SQL for Data Analytics
This course provides you with an overview of Structured Query Language (SQL) so that you can quickly begin working with and analyzing data with other data science tools. Before you can analyze data, you need to have the correct data. Many organizations store their data in structured databases and SQL is the language of choice to extract, manipulate, filter, and generally wrangle that data.
Topics covered include:
- SQL syntax
- Selecting data
- Filtering and sorting data
- Importing and exporting data from CSV and Excel
- Data normalization
- Differences between SQL and NoSQL databases
Who Should Take This Course
Anyone that wishes to work in data analytics.
This course assumes no previous knowledge of SQL. Students should be comfortable working at the command line in Windows, Linux, or Mac.
Why You Should Take This Course
Upon completing this course, students will be able to:
- Select data based on values within fields
- Work with patterns to match multiple values
- Join tables and queries together to answer questions
- Analyze data and find basic statistics with SQL aggregate functions
- Import and export data for use with analysis tools such as the Python pandas library
- Create data models
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Register 21 days before class start date and save $250! Enter discount code EARLY250 during registration.
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.