As data collection has increased exponentially, so has the need for people skilled at using and interacting with data; to be able to think critically, and provide insights to make better decisions and optimize their businesses. This is a data scientist, “part mathematician, part computer scientist, and part trend spotter” (SAS Institute, Inc.). According to Glassdoor, being a data scientist is the best job in America; with a median base salary of $110,000 and thousands of job openings at a time. The skills necessary to be a good data scientist include being able to retrieve and work with data, and to do that you need to be well versed in SQL, the standard language for communicating with database systems.
This course is part of the Learn SQL Basics for Data Science Specialization
Offered By
About this Course
Learner Career Outcomes
15%
21%
12%
What you will learn
Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results.
Use SQL commands to filter, sort, and summarize data.
Create an analysis table from multiple queries using the UNION operator.
Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis.
Skills you will gain
- Data Science
- Data Analysis
- Sqlite
- SQL
Learner Career Outcomes
15%
21%
12%
Offered by

University of California, Davis
UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, veterinary medicine, sustainability, environmental and biological sciences, and technology. With four colleges and six professional schools, UC Davis and its students and alumni are known for their academic excellence, meaningful public service and profound international impact.
Syllabus - What you will learn from this course
Getting Started and Selecting & Retrieving Data with SQL
In this module, you will be able to define SQL and discuss how SQL differs from other computer languages. You will be able to compare and contrast the roles of a database administrator and a data scientist, and explain the differences between one-to-one, one-to-many, and many-to-many relationships with databases. You will be able to use the SELECT statement and talk about some basic syntax rules. You will be able to add comments in your code and synthesize its importance.
Filtering, Sorting, and Calculating Data with SQL
In this module, you will be able to use several more new clauses and operators including WHERE, BETWEEN, IN, OR, NOT, LIKE, ORDER BY, and GROUP BY. You will be able to use the wildcard function to search for more specific or parts of records, including their advantages and disadvantages, and how best to use them. You will be able to discuss how to use basic math operators, as well as aggregate functions like AVERAGE, COUNT, MAX, MIN, and others to begin analyzing our data.
Subqueries and Joins in SQL
In this module, you will be able to discuss subqueries, including their advantages and disadvantages, and when to use them. You will be able to recall the concept of a key field and discuss how these help us link data together with JOINs. You will be able to identify and define several types of JOINs, including the Cartesian join, an inner join, left and right joins, full outer joins, and a self join. You will be able to use aliases and pre-qualifiers to make your SQL code cleaner and efficient.
Modifying and Analyzing Data with SQL
In this module, you will be able to discuss how to modify strings by concatenating, trimming, changing the case, and using the substring function. You will be able to discuss the date and time strings specifically. You will be able to use case statements and finish this module by discussing data governance and profiling. You will also be able to apply fundamental principles when using SQL for data science. You'll be able to use tips and tricks to apply SQL in a data science context.
Reviews
- 5 stars73.63%
- 4 stars20.29%
- 3 stars3.50%
- 2 stars1.27%
- 1 star1.28%
TOP REVIEWS FROM SQL FOR DATA SCIENCE
The contents and explanations on the course are good, even for people with no previews coding experience (like me)... Would be great to have a better structured way of work for the final excercise
Did not need to be a peer review assessment, most of the tests lacked creativity and could be automated. The peer review component should just apply to the creative components that can be expanded.
Thank you for the material that was presented well and clearly. I got to know better how to use SQL for data science. Hopefully, by learning this, I can implement it in the future as a Data Analyst.
The course is nice but I suppose the PowerPoint presentation that accompanies it could use some effects to show us the clauses one by one, as they are spoke. That would make understanding them easier.
About the Learn SQL Basics for Data Science Specialization
This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets.

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