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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!
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4.5 (3,560 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
30,427 students enrolled
Last updated 12/2016
English English
$10 $200 95% off
4 days left at this price!
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Includes:
  • 21 hours on-demand video
  • 3 Articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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 is the target audience?
  • 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
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What Will I 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
View Curriculum
Requirements
  • Only a passion for success
  • All software used in this course is either available for Free or as a Demo version
Curriculum For This Course
Expand All 210 Lectures Collapse All 210 Lectures 21:03:18
+
Get Excited
1 Lecture 04:41
+
What is Data Science?
5 Lectures 20:10
Intro (what you will learn in this section)
00:44

Profession of the future
06:58

Areas of Data Science
05:58


BONUS: Inspiring Data Science Interview
00:38
+
--------------------------- Part 1: Visualisation ---------------------------
1 Lecture 01:57
Welcome to Part 1
01:57
+
Introduction to Tableau
10 Lectures 59:12
Intro (what you will learn in this section)
00:28

Installing Tableau Desktop and Tableau Public (FREE)
06:08

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
07:40

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


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 01:29:07
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
01:38
+
--------------------------- Part 2: Modelling ---------------------------
1 Lecture 03:54
Welcome to Part 2
03:54
+
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
10 Lectures 01:16:37
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


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
18 More Sections
About the Instructor
4.5 Average rating
17,280 Reviews
94,408 Students
25 Courses
Data Scientist & Forex Systems Expert

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

I teach courses in two distinct Business areas on Udemy: Data Science and Forex Trading. I want you to be confident that I can deliver the best training there is, so below is some of my background in both these fields.

Data Science

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.

Forex Trading

Since 2007 I have been actively involved in the Forex market as a trader as well as running programming courses in MQL4. Forex trading is something I really enjoy, because the Forex market can give you financial, and more importantly - personal freedom.

In my other life I am a Data Scientist - I study numbers to analyze patterns in business processes and human behaviour... Sound familiar? Yep! Coincidentally, I am a big fan of Algorithmic Trading :) EAs, Forex Robots, Indicators, Scripts, MQL4, even java programming for Forex - Love It All!

Summary

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

4.5 Average rating
13,083 Reviews
73,115 Students
12 Courses
Helping Data Scientists Succeed

Hi there,

We are the SuperDataScience team. You will find us in the Data Science courses taught by Kirill Eremenko - we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We're passionate about helping you enjoy the courses!

See you in class,

Sincerely,

The Real People at SuperDataScience

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