Data Science A-Z™: Real-Life Data Science Exercises Included

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Data Science A-Z™: Real-Life Data Science Exercises Included

Course Description
Extremely Hands-On… Incredibly Practical… Unbelievably Real!

This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.

In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities – you name it!

This course will give you a full overview of the Data Science journey. Upon completing this course you will know:

How to clean and prepare your data for analysis
How to perform basic visualisation of your data
How to model your data
How to curve-fit your data
And finally, how to present your findings and wow the audience
This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry… But you won’t give up! You will crush it. In this course you will develop a good understanding of the following tools:
SQL
SSIS
Tableau
Gretl
This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.

Or you can do the whole course and set yourself up for an incredible career in Data Science.

The choice is yours. Join the class and start learning today!

See you inside,

Sincerely,

Kirill Eremenko

What are the requirements?
Only a passion for success
All software used in this course is either available for Free or as a Demo version
What am I going to get from this course?
Successfully perform all steps in a complex Data Science project
Create Basic Tableau Visualisations
Perform Data Mining in Tableau
Understand how to apply the Chi-Squared statistical test
Apply Ordinary Least Squares method to Create Linear Regressions
Assess R-Squared for all types of models
Assess the Adjusted R-Squared for all types of models
Create a Simple Linear Regression (SLR)
Create a Multiple Linear Regression (MLR)
Create Dummy Variables
Interpret coefficients of an MLR
Read statistical software output for created models
Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
Create a Logistic Regression
Intuitively understand a Logistic Regression
Operate with False Positives and False Negatives and know the difference
Read a Confusion Matrix
Create a Robust Geodemographic Segmentation Model
Transform independent variables for modelling purposes
Derive new independent variables for modelling purposes
Check for multicollinearity using VIF and the correlation matrix
Understand the intuition of multicollinearity
Apply the Cumulative Accuracy Profile (CAP) to assess models
Build the CAP curve in Excel
Use Training and Test data to build robust models
Derive insights from the CAP curve
Understand the Odds Ratio
Derive business insights from the coefficients of a logistic regression
Understand what model deterioration actually looks like
Apply three levels of model maintenance to prevent model deterioration
Install and navigate SQL Server
Install and navigate Microsoft Visual Studio Shell
Clean data and look for anomalies
Use SQL Server Integration Services (SSIS) to upload data into a database
Create Conditional Splits in SSIS
Deal with Text Qualifier errors in RAW data
Create Scripts in SQL
Apply SQL to Data Science projects
Create stored procedures in SQL
Present Data Science projects to stakeholders
What is the target audience?
Anybody with an interest in Data Science
Anybody who wants to improve their data mining skills
Anybody who wants to improve their statistical modelling skills
Anybody who wants to improve their data preparation skills
Anybody who wants to improve their Data Science presentation skills

