Data Science A-Z™: Hands-On Exercises & ChatGPT Bonus (2023)
About Course
Unlock the secrets of Data Science with this comprehensive and hands-on course, completely free on Theetay! Get ready to dive deep into the real-world challenges and complexities that Data Scientists face daily.
This course, originally from Udemy, will equip you with the skills to clean, prepare, visualize, model, and analyze data, culminating in a confident ability to present your findings. You’ll gain practical experience with essential tools like SQL, SSIS, Tableau, and Gretl, tackling thought-provoking exercises that will challenge your understanding and build your expertise.
Explore pre-planned pathways to personalize your learning journey, focusing on the skills most relevant to your career goals. Or, embark on the entire course and build a solid foundation for a successful career in Data Science.
This is your chance to learn from Kirill Eremenko, a renowned instructor, and join a vibrant community of learners. Start your Data Science journey today – it’s completely free on Theetay!
What Will You Learn?
- 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
Course Content
Get Excited
-
A Message from the Professor
-
Welcome to Data Science AZ™
04:41
What is Data Science
-
Intro what you will learn in this section
00:44 -
Updates on Udemy Reviews
01:09 -
Profession of the future
06:58 -
Areas of Data Science
05:58 -
IMPORTANT Course Pathways
05:52
Part 1 Visualisation
-
Welcome to Part 1
01:58
Introduction to Tableau
-
Intro what you will learn in this section
00:28 -
Installing Tableau Desktop and Tableau Public FREE
04:08 -
Challenge description view data in file
02:32 -
Connecting Tableau to a Data file
05:17 -
Navigating Tableau
08:42 -
Creating a calculated field
06:14 -
Adding colours
07:37 -
Adding labels and formatting
11:00 -
Exporting your worksheet
06:22 -
Section Recap
03:34
How to use Tableau for Data Mining
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Intro what you will learn in this section
00:44 -
Get the Dataset Project Overview
07:12 -
Connecting Tableau to an Excel File
03:57 -
How to visualise an AB 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
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
10:29 -
Chi
08:16 -
Visualising Balance and Estimated Salary distribution
11:04 -
Bonus Chi
19:12 -
Bonus Chi
09:10 -
Section Recap
05:44 -
Part Completed
01:31
Part 2 Modelling
-
Welcome to Part 2
03:54
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 -
Rsquared
05:11 -
Adjusted R
09:56
Simple Linear Regression
-
Intro what you will learn in this section
00:37 -
Introduction to Gretl
02:35 -
Get the dataset
04:00 -
Import data and run descriptive statistics
04:25 -
Reading Linear Regression Output
06:48 -
Plotting and analysing the graph
04:27
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 -
Understanding the P
11:45 -
Ways to build a model BACKWARD FORWARD STEPWISE
15:41 -
Backward Elimination
16:08 -
Using Adjusted R
10:17 -
Interpreting coefficients of MLR
12:47 -
Section Recap
04:15
Logistic Regression
-
Intro what you will learn in this section
01:34 -
Get the dataset
04:19 -
Binary outcome YesNo
09:09 -
Logistic regression intuition
17:03 -
Your first logistic regression
07:49 -
False Positives and False Negatives
08:01 -
Confusion Matrix
04:57 -
Interpreting coefficients of a logistic regression
10:03
Building a robust geodemographic segmentation model
-
Intro what you will learn in this section
01:01 -
Get the dataset
07:24 -
What is geo
05:05 -
Lets build the model
08:26 -
Lets build the model
11:11 -
Transforming independent variables
10:09 -
Creating derived variables
06:07 -
Checking for multicollinearity using VIF
08:06 -
Correlation Matrix and Multicollinearity Intuition
08:21 -
Model is Ready and Section Recap
06:27
Assessing your model
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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
14:27 -
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
07:59 -
Comparing training performance and test performance
11:33 -
Section Recap
03:33
Drawing insights from your model
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Intro what you will learn in this section
00:34 -
Power insights from your CAP
13:52 -
Coefficients of a Logistic Regression
03:47 -
Odds ratio advanced topic
08:30 -
Odds Ratio vs Coefficients in a Logistic Regression advanced topic
07:08 -
Deriving insights from your coefficients advanced topic
13:13 -
Section Recap
03:26
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
Part 3 Data Preparation
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Welcome to Part 3
02:24
Business Intelligence BI Tools
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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
05:34 -
Important Practice Database
09:35 -
ETL for Data Science
02:01 -
Microsoft BI Tools What is SSDT
04:04 -
Installing SSDT with MSVS Shell
04:24
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
ETL Phase 2 Stepbystep 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
06:57 -
Run the Upload & Disable
02:40 -
Due Dilligence Upload Quality Assurance
02:01
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
07:30 -
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
16:41 -
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
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
04:37 -
Comments in SQL
02:11 -
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
04:57
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
08:26 -
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
08:26 -
Create table
09:08 -
Insert INTO
05:41 -
Check if table exists drop table Truncate
05:59 -
Intermediate Recap
04:16 -
Create the proc for the second file
11:27 -
Adding leading zeros
07:16 -
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
Handling errors during ETL Phase 3
-
Intro what you will learn in this section
00:53 -
Download the dataset for this section
00:47 -
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
09:53 -
ETL Error Handling Vehicle Service Project
07:45
Part 4 Communication
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Welcome to Part 4
01:31
Working with people
-
Intro what you will learn in this section
00:44 -
Cross
04:13 -
Come to me with a Business Problem
02:10 -
Setting expectations and pre
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
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
09:26 -
REAL Data Science Presentation Walkthrough
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
01:59 -
Bonus my full presentation LIVE 2015
16:20
Homework Solutions
-
Advanced Data Mining with Tableau Visualising Credit Score & Tenure
05:44 -
Advanced Data Mining with Tableau Chi
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
14:24 -
THANK YOU bonus video
02:40
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