Complete SQL Bootcamp for Data Science, Analytics, Marketing

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Lazy Programmer Inc.

1:37:33

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1 - Why you should stop depending on engineers and learn SQL

1 - Introduction and Outline.mp4

07:12

2 - How to Succeed in this Course.mp4

03:13

2 - Survey of SQL databases and Installation of SQLite

3 - Overview of SQL databases.mp4

02:43

4 - Installing SQLite on Mac Linux and Windows.mp4

01:07

5 - Visual Walkthrough of Windows Installation.mp4

03:13

3 - What is a relational database Basic commands

6 - Whats a relational database.mp4

01:08

7 - Github Link.txt

7 - How to load the data used in this class.mp4

02:22

8 - Basic commands.mp4

01:07

9 - Querying a table.mp4

02:29

10 - Creating a table.mp4

01:29

11 - Importing Data on Windows.mp4

04:13

12 - Modifying a tables structure.mp4

02:33

13 - Suggestion Box.mp4

03:10

4 - Indexes and speed comparison

14 - Speeding things up with indexes.mp4

03:07

15 - Index Example in the Console.mp4

02:37

5 - Modifying a tables data

16 - Insert Update Delete.mp4

02:51

17 - What is CRUD.mp4

01:29

6 - Joining tables

18 - Joining or Merging tables together.mp4

02:18

19 - Joins in the console.mp4

01:21

7 - Aggregating grouping and sorting Real marketing queries

20 - Count Distinct Sum Min Max Avg.mp4

01:18

21 - Group by Sort Limit.mp4

02:06

22 - Funnels YOY revenue and Sales by Location.mp4

04:43

8 - Advanced SQL on Spark Optional

23 - Spark SQL.mp4

04:39

24 - Create your own Spark cluster on Amazon EC2.mp4

02:36

9 - Further Knowledge Practice and Exercises

25 - How to load the extra dataset tabseparated tables.mp4

06:19

26 - The IN Keyword.mp4

02:42

27 - The BETWEEN Keyword.mp4

01:43

28 - Interview QuestionStyle Exercises.mp4

05:50

10 - Appendix FAQ

29 - How to Succeed in this Course Long Version.mp4

10:24

30 - BONUS.mp4

05:31

Description

Master SQL for Job Interview Preparation, Data Analysis, Big Data, and Business Intelligence

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

Who is this 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

What You Need to Know?

You should you how to open a terminal / command line shell. All the examples will be done here.

More details

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.
Iâ€™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

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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. 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.

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