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

Video Introducing this tutorial


Get Excited :
Welcome to Data Science A-Z™

What is Data Science? :
Intro (what you will learn in this section)
Updates on Udemy Reviews
Profession of the future
Areas of Data Science
IMPORTANT: Course Pathways
Some Additional Resources!!

--------------------------- Part 1: Visualisation --------------------------- :
Welcome to Part 1

Introduction to Tableau :
Intro (what you will learn in this section)
Installing Tableau Desktop and Tableau Public (FREE)
Challenge description + view data in file
Connecting Tableau to a Data file - CSV file
Navigating Tableau - Measures and Dimensions
Creating a calculated field
Adding colours
Adding labels and formatting
Exporting your worksheet
Section Recap
Tableau Basics
5 questions

How to use Tableau for Data Mining :
Intro (what you will learn in this section)
Get the Dataset + Project Overview
Connecting Tableau to an Excel File
How to visualise an ad-hoc A-B test in Tableau
Working with Aliases
Adding a Reference Line
Looking for anomalies
Handy trick to validate your approach / data
Section Recap

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

--------------------------- Part 2: Modelling --------------------------- :
Welcome to Part 2

Stats Refresher :
Intro (what you will learn in this section)
Types of variables: Categorical vs Numeric
Types of regressions
Ordinary Least Squares
R-squared
Adjusted R-squared

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

Multiple Linear Regression :
Intro (what you will learn in this section)
Caveat: assumptions of a linear regression
Get the dataset
Dummy Variables
Dummy Variable Trap
Ways to build a model: BACKWARD, FORWARD, STEPWISE
Backward Elimination - Practice time
Using Adjusted R-squared to create Robust models
Interpreting coefficients of MLR
Section Recap
Logistic Regression
8 lectures
Intro (what you will learn in this section)
Get the dataset
Binary outcome: Yes/No-Type Business Problems
Logistic regression intuition
Your first logistic regression
False Positives and False Negatives
Confusion Matrix
Interpreting coefficients of a logistic regression
Building a robust geodemographic segmentation model
10 lectures
Intro (what you will learn in this section)
Get the dataset
What is geo-demographic segmenation?
Let's build the model - first iteration
Let's build the model - backward elimination: STEP-BY-STEP
Transforming independent variables
Creating derived variables
Checking for multicollinearity using VIF
Correlation Matrix and Multicollinearity Intuition
Model is Ready and Section Recap
Assessing your model
10 lectures
Intro (what you will learn in this section)
Accuracy paradox
Cumulative Accuracy Profile (CAP)
How to build a CAP curve in Excel
Assessing your model using the CAP curve
Get my CAP curve template
How to use test data to prevent overfitting your model
Applying the model to test data
Comparing training performance and test performance
Section Recap
Drawing insights from your model
7 lectures
Intro (what you will learn in this section)
Power insights from your CAP
Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
Odds ratio (advanced topic)
Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
Deriving insights from your coefficients (advanced topic)
Section Recap
Model maintenance
5 lectures
Intro (what you will learn in this section)
What does model deterioration look like?
Why do models deteriorate?
Three levels of maintenance for deployed models
Section Recap
--------------------------- Part 3: Data Preparation ---------------------------
1 lecture
Welcome to Part 3
Business Intelligence (BI) Tools
8 lectures
Intro (what you will learn in this section)
Working with Data
What is a Data Warehouse? What is a Database?
Setting up Microsoft SQL Server 2014 for practice
Important: Practice Database
ETL for Data Science - what is Extract Transform Load (ETL)?
Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS ?
Installing SSDT with MSVS Shell
ETL Phase 1: Data Wrangling before the Load
6 lectures
Intro (what you will learn in this section)
Preparing your folder structure for your Data Science project
Download the dataset for this section
Two things you HAVE to do before the load
Notepad ++
Editpad Lite
ETL Phase 2: Step-by-step guide to uploading data using SSIS
7 lectures
Intro (what you will learn in this section)
Starting and navigating an SSIS Project
Creating a flat file source task and OLE DB destination
Setting up your flat file source connection
Setting up your database connection and creating a RAW table
Run the Upload & Disable
Due Dilligence: Upload Quality Assurance
Handling errors during ETL (Phases 1 & 2)
16 lectures
Intro (what you will learn in this section)
Download the dataset for this section
How excel can mess up your data
Bulletproof Blueprint for Data Wrangling before the Load
SSIS Error: Text qualifier not specified
What do you do when your source file is corrupt? (Part 1)
What do you do when your source file is corrupt? (Part 2)
SSIS Error: Data truncation
Handy trick for finding anomalies in SQL
Automating Error Handling in SSIS: Conditional Split
Automating Error Handling in SSIS: Conditional Split (Level 2)
How to analyze the error files
Due Dilligence: the one thing you HAVE to do every time
Types of Errors in SSIS
Summary
Homework
SQL Programming for Data Science
17 lectures
Intro (what you will learn in this section)
Download the dataset for this section
Getting To Know MS SQL Management Studio
Shortcut to upload the data
SELECT * Statement
Using the WHERE clause to filter data
How to use Wildcards / Regular Expressions in SQL (% and _)
Comments in SQL
Order By
Data Types in SQL
Implicit Data Conversion in SQL
Using Cast() vs Convert()
Working with NULLs
Understanding how LEFT, RIGHT, INNER, and OUTER joins work
Joins with duplicate values
Joining on multiple fields
Practicing Joins
ETL Phase 3: Data Wrangling after the load
16 lectures
Intro (what you will learn in this section)
RAW, WRK, DRV tables
Download the dataset for this section
Create your first Stored Proc in SQL
Executing Stored Procedures
Modifying Stored Procedures
Create table
Insert INTO
Check if table exists + drop table + Truncate
Intermediate Recap - Procs
Create the proc for the second file
Adding leading zeros
Converting data on the fly
How to create a proc template
Archiving Procs
What you can do with these tables going forward [drv files etc.]
Handling errors during ETL (Phase 3)
12 lectures
Intro (what you will learn in this section)
Download the dataset for this section
Upload the data to RAW table
Create Stored Proc
How to deal with errors using the isnumeric() function
How to deal errors using the len() function
How to deal with errors using the isdate() function
Additional Quality Assurance check: Balance
Additional Quality Assurance check: ZipCode
Additional Quality Assurance check: Birthday
Part Completed
ETL Error Handling "Vehicle Service" Project
--------------------------- Part 4: Communication ---------------------------
1 lecture
Welcome to Part 4
Working with people
8 lectures
Intro (what you will learn in this section)
Cross-departmental Work
Come to me with a Business Problem
Setting expectations and pre-project communication
Go and sit with them
The art of saying "No"
Sometimes you have to go to the top
Building a data culture
Presenting for Data Scientists
11 lectures
Intro (what you will learn in this section)
Case study
Analysing the intro
Intro dissection - recap
REAL Data Science Presentation Walkthrough - Make Your Audience Say "WOW"
My brainstorming method
How to present to executives
The truth is not always pretty
Passion and the Wow-factor
Bonus: my full presentation | LIVE 2015
Bonus: links to other examples of good storytelling
Homework Solutions
6 lectures
Advanced Data Mining with Tableau: Visualising Credit Score & Tenure
Advanced Data Mining with Tableau: Chi-Squared Test for Country
ETL Error Handling (Phases 1 and 2)
ETL Error Handling "Vehicle Service" Project (Part 1 of 3)
ETL Error Handling "Vehicle Service" Project (Part 2 of 3)
ETL Error Handling "Vehicle Service" Project (Part 3 of 3)
Bonus Lectures
1 lecture
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