Data Science A-Z™: Real-Life Data Science Exercises Included
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
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
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
Get Excited
1
Welcome to Data Science A-Z™
2
Get the Datasets here
3
Extra Resources
What is Data Science?
1
Intro (what you will learn in this section)
2
Profession of the future
3
Areas of Data Science
4
IMPORTANT: Course Pathways
5
Some Additional Resources!!
6
EXTRA: Interview with DJ Patil
--------------------------- Part 1: Visualisation ---------------------------
1
Welcome to Part 1
Introduction to Tableau
1
Intro (what you will learn in this section)
2
Installing Tableau Desktop and Tableau Public (FREE)
3
Challenge description + view data in file
4
Connecting Tableau to a Data file - CSV file
5
Navigating Tableau - Measures and Dimensions
6
Creating a calculated field
7
Adding colours
8
Adding labels and formatting
9
Exporting your worksheet
10
Section Recap
11
Tableau Basics
How to use Tableau for Data Mining
1
Intro (what you will learn in this section)
2
Get the Dataset + Project Overview
3
Connecting Tableau to an Excel File
4
How to visualise an AB test in Tableau?
Learn how to do an AB test in Tableau with accessible and comprehensive visualization
5
Working with Aliases
6
Adding a Reference Line
7
Looking for anomalies
8
Handy trick to validate your approach / data
9
Section Recap
Advanced Data Mining With Tableau
1
Intro (what you will learn in this section)
2
Creating bins & Visualizing distributions
3
Creating a classification test for a numeric variable
4
Combining two charts and working with them in Tableau
5
Validating Tableau Data Mining with a Chi-Squared test
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Chi-Squared test when there is more than 2 categories
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Quick Note
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Visualising Balance and Estimated Salary distribution
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Extra: Chi-Squared Test (Stats Tutorial)
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Extra: Chi-Squared Test Part 2 (Stats Tutorial)
11
Section Recap
12
Part Completed
--------------------------- Part 2: Modelling ---------------------------
1
Welcome to Part 2
Stats Refresher
1
Intro (what you will learn in this section)
2
Types of variables: Categorical vs Numeric
3
Types of regressions
4
Ordinary Least Squares
5
R-squared
6
Adjusted R-squared
Simple Linear Regression
1
Intro (what you will learn in this section)
2
Introduction to Gretl
3
Get the dataset
4
Import data and run descriptive statistics
5
Reading Linear Regression Output
6
Plotting and analysing the graph
Multiple Linear Regression
1
Intro (what you will learn in this section)
2
Get the dataset
3
Assumptions of Linear Regression
4
Dummy Variables
5
Dummy Variable Trap
6
Understanding the P-Value
7
Ways to build a model: BACKWARD, FORWARD, STEPWISE
8
Backward Elimination - Practice time
9
Using Adjusted R-squared to create Robust models
10
Interpreting coefficients of MLR
11
Section Recap
Logistic Regression
1
Intro (what you will learn in this section)
2
Get the dataset
3
Binary outcome: Yes/No-Type Business Problems
4
Logistic regression intuition
5
Your first logistic regression
6
False Positives and False Negatives
7
Confusion Matrix
8
Interpreting coefficients of a logistic regression
Building a robust geodemographic segmentation model
1
Intro (what you will learn in this section)
2
Get the dataset
3
What is geo-demographic segmenation?
4
Let's build the model - first iteration
5
Let's build the model - backward elimination: STEP-BY-STEP
6
Transforming independent variables
7
Creating derived variables
8
Checking for multicollinearity using VIF
9
Correlation Matrix and Multicollinearity Intuition
10
Model is Ready and Section Recap
Assessing your model
1
Intro (what you will learn in this section)
2
Accuracy paradox
3
Cumulative Accuracy Profile (CAP)
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
