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
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										A Message from the Professor
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										Welcome to Data Science AZ™04:41
What is Data Science
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										Intro what you will learn in this section00:44
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										Updates on Udemy Reviews01:09
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										Profession of the future06:58
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										Areas of Data Science05:58
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										IMPORTANT Course Pathways05:52
Part 1 Visualisation
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										Welcome to Part 101:58
Introduction to Tableau
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										Intro what you will learn in this section00:28
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										Installing Tableau Desktop and Tableau Public FREE04:08
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										Challenge description view data in file02:32
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										Connecting Tableau to a Data file05:17
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										Navigating Tableau08:42
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										Creating a calculated field06:14
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										Adding colours07:37
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										Adding labels and formatting11:00
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										Exporting your worksheet06:22
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										Section Recap03:34
How to use Tableau for Data Mining
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										Intro what you will learn in this section00:44
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										Get the Dataset Project Overview07:12
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										Connecting Tableau to an Excel File03:57
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										How to visualise an AB test in Tableau06:29
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										Working with Aliases04:05
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										Adding a Reference Line04:53
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										Looking for anomalies08:35
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										Handy trick to validate your approach data09:13
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										Section Recap05:04
Advanced Data Mining With Tableau
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										Intro what you will learn in this section00:44
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										Creating bins & Visualizing distributions09:55
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										Creating a classification test for a numeric variable04:25
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										Combining two charts and working with them in Tableau08:31
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										Validating Tableau Data Mining with a Chi10:29
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										Chi08:16
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										Visualising Balance and Estimated Salary distribution11:04
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										Bonus Chi19:12
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										Bonus Chi09:10
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										Section Recap05:44
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										Part Completed01:31
Part 2 Modelling
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										Welcome to Part 203:54
Stats Refresher
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										Intro what you will learn in this section00:29
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										Types of variables Categorical vs Numeric05:26
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										Types of regressions08:09
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										Ordinary Least Squares03:11
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										Rsquared05:11
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										Adjusted R09:56
Simple Linear Regression
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										Intro what you will learn in this section00:37
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										Introduction to Gretl02:35
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										Get the dataset04:00
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										Import data and run descriptive statistics04:25
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										Reading Linear Regression Output06:48
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										Plotting and analysing the graph04:27
Multiple Linear Regression
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										Intro what you will learn in this section01:15
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										Caveat assumptions of a linear regression01:47
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										Get the dataset04:12
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										Dummy Variables08:05
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										Dummy Variable Trap02:10
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										Understanding the P11:45
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										Ways to build a model BACKWARD FORWARD STEPWISE15:41
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										Backward Elimination16:08
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										Using Adjusted R10:17
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										Interpreting coefficients of MLR12:47
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										Section Recap04:15
Logistic Regression
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										Intro what you will learn in this section01:34
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										Get the dataset04:19
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										Binary outcome YesNo09:09
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										Logistic regression intuition17:03
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										Your first logistic regression07:49
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										False Positives and False Negatives08:01
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										Confusion Matrix04:57
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										Interpreting coefficients of a logistic regression10:03
Building a robust geodemographic segmentation model
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										Intro what you will learn in this section01:01
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										Get the dataset07:24
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										What is geo05:05
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										Lets build the model08:26
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										Lets build the model11:11
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										Transforming independent variables10:09
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										Creating derived variables06:07
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										Checking for multicollinearity using VIF08:06
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										Correlation Matrix and Multicollinearity Intuition08:21
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										Model is Ready and Section Recap06:27
Assessing your model
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										Intro what you will learn in this section00:37
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										Accuracy paradox02:11
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										Cumulative Accuracy Profile CAP11:16
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										How to build a CAP curve in Excel14:27
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										Assessing your model using the CAP curve07:11
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										Get my CAP curve template06:20
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										How to use test data to prevent overfitting your model03:34
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										Applying the model to test data07:59
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										Comparing training performance and test performance11:33
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										Section Recap03:33
Drawing insights from your model
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										Intro what you will learn in this section00:34
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										Power insights from your CAP13:52
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										Coefficients of a Logistic Regression03:47
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										Odds ratio advanced topic08:30
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										Odds Ratio vs Coefficients in a Logistic Regression advanced topic07:08
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										Deriving insights from your coefficients advanced topic13:13
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										Section Recap03:26
Model maintenance
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										Intro what you will learn in this section00:37
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										What does model deterioration look like04:36
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										Why do models deteriorate15:26
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										Three levels of maintenance for deployed models08:21
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										Section Recap01:38
Part 3 Data Preparation
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										Welcome to Part 302:24
Business Intelligence BI Tools
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										Intro what you will learn in this section00:23
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										Working with Data01:15
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										What is a Data Warehouse What is a Database03:28
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										Setting up Microsoft SQL Server 2014 for practice05:34
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										Important Practice Database09:35
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										ETL for Data Science02:01
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										Microsoft BI Tools What is SSDT04:04