Curriculum

Section 1: Get Excited
Welcome to Data Science A-Z™
04:41
Section 2: What is Data Science?
Intro (what you will learn in this section)
00:44
Profession of the future
06:58
Areas of Data Science
05:58
IMPORTANT: Course Pathways
05:52
Section 3: ————————— Part 1: Visualisation —————————
Welcome to Part 1
01:57
Section 4: Introduction to Tableau
Intro (what you will learn in this section)
00:28
Installing Tableau Desktop and Tableau Public (FREE)
06:08
Challenge description + view data in file
02:32
Connecting Tableau to a Data file – CSV file
05:17
Navigating Tableau – Measures and Dimensions
08:42
Creating a calculated field
06:14
Adding colours
07:37
Adding labels and formatting
11:00
Exporting your worksheet
07:40
Section Recap
03:34
Tableau Basics
5 questions
Section 5: How to use Tableau for Data Mining
Intro (what you will learn in this section)
00:44
Get the Dataset + Project Overview
07:12
Connecting Tableau to an Excel File
03:56
How to visualise an ad-hoc A-B test in Tableau
06:29
Working with Aliases
04:05
Adding a Reference Line
04:53
Looking for anomalies
08:35
Handy trick to validate your approach / data
09:13
Section Recap
05:04
Section 6: Advanced Data Mining With Tableau
Intro (what you will learn in this section)
00:44
Creating bins & Visualizing distributions
09:55
Creating a classification test for a numeric variable
04:25
Combining two charts and working with them in Tableau
08:31
Validating Tableau Data Mining with a Chi-Squared test
10:29
Chi-Squared test when there is more than 2 categories
08:15
Visualising Balance and Estimated Salary distribution
11:04
Bonus: Chi-Squared Test (Stats Tutorial)
19:12
Bonus: Chi-Squared Test Part 2 (Stats Tutorial)
09:10
Section Recap
05:44
Part Completed
01:38
Section 7: ————————— Part 2: Modelling —————————
Welcome to Part 2
03:54
Section 8: Stats Refresher
Intro (what you will learn in this section)
00:29
Types of variables: Categorical vs Numeric
05:26
Types of regressions
08:09
Ordinary Least Squares
03:11
R-squared
05:11
Adjusted R-squared
09:56
Section 9: Simple Linear Regression
Intro (what you will learn in this section)
00:37
Introduction to Gretl
02:34
Get the dataset
04:03
Import data and run descriptive statistics
04:25
Reading Linear Regression Output
06:48
Plotting and analysing the graph
04:22
Section 10: Multiple Linear Regression
Intro (what you will learn in this section)
01:15
Caveat: assumptions of a linear regression
01:47
Get the dataset
04:12
Dummy Variables
08:05
Dummy Variable Trap
02:10
Ways to build a model: BACKWARD, FORWARD, STEPWISE
15:41
Backward Elimination – Practice time
16:08
Using Adjusted R-squared to create Robust models
10:17
Interpreting coefficients of MLR
12:47
Section Recap
04:15
Section 11: Logistic Regression
Intro (what you will learn in this section)
01:34
Get the dataset
04:13
Binary outcome: Yes/No-Type Business Problems
09:09
Logistic regression intuition
17:03
Your first logistic regression
08:04
False Positives and False Negatives
08:01
Confusion Matrix
04:57
Interpreting coefficients of a logistic regression
10:03
Section 12: Building a robust geodemographic segmentation model
Intro (what you will learn in this section)
01:01
Get the dataset
07:32
What is geo-demographic segmenation?
05:05
Let’s build the model – first iteration
08:26
Let’s build the model – backward elimination: STEP-BY-STEP
11:11
Transforming independent variables
10:09
Creating derived variables
06:09
Checking for multicollinearity using VIF
08:11
Correlation Matrix and Multicollinearity Intuition
08:20
Model is Ready and Section Recap
06:27
Section 13: Assessing your model
Intro (what you will learn in this section)
00:37
Accuracy paradox
02:11
Cumulative Accuracy Profile (CAP)
11:16
How to build a CAP curve in Excel
14:47
Assessing your model using the CAP curve
07:11
Get my CAP curve template
06:20
How to use test data to prevent overfitting your model
03:34
Applying the model to test data
08:09
Comparing training performance and test performance
11:33
Section Recap
03:33
Section 14: Drawing insights from your model
Intro (what you will learn in this section)
00:34
Power insights from your CAP
13:52
Coefficients of a Logistic Regression – Plan of Attack (advanced topic)
03:47
Odds ratio (advanced topic)
08:29
Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
07:08
Deriving insights from your coefficients (advanced topic)
13:15
Section Recap
03:26
Section 15: Model maintenance
Intro (what you will learn in this section)
00:37
What does model deterioration look like?
04:36
Why do models deteriorate?
15:26
Three levels of maintenance for deployed models
08:21
Section Recap
01:38
Section 16: ————————— Part 3: Data Preparation —————————
Welcome to Part 3
02:24
Section 17: Business Intelligence (BI) Tools
Intro (what you will learn in this section)
00:23
Working with Data
01:15
What is a Data Warehouse? What is a Database?
03:28
Setting up Microsoft SQL Server 2014 for practice
08:05
Important: Practice Database
09:44
ETL for Data Science – what is Extract Transform Load (ETL)?
02:01
Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS ?
04:04
Installing SSDT with MSVS Shell
04:24
Section 18: ETL Phase 1: Data Wrangling before the Load
Intro (what you will learn in this section)
00:48
Preparing your folder structure for your Data Science project
02:20
Download the dataset for this section
01:27
Two things you HAVE to do before the load
04:56
Notepad ++
01:00
Editpad Lite
01:11
Section 19: ETL Phase 2: Step-by-step guide to uploading data using SSIS
Intro (what you will learn in this section)
00:50
Starting and navigating an SSIS Project
