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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):
Not for you? No problem.
30 day money back guarantee.
Learn on the go.
Desktop, iOS and Android.
Certificate of completion.
|Section 1: Why you should stop depending on engineers and learn SQL|
Why you should stop depending on engineers and learn SQLPreview
Outline of this coursePreview
|Section 2: Survey of SQL databases and Installation of SQLite|
Overview of SQL databasesPreview
Installing SQLite on Mac, Linux, and WindowsPreview
|Section 3: What is a relational database? Basic commands|
What’s a relational database?Preview
How to load the data used in this classPreview
Querying a table
Creating a table
Modifying a table’s structure
|Section 4: Indexes and speed comparison|
Speeding things up with indexes
Index Example in the Console
|Section 5: Modifying a table's data|
Insert / Update / Delete
What is CRUD?
|Section 6: Joining tables|
Joining or Merging tables together
Joins in the console
|Section 7: Aggregating, grouping, and sorting. Real marketing queries.|
Count, Distinct, Sum, Min, Max, Avg
Group by, Sort, Limit
Funnels, YOY revenue, and Sales by Location
|Section 8: Advanced: SQL on Spark|
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.
Create your own Spark cluster on Amazon EC2
|Section 9: Further Knowledge, Practice and Exercises|
How to load the extra dataset (tab-separated tables)
The "IN" Keyword
The "BETWEEN" Keyword
Interview Question-Style Exercises
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.