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0.0 Data Science A-Z
0.0 Data Science A-Z 00:00:00
1.1 Welcome to Data Science A-Z
1.1 Welcome to Data Science A-Z 00:00:00
2.1 Intro (what you will learn in this section)
2.1 Intro (what you will learn in this section) 00:00:00
2.2 Profession of the future
2.2 Profession of the future 00:00:00
2.3 Areas of Data Science
2.3 Areas of Data Science 00:00:00
2.4 Important Course Pathways
2.4 Important Course Pathways 00:00:00
2.5 Bonus/ Success Story
2.5 Bonus/ Success Story 00:00:00
3.1 Welcome to Part 1
3.1 Welcome to Part 1 00:00:00
4.1 Intro (what you will learn in this section)
4.1 Intro (what you will learn in this section) 00:00:00
4.2 Installing Tableau Desktop and Tableau Public (Free)
4.2 Installing Tableau Desktop and Tableau Public (Free) 00:00:00
4.3 Challenge description + view data in file
4.3 Challenge description + view data in file 00:00:00
4.4 Connecting Tableau to a Data file - CSV file
4.4 Connecting Tableau to a Data file – CSV file 00:00:00
4.5 Navigating Tableau - Measures and Dimensions
4.5 Navigating Tableau – Measures and Dimensions 00:00:00
4.6 Creating a calculated field
4.6 Creating a calculated field 00:00:00
4.7 Adding colours
4.7 Adding colours 00:00:00
4.8 Adding labels and formatting
4.8 Adding labels and formatting 00:00:00
4.9 Exporting your worksheet
4.9 Exporting your worksheet 00:00:00
4.10 Section Recap
4.10 Section Recap 00:00:00
5.1 Intro (what you will learn in this section)
5.1 Intro (what you will learn in this section) 00:00:00
5.2 Get the Dataset + Project Overview
5.2 Get the Dataset + Project Overview 00:00:00
5.3 Connecting Tableau to an Excel File
5.3 Connecting Tableau to an Excel File 00:00:00
5.5 Working with Aliases
5.5 Working with Aliases 00:00:00
5.6 Adding a Reference Line
5.6 Adding a Reference Line 00:00:00
5.7 Looking for anomalies
5.7 Looking for anomalies 00:00:00
5.8 Handy trick to validate your approach data
5.8 Handy trick to validate your approach data 00:00:00
5.9 Section Recap
5.9 Section Recap 00:00:00
6.1 Intro (what you will learn in this section)
6.1 Intro (what you will learn in this section) 00:00:00
6.2 Creating bins & Visualizing distributions
6.2 Creating bins & Visualizing distributions 00:00:00
6.3 Creating a classification test for a numeric variable
6.3 Creating a classification test for a numeric variable 00:00:00
6.4 Combining two charts and working with them in Tableau
6.4 Combining two charts and working with them in Tableau 00:00:00
6.5 Validating Tableau Data Mining with a Chi-Squared test
6.5 Validating Tableau Data Mining with a Chi-Squared test 00:00:00
6.6 Chi-Squared test when there is more than 2 categories
6.6 Chi-Squared test when there is more than 2 categories 00:00:00
6.7 Visualising Balance and Estimated Salary distribution
6.7 Visualising Balance and Estimated Salary distribution 00:00:00
6.8 Bonus Chi-Squared Test (Stats Tutorial)
6.8 Bonus Chi-Squared Test (Stats Tutorial) 00:00:00
6.9 Bonus Chi-Squared Test Part 2 (Stats Tutorial)
6.9 Bonus Chi-Squared Test Part 2 (Stats Tutorial) 00:00:00
6.10 Section Recap
6.10 Section Recap 00:00:00
7.1 Welcome to Part 2
7.1 Welcome to Part 2 00:00:00
8.1 Intro (what you will learn in this section)
8.1 Intro (what you will learn in this section) 00:00:00
8.2 Types of variables Categorical vs Numeric
8.2 Types of variables Categorical vs Numeric 00:00:00
8.3 Types of regressions
8.3 Types of regressions 00:00:00
8.4 Ordinary Least Squares
8.4 Ordinary Least Squares 00:00:00
8.5 R-squared
8.5 R-squared 00:00:00
8.6 Adjusted R-squared
8.6 Adjusted R-squared 00:00:00
9.1 Intro (what you will learn in this section)
9.1 Intro (what you will learn in this section) 00:00:00
9.2 Introduction to Gretl
9.2 Introduction to Gretl 00:00:00