01:46
Creating a flat file source task and OLE DB destination
01:53
Setting up your flat file source connection
06:08
Setting up your database connection and creating a RAW table
07:43
Run the Upload & Disable
02:39
Due Dilligence: Upload Quality Assurance
02:02
Section 20: Handling errors during ETL (Phases 1 & 2)
Intro (what you will learn in this section)
00:50
Download the dataset for this section
00:46
How excel can mess up your data
03:46
Bulletproof Blueprint for Data Wrangling before the Load
07:13
SSIS Error: Text qualifier not specified
07:15
What do you do when your source file is corrupt? (Part 1)
18:01
What do you do when your source file is corrupt? (Part 2)
06:09
SSIS Error: Data truncation
15:56
Handy trick for finding anomalies in SQL
03:45
Automating Error Handling in SSIS: Conditional Split
08:20
Automating Error Handling in SSIS: Conditional Split (Level 2)
09:03
How to analyze the error files
16:40
Due Dilligence: the one thing you HAVE to do every time
04:57
Types of Errors in SSIS
04:00
Summary
19:06
Homework
03:39
Section 21: SQL Programming for Data Science
Intro (what you will learn in this section)
00:31
Download the dataset for this section
00:38
Getting To Know MS SQL Management Studio
02:14
Shortcut to upload the data
04:20
SELECT * Statement
05:52
Using the WHERE clause to filter data
05:50
How to use Wildcards / Regular Expressions in SQL (% and _)
04:38
Comments in SQL
02:43
Order By
05:49
Data Types in SQL
07:54
Implicit Data Conversion in SQL
03:35
Using Cast() vs Convert()
03:51
Working with NULLs
05:03
Understanding how LEFT, RIGHT, INNER, and OUTER joins work
06:18
Joins with duplicate values
02:32
Joining on multiple fields
05:21
Practicing Joins
05:00
Section 22: ETL Phase 3: Data Wrangling after the load
Intro (what you will learn in this section)
00:57
RAW, WRK, DRV tables
05:54
Download the dataset for this section
01:32
Create your first Stored Proc in SQL
03:30
Executing Stored Procedures
02:49
Modifying Stored Procedures
08:25
Create table
09:30
Insert INTO
05:42
Check if table exists + drop table + Truncate
05:59
Intermediate Recap – Procs
04:16
Create the proc for the second file
11:36
Adding leading zeros
07:29
Converting data on the fly
10:21
How to create a proc template
07:52
Archiving Procs
04:38
What you can do with these tables going forward [drv files etc.]
13:50
Section 23: Handling errors during ETL (Phase 3)
Intro (what you will learn in this section)
00:53
Download the dataset for this section
00:46
Upload the data to RAW table
11:02
Create Stored Proc
05:09
How to deal with errors using the isnumeric() function
07:45
How to deal errors using the len() function
07:36
How to deal with errors using the isdate() function
07:40
Additional Quality Assurance check: Balance
03:51
Additional Quality Assurance check: ZipCode
03:17
Additional Quality Assurance check: Birthday
04:08
Part Completed
09:52
ETL Error Handling “Vehicle Service” Project
07:45
Section 24: ————————— Part 4: Communication —————————
Welcome to Part 4
01:31
Section 25: Working with people
Intro (what you will learn in this section)
00:44
Cross-departmental Work
04:13
Come to me with a Business Problem
02:10
Setting expectations and pre-project communication
03:30
Go and sit with them
05:20
The art of saying “No”
05:24
Sometimes you have to go to the top
02:37
Building a data culture
05:07
Section 26: Presenting for Data Scientists
Intro (what you will learn in this section)
01:42
Case study
02:00
Analysing the intro
03:33
Intro dissection – recap
09:26
REAL Data Science Presentation Walkthrough – Make Your Audience Say “WOW”
16:29
My brainstorming method
03:03
How to present to executives
05:27
The truth is not always pretty
02:45
Passion and the Wow-factor
01:59
Bonus: my full presentation | LIVE 2015
16:20
Bonus: links to other examples of good storytelling
Article
Section 27: Homework Solutions
Advanced Data Mining with Tableau: Visualising Credit Score & Tenure
05:44
Advanced Data Mining with Tableau: Chi-Squared Test for Country
04:18
ETL Error Handling (Phases 1 and 2)
19:51
ETL Error Handling “Vehicle Service” Project (Part 1 of 3)
19:09
ETL Error Handling “Vehicle Service” Project (Part 2 of 3)
10:41
ETL Error Handling “Vehicle Service” Project (Part 3 of 3)
14:34
Section 28: Special Bonuses for Course Students
** NEW BONUS For Students: EXCLUSIVE Discount!! **
Article

Instructor Biography
Kirill Eremenko, Data Scientist & Forex Systems Expert
My name is Kirill Eremenko and I am super-psyched that you are reading this!

I teach courses in two distinct Business areas on Udemy: Data Science and Forex Trading. I want you to be confident that I can deliver the best training there is, so below is some of my background in both these fields.

Data Science

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

Forex Trading

Since 2007 I have been actively involved in the Forex market as a trader as well as running programming courses in MQL4. Forex trading is something I really enjoy, because the Forex market can give you financial, and more importantly – personal freedom.

In my other life I am a Data Scientist – I study numbers to analyze patterns in business processes and human behaviour… Sound familiar? Yep! Coincidentally, I am a big fan of Algorithmic Trading 🙂 EAs, Forex Robots, Indicators, Scripts, MQL4, even java programming for Forex – Love It All!

Summary

To sum up, I am absolutely and utterly passionate about both Data Science and Forex Trading and I am looking forward to sharing my passion and knowledge with you!

Course Features

  • Lectures
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