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										Installing SSDT with MSVS Shell04:24
ETL Phase 1 Data Wrangling before the Load
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										Intro what you will learn in this section00:48
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										Preparing your folder structure for your Data Science project02:20
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										Download the dataset for this section01:27
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										Two things you HAVE to do before the load04:56
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										Notepad01:00
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										Editpad Lite01:11
ETL Phase 2 Stepbystep guide to uploading data using SSIS
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										Intro what you will learn in this section00:50
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										Starting and navigating an SSIS Project01:46
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										Creating a flat file source task and OLE DB destination01:53
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										Setting up your flat file source connection06:08
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										Setting up your database connection and creating a RAW table06:57
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										Run the Upload & Disable02:40
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										Due Dilligence Upload Quality Assurance02:01
Handling errors during ETL Phases 1 & 2
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										Intro what you will learn in this section00:50
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										Download the dataset for this section00:46
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										How excel can mess up your data03:46
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										Bulletproof Blueprint for Data Wrangling before the Load07:30
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										SSIS Error Text qualifier not specified07:15
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										What do you do when your source file is corrupt Part 118:01
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										What do you do when your source file is corrupt Part 206:09
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										SSIS Error Data truncation15:56
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										Handy trick for finding anomalies in SQL03:45
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										Automating Error Handling in SSIS Conditional Split08:20
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										Automating Error Handling in SSIS Conditional Split Level 209:03
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										How to analyze the error files16:41
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										Due Dilligence the one thing you HAVE to do every time04:57
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										Types of Errors in SSIS04:00
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										Summary19:06
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										Homework03:39
SQL Programming for Data Science
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										Intro what you will learn in this section00:31
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										Download the dataset for this section00:38
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										Getting To Know MS SQL Management Studio02:14
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										Shortcut to upload the data04:20
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										SELECT Statement05:52
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										Using the WHERE clause to filter data05:50
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										How to use Wildcards Regular Expressions in SQL and04:37
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										Comments in SQL02:11
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										Order By05:49
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										Data Types in SQL07:54
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										Implicit Data Conversion in SQL03:35
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										Using Cast vs Convert03:51
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										Working with NULLs05:03
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										Understanding how LEFT RIGHT INNER and OUTER joins work06:18
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										Joins with duplicate values02:32
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										Joining on multiple fields05:21
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										Practicing Joins04:57
ETL Phase 3 Data Wrangling after the load
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										Intro what you will learn in this section00:57
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										RAW WRK DRV tables05:54
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										Download the dataset for this section01:32
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										Create your first Stored Proc in SQL03:30
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										Executing Stored Procedures02:49
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										Modifying Stored Procedures08:26
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										Intro what you will learn in this section00:57
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										RAW WRK DRV tables05:54
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										Download the dataset for this section01:32
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										Create your first Stored Proc in SQL03:30
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										Executing Stored Procedures02:49
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										Modifying Stored Procedures08:26
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										Create table09:08
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										Insert INTO05:41
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										Check if table exists drop table Truncate05:59
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										Intermediate Recap04:16
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										Create the proc for the second file11:27
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										Adding leading zeros07:16
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										Converting data on the fly10:21
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										How to create a proc template07:52
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										Archiving Procs04:38
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										What you can do with these tables going forward drv files etc13:50
Handling errors during ETL Phase 3
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										Intro what you will learn in this section00:53
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										Download the dataset for this section00:47
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										Upload the data to RAW table11:02
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										Create Stored Proc05:09
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										How to deal with errors using the isnumeric function07:45
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										How to deal errors using the len function07:36
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										How to deal with errors using the isdate function07:40
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										Additional Quality Assurance check Balance03:51
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										Additional Quality Assurance check ZipCode03:17
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										Additional Quality Assurance check Birthday04:08
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										Part Completed09:53
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										ETL Error Handling Vehicle Service Project07:45
Part 4 Communication
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										Welcome to Part 401:31
Working with people
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										Intro what you will learn in this section00:44
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										Cross04:13
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										Come to me with a Business Problem02:10
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										Setting expectations and pre03:30
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										Go and sit with them05:20
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										The art of saying No05:24
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										Sometimes you have to go to the top02:37
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										Building a data culture05:07
Presenting for Data Scientists
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										Intro what you will learn in this section01:42
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										Case study02:00
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										Analysing the intro03:33
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										Intro dissection09:26
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										REAL Data Science Presentation Walkthrough16:29
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										My brainstorming method03:03
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										How to present to executives05:27
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										The truth is not always pretty02:45
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										Passion and the Wow01:59
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										Bonus my full presentation LIVE 201516:20
Homework Solutions
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										Advanced Data Mining with Tableau Visualising Credit Score & Tenure05:44
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										Advanced Data Mining with Tableau Chi04:18
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										ETL Error Handling Phases 1 and 219:51
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										ETL Error Handling Vehicle Service Project Part 1 of 319:09
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										ETL Error Handling Vehicle Service Project Part 2 of 310:41
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										ETL Error Handling Vehicle Service Project Part 3 of 314:24
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										THANK YOU bonus video02:40
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