9.3 Get the dataset
9.3 Get the dataset 00:00:00
9.4 Import data and run descriptive statistics
9.4 Import data and run descriptive statistics 00:00:00
9.5 Reading Linear Regression Output
9.5 Reading Linear Regression Output 00:00:00
9.6 Plotting and analysing the graph
9.6 Plotting and analysing the graph 00:00:00
10.1 Intro (what you will learn in this section)
10.1 Intro (what you will learn in this section) 00:00:00
10.2 Caveat assumptions of a linear regression
10.2 Caveat assumptions of a linear regression 00:00:00
10.3 Get the dataset
10.3 Get the dataset 00:00:00
10.4 Dummy Variables
10.4 Dummy Variables 00:00:00
10.5 Dummy Variable Trap
10.5 Dummy Variable Trap 00:00:00
10.6 Ways to build a model BACKWARD, FORWARD, STEPWISE
10.6 Ways to build a model BACKWARD, FORWARD, STEPWISE 00:00:00
10.7 Backward Elimination - Practice time
10.7 Backward Elimination – Practice time 00:00:00
10.8 Using Adjusted R-squared to create Robust models
10.8 Using Adjusted R-squared to create Robust models 00:00:00
10.9 Interpreting coefficients of MLR
10.9 Interpreting coefficients of MLR 00:00:00
10.10 Section Recap
10.10 Section Recap 00:00:00
11.1 Intro (what you will learn in this section)
11.1 Intro (what you will learn in this section) 00:00:00
11.2 Get the dataset
11.2 Get the dataset 00:00:00
11.3 Binary outcome YesNo-Type Business Problems
11.3 Binary outcome YesNo-Type Business Problems 00:00:00
11.4 Logistic regression intuition
11.4 Logistic regression intuition 00:00:00
11.5 Your first logistic regression
11.5 Your first logistic regression 00:00:00
11.6 False Positives and False Negatives
11.6 False Positives and False Negatives 00:00:00
11.7 Confusion Matrix
11.7 Confusion Matrix 00:00:00
11.8 Interpreting coefficients of a logistic regression
11.8 Interpreting coefficients of a logistic regression 00:00:00
12.1 Intro (what you will learn in this section)
12.1 Intro (what you will learn in this section) 00:00:00
12.2 Get the dataset
12.2 Get the dataset 00:00:00
12.3 What is geo-demographic segmenation
12.3 What is geo-demographic segmenation 00:00:00
12.4 Let's build the model - First iteration
12.4 Let’s build the model – First iteration 00:00:00
12.5 Let's build the model - backward elimination Step-by-Step
12.5 Let’s build the model – backward elimination Step-by-Step 00:00:00
12.6 Transforming independent variables
12.6 Transforming independent variables 00:00:00
12.7 Creating derived variables
12.7 Creating derived variables 00:00:00
12.8 Checking for multicollinearity using VIF
12.8 Checking for multicollinearity using VIF 00:00:00
12.9 Correlation Matr12.9 Correlation Matrix and Multicollinearity Intuition.tsix and Multicollinearity Intuition
12.9 Correlation Matrix and Multicollinearity Intuition 00:00:00
12.10 Model is Ready and Section Recap
12.10 Model is Ready and Section Recap 00:00:00
13.1 Intro (what you will learn in this section)
13.1 Intro (what you will learn in this section) 00:00:00
13.2 Accuracy paradox
13.2 Accuracy paradox 00:00:00
13.3 Cumulative Accuracy Profile (CAP)
13.3 Cumulative Accuracy Profile (CAP) 00:00:00
13.4 How to build a CAP curve in Excel
13.4 How to build a CAP curve in Excel 00:00:00
13.5 Assessing your model using the CAP curve
13.5 Assessing your model using the CAP curve 00:00:00
13.6 Get my CAP curve template
13.6 Get my CAP curve template 00:00:00
13.7 How to use test data to prevent overfitting your model
13.7 How to use test data to prevent overfitting your model 00:00:00
13.8 Applying the model to test data
13.8 Applying the model to test data 00:00:00
13.9 Comparing training performance and test performance
13.9 Comparing training performance and test performance 00:00:00
13.10 Section Recap
13.10 Section Recap 00:00:00
14.1 Intro (what you will learn in this section)
