It is becoming ever more important that companies make data-driven decisions.
With big data and data science on the rise, we have more data than we know what to do with.
One of the basic languages of data analytics is SQL, which is used for many popular databases including MySQL, Postgres, SQLite, Microsoft SQL Server, Oracle, and even big data solutions like Hive and Cassandra.
I’m going to let you in on a little secret. Most high-level marketers and product managers at big tech companies know how to manipulate data to gain important insights. No longer do you have to wait around the entire day for some software engineer to answer your questions - now you can find the answers directly, by yourself, using SQL!
In this course, SQL for marketers, we'll start from the basics - installing SQL onto your Mac, Linux, or Windows machine and explaining what a relational database is. Next, we'll look at basic tasks like creating tables and loading data into those tables. We will look at a wide variety of SQL commands and I will show you how to speed things up using indexes.
Once you know all the SQL commands we will start doing advanced examples - answering questions marketers and business people often have, like where are customers dropping off in our sales funnel? And which of our locations has the highest revenue?
In the last section, we'll do Advanced SQL queries on Spark, the big data framework that is the successor to MapReduce and also runs on top of Hadoop. I will teach you how to install Spark, create a cluster very quickly on Amazon EC2, and run SQL queries, allowing you to apply everything you learned up until this point in a big data environment.
Do you want to know how to optimize your sales funnel using SQL, look at the seasonal trends in your industry, and run a SQL query on Hadoop? Then join me now in my new class, SQL for marketers! Dominate data analytics, data science, and big data!
All the code for this course can be downloaded from my github: /lazyprogrammer/sql_class
Make sure you always "git pull" so you have the latest version!
TIPS (for getting through the course):
How to install Spark locally, how to load data into Spark for making SQL queries, and some boilerplate code for writing any SQL query on a Spark table.
I am a data scientist, big data engineer, and full stack software engineer.
For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.
I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.