**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