Complete SQL Bootcamp for Data Science, Analytics, Marketing
What you'll learn
- Understand what a relational database is
- How to install SQL on Mac, Linux, or Windows
- How to create a table
- How to import data into a table
- How to query a database table using SQL
- How to insert into, update, and delete from a table
- Speed things up using indexes
- Join tables together to merge data
- Aggregate data using count, sum, and average
- Determine where in the sales funnel customers are being lost
- Chart your year over year revenue
- Group and sort sales by location
- Use SQL on Spark
- Install Spark
- Create a Spark cluster on AWS EC2
- You should you how to open a terminal / command line shell. All the examples will be done here.
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, The Complete SQL Bootcamp for Data Science, Analytics, and Marketing, 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 on SQL! Dominate data analytics, data science, and big data!
TIPS (for getting through the course):
Watch it at 2x to learn twice as fast!
Take handwritten notes. This will drastically increase your ability to retain the information.
Ask lots of questions on the discussion board. The more the better!
Write code yourself, don't just sit there and look at my code.
Who this course is for:
- This course is for not only marketers but anyone who wants to be able to answer data-related questions on their own
- Students who want a different approach to learning SQL
- Professionals who are exposed to data but can't yet leverage its power
- Developers who build web-applications but don't yet know how to use a database backend
- Product managers who want to make data-driven decisions
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.
Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.
I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we 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, Hunter College, and The New School.