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

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 21 Hours | 14.8 GB
eLearning | Skill level: All Levels


Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

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!

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
+ Table of Contents

Get Excited
1 Welcome to Data Science A-Z™
2 BONUS Learning Paths
3 Get the materials
4 Your Shortcut To Becoming A Better Data Scientist!

What is Data Science
5 Intro (what you will learn in this section)
6 Updates on Udemy Reviews
7 Profession of the future
8 Areas of Data Science
9 IMPORTANT Course Pathways
10 Some Additional Resources!!
11 BONUS Interview with DJ Patil

Part 1 Visualisation —————————
12 Welcome to Part 1

Introduction to Tableau
13 Intro (what you will learn in this section)
14 Installing Tableau Desktop and Tableau Public (FREE)
15 Challenge description + view data in file
16 Connecting Tableau to a Data file – CSV file
17 Navigating Tableau – Measures and Dimensions
18 Creating a calculated field
19 Adding colours
20 Adding labels and formatting
21 Exporting your worksheet
22 Section Recap

How to use Tableau for Data Mining
23 Intro (what you will learn in this section)
24 Get the Dataset + Project Overview
25 Connecting Tableau to an Excel File
26 How to visualise an AB test in Tableau
27 Working with Aliases
28 Adding a Reference Line
29 Looking for anomalies
30 Handy trick to validate your approach data
31 Section Recap

Advanced Data Mining With Tableau
32 Intro (what you will learn in this section)
33 Creating bins & Visualizing distributions
34 Creating a classification test for a numeric variable
35 Combining two charts and working with them in Tableau
36 Validating Tableau Data Mining with a Chi-Squared test
37 Chi-Squared test when there is more than 2 categories
38 Visualising Balance and Estimated Salary distribution
39 Bonus Chi-Squared Test (Stats Tutorial)
40 Bonus Chi-Squared Test Part 2 (Stats Tutorial)
41 Section Recap
42 Part Completed

Part 2 Modelling —————————
43 Welcome to Part 2

Stats Refresher
44 Intro (what you will learn in this section)
45 Types of variables Categorical vs Numeric
46 Types of regressions
47 Ordinary Least Squares
48 R-squared
49 Adjusted R-squared

Simple Linear Regression
50 Intro (what you will learn in this section)
51 Introduction to Gretl
52 Get the dataset
53 Import data and run descriptive statistics
54 Reading Linear Regression Output
55 Plotting and analysing the graph

Multiple Linear Regression
56 Intro (what you will learn in this section)
57 Caveat assumptions of a linear regression
58 Get the dataset
59 Dummy Variables
60 Dummy Variable Trap
61 Understanding the P-Value
62 Ways to build a model BACKWARD, FORWARD, STEPWISE
63 Backward Elimination – Practice time
64 Using Adjusted R-squared to create Robust models
65 Interpreting coefficients of MLR
66 Section Recap

Logistic Regression
67 Intro (what you will learn in this section)
68 Get the dataset
69 Binary outcome YesNo-Type Business Problems
70 Logistic regression intuition
71 Your first logistic regression
72 False Positives and False Negatives
73 Confusion Matrix
74 Interpreting coefficients of a logistic regression

Building a robust geodemographic segmentation model
75 Intro (what you will learn in this section)
76 Get the dataset
77 What is geo-demographic segmenation
78 Let’s build the model – first iteration
79 Let’s build the model – backward elimination STEP-BY-STEP
80 Transforming independent variables
81 Creating derived variables
82 Checking for multicollinearity using VIF
83 Correlation Matrix and Multicollinearity Intuition
84 Model is Ready and Section Recap

Assessing your model
85 Intro (what you will learn in this section)
86 Accuracy paradox
87 Cumulative Accuracy Profile (CAP)
88 How to build a CAP curve in Excel
89 Assessing your model using the CAP curve
90 Get my CAP curve template
91 How to use test data to prevent overfitting your model
92 Applying the model to test data
93 Comparing training performance and test performance
94 Section Recap

Drawing insights from your model
95 Intro (what you will learn in this section)
96 Power insights from your CAP
97 Coefficients of a Logistic Regression – Plan of Attack (advanced topic)
98 Odds ratio (advanced topic)
99 Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
100 Deriving insights from your coefficients (advanced topic)
101 Section Recap

Model maintenance
102 Intro (what you will learn in this section)
103 What does model deterioration look like
104 Why do models deteriorate
105 Three levels of maintenance for deployed models
106 Section Recap

