Data Analytics: SQL for newbs, beginners and marketers
4.5 (115 ratings)
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Data Analytics: SQL for newbs, beginners and marketers

Dominate data analytics, data science, and big data
4.5 (115 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
935 students enrolled
Last updated 11/2016
English
Current price: $10 Original price: $60 Discount: 83% off
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Includes:
  • 1 hour on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
Know how to answer all of their marketing-related questions using a SQL query
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 table
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
View Curriculum
Requirements
  • You should you how to open a terminal / command line shell. All the examples will be done here.
Description

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!


NOTES:

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):

  • Watch it at 2x.
  • 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 is the target audience?
  • 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
Students Who Viewed This Course Also Viewed
Curriculum For This Course
Expand All 25 Lectures Collapse All 25 Lectures 01:04:11
+
Why you should stop depending on engineers and learn SQL
2 Lectures 03:30
+
Survey of SQL databases and Installation of SQLite
2 Lectures 04:36
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What is a relational database? Basic commands
6 Lectures 10:55


Basic commands
01:07

Querying a table
02:25

Creating a table
01:28

Modifying a table’s structure
02:29
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Indexes and speed comparison
2 Lectures 05:43
Speeding things up with indexes
03:07

Index Example in the Console
02:36
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Modifying a table's data
2 Lectures 04:16
Insert / Update / Delete
02:47

What is CRUD?
01:29
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Joining tables
2 Lectures 03:39
Joining or Merging tables together
02:18

Joins in the console
01:21
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Aggregating, grouping, and sorting. Real marketing queries.
3 Lectures 07:53
Count, Distinct, Sum, Min, Max, Avg
01:18

Group by, Sort, Limit
02:02

Funnels, YOY revenue, and Sales by Location
04:33
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Advanced: SQL on Spark
2 Lectures 07:11

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.

Spark SQL
04:35

Create your own Spark cluster on Amazon EC2
02:36
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Further Knowledge, Practice and Exercises
4 Lectures 16:28
How to load the extra dataset (tab-separated tables)
06:19

The "IN" Keyword
02:38

The "BETWEEN" Keyword
01:41

Interview Question-Style Exercises
05:50
About the Instructor
Lazy Programmer Inc.
4.6 Average rating
6,200 Reviews
34,968 Students
18 Courses
Data scientist and big data engineer

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. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.