14.1 Intro (what you will learn in this section) 00:00:00
14.2 Power insights from your CAP
14.2 Power insights from your CAP 00:00:00
14.3 Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
14.3 Coefficients of a Logistic Regression – Plan of Attack (advanced topic) 00:00:00
14.2 Power insights from your CAP
14.2 Power insights from your CAP 00:00:00
14.3 Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
14.3 Coefficients of a Logistic Regression – Plan of Attack (advanced topic) 00:00:00
14.4 Odds ratio (advanced topic)
14.4 Odds ratio (advanced topic) 00:00:00
14.5 Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
14.5 Odds Ratio vs Coefficients in a Logistic Regression (advanced topic) 00:00:00
14.6 Deriving insights from your coefficients (advanced topic)
14.6 Deriving insights from your coefficients (advanced topic) 00:00:00
14.7 Section Recap
14.7 Section Recap 00:00:00
15.1 Intro (what you will learn in this section)
15.1 Intro (what you will learn in this section) 00:00:00
15.2 What does model deterioration look like
15.2 What does model deterioration look like 00:00:00
15.3 Why do models deteriorate
15.3 Why do models deteriorate 00:00:00
15.4 Three levels of maintenance for deployed models
15.4 Three levels of maintenance for deployed models 00:00:00
15.5 Section Recap
15.5 Section Recap 00:00:00
16.1 Welcome to Part 3
16.1 Welcome to Part 3 00:00:00
17.1 Intro (what you will learn in this section)
17.1 Intro (what you will learn in this section) 00:00:00
17.2 Working with Data
17.2 Working with Data 00:00:00
17.3 What is a Data Warehouse What is a Database
17.3 What is a Data Warehouse What is a Database 00:00:00
17.4 Setting up Microsoft SQL Server 2014 for practice
17.4 Setting up Microsoft SQL Server 2014 for practice 00:00:00
17.5 Important Practice Database
17.5 Important Practice Database 00:00:00
17.6 ETL for Data Science - what is Extract Transform Load (ETL)
17.6 ETL for Data Science – what is Extract Transform Load (ETL) 00:00:00
17.7 Microsoft BI Tools What is SSDT-BI and what are SSISSSASSSRS
17.7 Microsoft BI Tools What is SSDT-BI and what are SSISSSASSSRS 00:00:00
18.1 Intro (what you will learn in this section)
18.1 Intro (what you will learn in this section) 00:00:00
18.2 Preparing your folder structure for your Data Science project
18.2 Preparing your folder structure for your Data Science project 00:00:00
18.3 Download the dataset for this section
18.3 Download the dataset for this section 00:00:00
18.4 Two things you HAVE to do before the load
18.4 Two things you HAVE to do before the load 00:00:00
18.5 Notepad ++
18.5 Notepad ++ 00:00:00
18.6 Editpad Lite
18.6 Editpad Lite 00:00:00
19.1 Intro (what you will learn in this section)
19.1 Intro (what you will learn in this section) 00:00:00
19.2 Starting and navigating an SSIS Project
19.2 Starting and navigating an SSIS Project 00:00:00
19.3 Creating a flat file source task and OLE DB destination
19.3 Creating a flat file source task and OLE DB destination 00:00:00
19.4 Setting up your flat file source connection
19.4 Setting up your flat file source connection 00:00:00
19.5 Setting up your database connection and creating a RAW table
19.5 Setting up your database connection and creating a RAW table 00:00:00
19.7 Due Dilligence Upload Quality Assurance
19.7 Due Dilligence Upload Quality Assurance 00:00:00
20.1 Intro (what you will learn in this section)
20.1 Intro (what you will learn in this section) 00:00:00
20.2 Download the dataset for this section
20.2 Download the dataset for this section 00:00:00
20.3 How excel can mess up your data
20.3 How excel can mess up your data 00:00:00
20.4 Bulletproof Blueprint for Data Wrangling before the Load
20.4 Bulletproof Blueprint for Data Wrangling before the Load 00:00:00
