Data Science A-Z™: Real-Life Data Science Exercises Included

Data Science, Data Analysis, Data Analytics, Data Analyst, Data Mining, Tableau, Statistics, Modeling, SQL, SSIS
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  • Lectures 209
  • Contents Video: 21 hours
    Other: 0 mins
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 8/2015 English

Course 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

What are the requirements?

  • Only a passion for success
  • All software used in this course is either available for Free or as a Demo version

What am I going to get from this course?

  • 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

What 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

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Get Excited
Welcome to Data Science A-Z™
Preview
04:41
Section 2: What is Data Science?
Intro (what you will learn in this section)
00:44
Profession of the future
06:58
Areas of Data Science
05:58
IMPORTANT: Course Pathways
Preview
05:52
Section 3: --------------------------- Part 1: Visualisation ---------------------------
Welcome to Part 1
01:57
Section 4: Introduction to Tableau
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
Section 5: How to use Tableau for Data Mining
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 ad-hoc A-B test in Tableau
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
Section 6: Advanced Data Mining With Tableau
Intro (what you will learn in this section)
00:44
Creating bins & Visualizing distributions
09:55
Creating a classification test for a numeric variable
Preview
04:25
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
01:38
Section 7: --------------------------- Part 2: Modelling ---------------------------
Welcome to Part 2
03:54
Section 8: Stats Refresher
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
R-squared
Preview
05:11
Adjusted R-squared
09:56
Section 9: Simple Linear Regression
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
Section 10: Multiple Linear Regression
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
Ways to build a model: BACKWARD, FORWARD, STEPWISE
Preview
15:41
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
Section 11: Logistic Regression
Intro (what you will learn in this section)
01:34
Get the dataset
04:13
Binary outcome: Yes/No-Type Business Problems
09:09
Logistic regression intuition
Preview
17:03
Your first logistic regression
08:04
False Positives and False Negatives
08:01
Confusion Matrix
04:57
Interpreting coefficients of a logistic regression
10:03
Section 12: Building a robust geodemographic segmentation model
Intro (what you will learn in this section)
01:01
Get the dataset
07:32
What is geo-demographic segmenation?
05:05
Let's build the model - first iteration
08:26
Let's build the model - backward elimination: STEP-BY-STEP
11:11
Transforming independent variables
10:09
Creating derived variables
06:09
Checking for multicollinearity using VIF
08:11
Correlation Matrix and Multicollinearity Intuition
08:20
Model is Ready and Section Recap
06:27
Section 13: Assessing your model
Intro (what you will learn in this section)
00:37
Accuracy paradox
02:11
Cumulative Accuracy Profile (CAP)
Preview
11:16
How to build a CAP curve in Excel
14:47
Assessing your model using the CAP curve
07:11
Get my CAP curve template
06:20
How to use test data to prevent overfitting your model
03:34
Applying the model to test data
08:09
Comparing training performance and test performance
11:33

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Instructor Biography

Kirill Eremenko, 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!

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