messages100 xpUsing date functions on strings100 xpCleaning strings50 xpString functions100 xpReplacing and removing substrings100 xpFixing incorrect groupings100 xpDealing with nulls50 xpFiltering out nulls100 xpFixing calculations with coalesce100 xpReport duplication50 xpIdentifying duplication100 xpFixing duplication through a JOIN100 xpReport 3: Countries with high medal rates100 xp
The value of reporting really shows when presenting not-so-obvious insights through complex calculations. In this chapter, you will learn how to build more complicated fields by leveraging window functions and layered calculations. You will gain hands-on experience building two advanced calculations in particular: the percent of a total calculation and the performance index calculation.Building complex calculations50 xpTesting out window functions100 xpAverage total country medals by region100 xpMost decorated athlete per region100 xpComparing groups50 xpVolume vs efficiency metrics50 xpPercent of gdp per country100 xpGDP per capita performance index100 xpComparing dates50 xpMonth-over-month comparison100 xpWeek-over-week comparison100 xpReport 4: Tallest athletes and % GDP by region100 xpCourse summary50 xp
In the following tracksSQL for Business Analysts
DatasetsCourse Database Entity Relationship DiagramCourse Database Creation CodeAthletesCountriesCountry StatsSummer GamesWinter Games
PrerequisitesData Manipulation in SQL
Learning & Development Consultant
As a learning & development consultant by day and a freelancer by night, Tyler has two clear passions in life: teaching and data. After receiving a bachelor's in biophysics at Johns Hopkins University, he moved into the world of data and began working in the ad-tech industry, leading two business intelligence teams in the process. He specializes in SQL, data visualization, data modeling, and data analysis.