20.5 SSIS Error Text qualifier not specified
20.5 SSIS Error Text qualifier not specified 00:00:00
20.6 What do you do when your source file is corrupt (Part 1)
20.6 What do you do when your source file is corrupt (Part 1) 00:00:00
20.7 What do you do when your source file is corrupt (Part 2)
20.7 What do you do when your source file is corrupt (Part 2) 00:00:00
20.8 SSIS Error Data truncation
20.8 SSIS Error Data truncation 00:00:00
20.9 Handy trick for finding anomalies in SQL
20.9 Handy trick for finding anomalies in SQL 00:00:00
20.10 Automating Error Handling in SSIS Conditional Split
20.10 Automating Error Handling in SSIS Conditional Split 00:00:00
20.11 Automating Error Handling in SSIS Conditional Split (Level 2)
20.11 Automating Error Handling in SSIS Conditional Split (Level 2) 00:00:00
20.12 How to analyze the error files
20.12 How to analyze the error files 00:00:00
20.13 Due Dilligence the one thing you HAVE to do every time
20.13 Due Dilligence the one thing you HAVE to do every time 00:00:00
20.15 Summary
20.15 Summary 00:00:00
20.16 Homework
20.16 Homework 00:00:00
21.1 Intro (what you will learn in this section)
21.1 Intro (what you will learn in this section) 00:00:00
21.2 Download the dataset for this section
21.2 Download the dataset for this section 00:00:00
21.3 Getting To Know MS SQL Management Studio
21.3 Getting To Know MS SQL Management Studio 00:00:00
21.4 Shortcut to upload the data
21.4 Shortcut to upload the data 00:00:00
21.5 SELECT Statement
21.5 SELECT Statement 00:00:00
21.6 Using the WHERE clause to filter data
21.6 Using the WHERE clause to filter data 00:00:00
21.7 How to use Wildcards Regular Expressions in SQL (% and _)
21.7 How to use Wildcards Regular Expressions in SQL (% and _) 00:00:00
21.8 Comments in SQL
21.8 Comments in SQL 00:00:00
21.9 Order By
21.9 Order By 00:00:00
21.10 Data Types in SQL
21.10 Data Types in SQL 00:00:00
21.11 Implicit Data Conversion in SQL
21.11 Implicit Data Conversion in SQL 00:00:00
21.12 Using Cast() vs Convert()
21.12 Using Cast() vs Convert() 00:00:00
21.13 Working with NULLs
21.13 Working with NULLs 00:00:00
21.14 Understanding how LEFT, RIGHT, INNER, and OUTER joins work
21.14 Understanding how LEFT, RIGHT, INNER, and OUTER joins work 00:00:00
21.15 Joins with duplicate values
21.15 Joins with duplicate values 00:00:00
21.16 Joining on multiple fields
21.16 Joining on multiple fields 00:00:00
21.17 Practicing Joins
21.17 Practicing Joins 00:00:00
22.1 Intro (what you will learn in this section)
22.1 Intro (what you will learn in this section) 00:00:00
22.2 RAW, WRK, DRV tables
22.2 RAW, WRK, DRV tables 00:00:00
22.3 Download the dataset for this section
22.3 Download the dataset for this section 00:00:00
22.4 Create your first Stored Proc in SQL
22.4 Create your first Stored Proc in SQL 00:00:00
22.5 Executing Stored Procedures
22.5 Executing Stored Procedures 00:00:00
22.6 Modifying Stored Procedures
22.6 Modifying Stored Procedures 00:00:00
22.7 Create table
22.7 Create table 00:00:00
22.8 Insert INTO
22.8 Insert INTO 00:00:00
22.9 Check if table exists + drop table + Truncate
22.9 Check if table exists + drop table + Truncate 00:00:00
22.10 Insert INTO
22.10 Insert INTO 00:00:00
22.11 Create the proc for the second file
22.11 Create the proc for the second file 00:00:00
22.12 Adding leading zeros
22.12 Adding leading zeros 00:00:00
22.13 Converting data on the fly
22.13 Converting data on the fly 00:00:00
22.14 How to create a proc template
22.14 How to create a proc template 00:00:00
22.15 Archiving Procs
22.15 Archiving Procs 00:00:00
22.16 What you can do with these tables going forward [drv files etc.]
22.16 What you can do with these tables going forward [drv files etc.] 00:00:00
23.1 Intro (what you will learn in this section)
