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Development Data Science

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!
Rating: 4.6 out of 54.6 (29,130 ratings)
186,839 students
Created by Kirill Eremenko, Ligency Team
Last updated 2/2021
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

28 sections • 217 lectures • 21h 13m total length

  • Preview04:41
  • BONUS: Learning Paths
    00:51
  • Get the materials
    00:05
  • Your Shortcut To Becoming A Better Data Scientist!
    02:05

  • 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
  • Preview05:52
  • Some Additional Resources!!
    00:13
  • BONUS: Interview with DJ Patil
    00:59

  • Welcome to Part 1
    01:58

  • 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

  • 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 AB test in Tableau?
    Preview06:29
  • 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

  • Intro (what you will learn in this section)
    00:44
  • Creating bins & Visualizing distributions
    09:55
  • Preview04:25
  • 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
    01:31

  • Welcome to Part 2
    03:54

  • 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
  • Preview05:11
  • Adjusted R-squared
    09:56

  • 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

  • Intro (what you will learn in this section)
    01:15
  • Caveat: assumptions of a linear regression
    01:47
  • Get the dataset
    04:11
  • Dummy Variables
    08:05
  • Dummy Variable Trap
    02:10
  • Understanding the P-Value
    11:44
  • Preview15:41
  • 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

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

Featured review

Christos Asvestopoulos
Christos Asvestopoulos
23 courses
4 reviews
Rating: 5.0 out of 510 months ago
The "Data Science A-Z™: Real-Life Data Science Exercises Included" is one of the best online courses I have ever taken. It concludes real-life datasets, insightful tutorials, challenging homework, tools (such as SSIS, SQL Server, Tableau, Gretl and SQL) and very interesting Q&A discussions (kudos to the teaching assistants for the last one). Highly recommended for anyone who wants to build a strong foundation on Data Science.

Instructors

Kirill Eremenko
Data Scientist
Kirill Eremenko
  • 4.5 Instructor Rating
  • 459,293 Reviews
  • 1,697,880 Students
  • 44 Courses

My name is Kirill Eremenko and I am super-psyched that you are reading this!

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.

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

Ligency Team
Helping Data Scientists Succeed
Ligency Team
  • 4.5 Instructor Rating
  • 469,134 Reviews
  • 1,685,674 Students
  • 109 Courses

Hi there,

We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology.

See you in class,

Sincerely,

The Real People at Ligency

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