Part 3 Data Preparation —————————
107 Welcome to Part 3

Business Intelligence (BI) Tools
108 Intro (what you will learn in this section)
109 Working with Data
110 What is a Data Warehouse What is a Database
111 Setting up Microsoft SQL Server 2014 for practice
112 Important Practice Database
113 ETL for Data Science – what is Extract Transform Load (ETL)
114 Microsoft BI Tools What is SSDT-BI and what are SSISSSASSSRS
115 Installing SSDT with MSVS Shell

ETL Phase 1 Data Wrangling before the Load
116 Intro (what you will learn in this section)
117 Preparing your folder structure for your Data Science project
118 Download the dataset for this section
119 Two things you HAVE to do before the load
120 Notepad ++
121 Editpad Lite

ETL Phase 2 Step-by-step guide to uploading data using SSIS
122 Intro (what you will learn in this section)
123 Starting and navigating an SSIS Project
124 Creating a flat file source task and OLE DB destination
125 Setting up your flat file source connection
126 Setting up your database connection and creating a RAW table
127 Run the Upload & Disable
128 Due Dilligence Upload Quality Assurance

Handling errors during ETL (Phases 1 & 2)
129 Intro (what you will learn in this section)
130 Download the dataset for this section
131 How excel can mess up your data
132 Bulletproof Blueprint for Data Wrangling before the Load
133 SSIS Error Text qualifier not specified
134 What do you do when your source file is corrupt (Part 1)
135 What do you do when your source file is corrupt (Part 2)
136 SSIS Error Data truncation
137 Handy trick for finding anomalies in SQL
138 Automating Error Handling in SSIS Conditional Split
139 Automating Error Handling in SSIS Conditional Split (Level 2)
140 How to analyze the error files
141 Due Dilligence the one thing you HAVE to do every time
142 Types of Errors in SSIS
143 Summary
144 Homework

SQL Programming for Data Science
145 Intro (what you will learn in this section)
146 Download the dataset for this section
147 Getting To Know MS SQL Management Studio
148 Shortcut to upload the data
149 SELECT Statement
150 Using the WHERE clause to filter data
151 How to use Wildcards Regular Expressions in SQL (% and )
152 Comments in SQL
153 Order By
154 Data Types in SQL
155 Implicit Data Conversion in SQL
156 Using Cast() vs Convert()
157 Working with NULLs
158 Understanding how LEFT, RIGHT, INNER, and OUTER joins work
159 Joins with duplicate values
160 Joining on multiple fields
161 Practicing Joins

ETL Phase 3 Data Wrangling after the load
162 Intro (what you will learn in this section)
163 RAW, WRK, DRV tables
164 Download the dataset for this section
165 Create your first Stored Proc in SQL
166 Executing Stored Procedures
167 Modifying Stored Procedures
168 Create table
169 Insert INTO
170 Check if table exists + drop table + Truncate
171 Intermediate Recap – Procs
172 Create the proc for the second file
173 Adding leading zeros
174 Converting data on the fly
175 How to create a proc template
176 Archiving Procs
177 What you can do with these tables going forward [drv files etc.]

Handling errors during ETL (Phase 3)
178 Intro (what you will learn in this section)
179 Download the dataset for this section
180 Upload the data to RAW table
181 Create Stored Proc
182 How to deal with errors using the isnumeric() function
183 How to deal errors using the len() function
184 How to deal with errors using the isdate() function
185 Additional Quality Assurance check Balance
186 Additional Quality Assurance check ZipCode
187 Additional Quality Assurance check Birthday
188 Part Completed
189 ETL Error Handling Vehicle Service Project

Part 4 Communication —————————
190 Welcome to Part 4

Working with people
191 Intro (what you will learn in this section)
192 Cross-departmental Work
193 Come to me with a Business Problem
194 Setting expectations and pre-project communication
195 Go and sit with them
196 The art of saying No
197 Sometimes you have to go to the top
198 Building a data culture

Presenting for Data Scientists
199 Intro (what you will learn in this section)
200 Case study
201 Analysing the intro
202 Intro dissection – recap
203 REAL Data Science Presentation Walkthrough – Make Your Audience Say WOW
204 My brainstorming method
205 How to present to executives
206 The truth is not always pretty
207 Passion and the Wow-factor
208 Bonus my full presentation LIVE 2015
209 Bonus links to other examples of good storytelling

Homework Solutions
210 Advanced Data Mining with Tableau Visualising Credit Score & Tenure
211 Advanced Data Mining with Tableau Chi-Squared Test for Country
212 ETL Error Handling (Phases 1 and 2)
213 ETL Error Handling Vehicle Service Project (Part 1 of 3)
214 ETL Error Handling Vehicle Service Project (Part 2 of 3)
215 ETL Error Handling Vehicle Service Project (Part 3 of 3)
216 THANK YOU bonus video

Bonus Lectures
217 YOUR SPECIAL BONUS