23.1 Intro (what you will learn in this section) 00:00:00
23.2 Download the dataset for this section
23.2 Download the dataset for this section 00:00:00
23.3 Upload the data to RAW table
23.3 Upload the data to RAW table 00:00:00
23.4 Create Stored Proc
23.4 Create Stored Proc 00:00:00
23.5 How to deal with errors using the isnumeric() function
23.5 How to deal with errors using the isnumeric() function 00:00:00
23.6 How to deal errors using the len() function
23.6 How to deal errors using the len() function 00:00:00
23.7 How to deal with errors using the isdate() function
23.7 How to deal with errors using the isdate() function 00:00:00
23.8 Additional Quality Assurance check Balance
23.8 Additional Quality Assurance check Balance 00:00:00
23.9 Additional Quality Assurance check ZipCode
23.9 Additional Quality Assurance check ZipCode 00:00:00
23.10 Additional Quality Assurance check Birthday
23.10 Additional Quality Assurance check Birthday 00:00:00
23.11 Part Completed
23.11 Part Completed 00:00:00
23.12 ETL Error Handling Vehicle Service Project
23.12 ETL Error Handling Vehicle Service Project 00:00:00
24.1 Welcome to Part 4
24.1 Welcome to Part 4 00:00:00
25.1 Intro (what you will learn in this section)
25.1 Intro (what you will learn in this section) 00:00:00
25.2 Cross-departmental Work
25.2 Cross-departmental Work 00:00:00
25.3 Come to me with a Business Problem
25.3 Come to me with a Business Problem 00:00:00
25.4 Setting expectations and pre-project communication
25.4 Setting expectations and pre-project communication 00:00:00
25.5 Go and sit with them
25.5 Go and sit with them 00:00:00
25.6 The art of saying No
25.6 The art of saying No 00:00:00
25.7 Sometimes you have to go to the top
25.7 Sometimes you have to go to the top 00:00:00
25.8 Building a data culture
25.8 Building a data culture 00:00:00
26.1 Intro (what you will learn in this section)
26.1 Intro (what you will learn in this section) 00:00:00
26.2 Case study
26.2 Case study 00:00:00
26.3 Analysing the intro
26.3 Analysing the intro 00:00:00
26.4 Intro dissection - recap
26.4 Intro dissection – recap 00:00:00
26.5 REAL Data Science Presentation Walkthrough - Make Your Audience Say WOW
26.5 REAL Data Science Presentation Walkthrough – Make Your Audience Say WOW 00:00:00
26.6 My brainstorming method
26.6 My brainstorming method 00:00:00
26.7 How to present to executives
26.7 How to present to executives 00:00:00
26.8 The truth is not always pretty
26.8 The truth is not always pretty 00:00:00
26.9 Passion and the Wow-factor
26.9 Passion and the Wow-factor 00:00:00
26.10 Bonus My full presentation Live 2015
26.10 Bonus My full presentation Live 2015 00:00:00
26.11 Bonus/ links to other examples of good storytelling
26.11 Bonus/ links to other examples of good storytelling 00:00:00
27.1 Advanced Data Mining with Tableau Visualising Credit Score & Tenure
27.1 Advanced Data Mining with Tableau Visualising Credit Score & Tenure 00:00:00
27.2 Advanced Data Mining with Tableau Chi-Squared Test for Country
27.2 Advanced Data Mining with Tableau Chi-Squared Test for Country 00:00:00
27.3 ETL Error Handling (Phases 1 and 2)
27.3 ETL Error Handling (Phases 1 and 2) 00:00:00
27.4 ETL Error Handling Vehicle Service Project (Part 1 of 3)
27.4 ETL Error Handling Vehicle Service Project (Part 1 of 3) 00:00:00
27.5 ETL Error Handling Vehicle Service Project (Part 2 of 3)
27.5 ETL Error Handling Vehicle Service Project (Part 2 of 3) 00:00:00
27.6 ETL Error Handling Vehicle Service Project (Part 3 of 3)
27.6 ETL Error Handling Vehicle Service Project (Part 3 of 3) 00:00:00
28.1 Your special Bonus
28.1 Your special Bonus 00:00:00
Data Science A-Z Course Details
Data Science A-Z Course Details 00:00:00

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