
Explore basic sql concepts, including selecting data with from and where, and understand primary and foreign keys, relationships, and joins across one-to-many, one-to-one, and many-to-many structures.
Explore using a safe lab to write queries against a sample database and navigate the setup. Practice joins that count languages represented in films and inventory for analysis.
Develop resourceful data skills by reviewing lab questions that cover count, where, order by desc, casting to float, aggregates, joins, and views in SQL.
Explore when to use views, functions, or stored procedures to persist and reuse SQL logic; this lab introduces views as virtual tables in PostgreSQL.
Create and install a postgres view named customers by total spent to analyze lifetime value, using case and group by, and query it like a table to identify top customers.
Explore how to pull data from a database into a pandas data frame using Python, introduce Jupyter Lab, and explain why writing actual SQL statements isn't central in this lab.
Discover how to process big data in memory with Apache Spark, Databricks, and Pae Spark, using Spark SQL API and notebook workflows to spin up clusters.
Learn to spin up a spark cluster in Databricks, create a data table via the sequel API, and run notebooks with PySpark to analyze a dataset.
The problem with online learning and self-guided learning, especially in programming, is that you are often not certain exactly where to get started on your journey. But you do know that having to run to the IT department whenever you need data pulled, or need a dataset to be created, can sometimes leave you stranded.
SQL, as an easy programming language designed for interacting with relational databases, is your starting point when it comes to anything data related. The user base of SQL is so deep that many big data technologies utilize some kind of port of SQL.
The problem with other SQL for data science classes is that, by only covering SQL itself, they leave you hanging on your journey to data science mastery, much like when you have to rely on IT to meet your data needs.
With Mass Street University, that journey does not end with bare bones knowledge of SQL. DSCI100: SQL Crash Course for Data Science is designed to quickly get you up to speed on the basics of using SQL. But this course does more than give you the SQL foundation you need to work with data.
In DSCI100, we extend your journey by teaching you some real data science. By demonstrating how Python and Apache Spark interact with SQL in hands-on labs, we teach you how SQL is actually used in a data science workflow once you have mastered the basics.
At the end of the course, you will be able to work with relational databases to pull the necessary data you need to perform complex analyses…without the assistance of IT. You will also have a good understanding of how to use SQL with common data science languages.
No other course will grant you the tools and fuel that allow you to take control of your journey. Enroll today for a real, self-sufficient path to data science excellence.