Data Science A-Z™: Real-Life Data Science Exercises Included
4.6 (26,780 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
175,991 students enrolled

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!
4.6 (26,780 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
176,003 students enrolled
Last updated 7/2020
English
English, Dutch, 11 more
  • French
  • German [Auto]
  • Hindi
  • Indonesian [Auto]
  • Italian [Auto]
  • Japanese [Auto]
  • Korean
  • Polish
  • Portuguese [Auto]
  • Spanish [Auto]
  • Traditional Chinese
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 21 hours on-demand video
  • 7 articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll 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
Expand all 217 lectures 21:18:56
+ Get Excited
4 lectures 07:43
BONUS: Learning Paths
00:51
Get the materials
00:05
Your Shortcut To Becoming A Better Data Scientist!
02:05
+ What is Data Science?
7 lectures 21:54
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
Some Additional Resources!!
00:13
BONUS: Interview with DJ Patil
00:59
+ Introduction to Tableau
10 lectures 55:54
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 - 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
06:22
Section Recap
03:34
Tableau Basics
5 questions
+ How to use Tableau for Data Mining
9 lectures 50:11
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

Learn how to do an AB test in Tableau with accessible and comprehensive visualization

Preview 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
11 lectures 01:29:00
Intro (what you will learn in this section)
00:44
Creating bins & Visualizing distributions
09:55
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
01:31
+ Stats Refresher
6 lectures 32:22
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
Adjusted R-squared
09:56
+ Simple Linear Regression
6 lectures 22:49
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
+ Multiple Linear Regression
11 lectures 01:28:21
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-Value
11:44
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
Requirements
  • Only a passion for success
  • All software used in this course is either available for Free or as a Demo version
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

Who this course is for:
  • 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