Module 1 Section 1: Get Excited
Unit 1 Welcome to Data Science A-Z™
Module 2 Section 2: What is Data Science?
Unit 1 Intro (what you will learn in this section)
Unit 2 Profession of the future
Unit 3 Areas of Data Science
Unit 4 IMPORTANT: Course Pathways
Module 3 Section 3: --- Part 1: Visualisation ---
Unit 1 Welcome to Part 1
Module 4 Section 4: Introduction to Tableau
Unit 1 Intro (what you will learn in this section)
Unit 2 Installing Tableau Desktop and Tableau Public (FREE)
Unit 3 Challenge description + view data in file
Unit 4 Connecting Tableau to a Data file - CSV file
Unit 5 Navigating Tableau - Measures and Dimensions
Unit 6 Creating a calculated field
Unit 7 Adding colours
Unit 8 Adding labels and formatting
Unit 9 Exporting your worksheet
Unit 10 Section Recap
Unit 11 Quiz 1 - Tableau Basics
Module 5 Section 5: How to use Tableau for Data Mining
Unit 1 Intro (what you will learn in this section)
Unit 2 Get the Dataset + Project Overview
Unit 3 Connecting Tableau to an Excel File
Unit 4 How to visualise an ad-hoc A-B test in Tableau
Unit 5 Working with Aliases
Unit 6 Adding a Reference Line
Unit 7 Looking for anomalies
Unit 8 Handy trick to validate your approach / data
Unit 9 Section Recap
Module 6 Section 6: Advanced Data Mining With Tableau
Unit 1 Intro (what you will learn in this section)
Unit 2 Creating bins & Visualizing distributions
Unit 3 Creating a classification test for a numeric variable
Unit 4 Combining two charts and working with them in Tableau
Unit 5 Validating Tableau Data Mining with a Chi-Squared test
Unit 6 Chi-Squared test when there is more than 2 categories
Unit 7 Visualising Balance and Estimated Salary distribution
Unit 8 Bonus: Chi-Squared Test (Stats Tutorial)
Unit 9 Bonus: Chi-Squared Test Part 2 (Stats Tutorial)
Unit 10 Section Recap
Module 7 Section 7: --- Part 2: Modelling ---
Unit 1 Welcome to Part 2
Module 8 Section 8: Stats Refresher
Unit 1 Intro (what you will learn in this section)
Unit 2 Types of variables: Categorical vs Numeric
Unit 3 Types of regressions
Unit 4 Ordinary Least Squares
Unit 5 R-squared
Unit 6 Adjusted R-squared
Module 9 Section 9: Simple Linear Regression
Unit 1 Intro (what you will learn in this section)
Unit 2 Introduction to Gretl
Unit 3 Get the dataset
Unit 4 Import data and run descriptive statistics
Unit 5 Reading Linear Regression Output
Unit 6 Plotting and analysing the graph
Module 10 Section 10: Multiple Linear Regression
Unit 1 Intro (what you will learn in this section)
Unit 2 Caveat: assumptions of a linear regression
Unit 3 Get the dataset
Unit 4 Dummy Variables
Unit 5 Dummy Variable Trap
Unit 6 Ways to build a model: BACKWARD, FORWARD, STEPWISE
Unit 7 Backward Elimination - Practice time
Unit 8 Using Adjusted R-squared to create Robust models
Unit 9 Interpreting coefficients of MLR
Unit 10 Section Recap
Module 11 Section 11: Logistic Regression
Unit 1 Intro (what you will learn in this section)
Unit 2 Get the dataset
Unit 3 Binary outcome: Yes/No-Type Business Problems
Unit 4 Logistic regression intuition
Unit 5 Your first logistic regression
Unit 6 False Positives and False Negatives
Unit 7 Confusion Matrix
Unit 8 Interpreting coefficients of a logistic regression
Module 12 Section 12: Building a robust geodemographic segmentation model
Unit 1 Intro (what you will learn in this section)
Unit 2 Get the dataset
Unit 3 What is geo-demographic segmenation?
Unit 4 Let's build the model - first iteration
Unit 5 Let's build the model - backward elimination: STEP-BY-STEP
Unit 6 Transforming independent variables
Unit 7 Creating derived variables
Unit 8 Checking for multicollinearity using VIF
Unit 9 Correlation Matrix and Multicollinearity Intuition
Unit 10 Model is Ready and Section Recap
Module 13 Section 13: Assessing your model
Unit 1 Intro (what you will learn in this section)
Unit 2 Accuracy paradox
Unit 3 Cumulative Accuracy Profile (CAP)
Unit 4 How to build a CAP curve in Excel
Unit 5 Assessing your model using the CAP curve
Unit 6 Get my CAP curve template
Unit 7 How to use test data to prevent overfitting your model
Unit 8 Applying the model to test data
Unit 9 Comparing training performance and test performance
Unit 10 Section Recap
Module 14 Section 14: Drawing insights from your model
Unit 1 Intro (what you will learn in this section)
Unit 2 Power insights from your CAP
Unit 3 Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
Unit 4 Odds ratio (advanced topic)
Unit 5 Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
Unit 6 Deriving insights from your coefficients (advanced topic)
Unit 7 Section Recap
Module 15 Section 15: Model maintenance
Unit 1 Intro (what you will learn in this section)
Unit 2 What does model deterioration look like?
Unit 3 Why do models deteriorate?
Unit 4 Three levels of maintenance for deployed models
Unit 5 Section Recap
Module 16 Section 16: --- Part 3: Data Preparation ---
Unit 1 Welcome to Part 3
Module 17 Section 17: Business Intelligence (BI) Tools
Unit 1 Intro (what you will learn in this section)
Unit 2 Working with Data
Unit 3 What is a Data Warehouse? What is a Database?
Unit 4 Setting up Microsoft SQL Server 2014 for practice
Unit 5 Important: Practice Database
Unit 6 ETL for Data Science - what is Extract Transform Load (ETL)?
Unit 7 Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS ?
Unit 8 Installing SSDT with MSVS Shell
Module 18 Section 18: ETL Phase 1: Data Wrangling before the Load
Unit 1 Intro (what you will learn in this section)
Unit 2 Preparing your folder structure for your Data Science project
Unit 3 Download the dataset for this section
Unit 4 Two things you HAVE to do before the load
Unit 5 Notepad ++
Unit 6 Editpad Lite
Module 19 Section 19: ETL Phase 2: Step-by-step guide to uploading data using SSIS
Unit 1 Intro (what you will learn in this section)
Unit 2 Starting and navigating an SSIS Project
Unit 3 Creating a flat file source task and OLE DB destination
Unit 4 Setting up your flat file source connection
Unit 5 Setting up your database connection and creating a RAW table
Unit 6 Run the Upload & Disable
Unit 7 Due Dilligence: Upload Quality Assurance
Module 20 Section 20: Handling errors during ETL (Phases 1 & 2)
Unit 1 Intro (what you will learn in this section)
Unit 2 Download the dataset for this section
Unit 3 How excel can mess up your data
Unit 4 Bulletproof Blueprint for Data Wrangling before the Load
Unit 5 SSIS Error: Text qualifier not specified
Unit 6 What do you do when your source file is corrupt? (Part 1)
Unit 7 What do you do when your source file is corrupt? (Part 2)
Unit 8 SSIS Error: Data truncation
Unit 9 Handy trick for finding anomalies in SQL
Unit 10 Automating Error Handling in SSIS: Conditional Split
Unit 11 Automating Error Handling in SSIS: Conditional Split (Level 2)
Unit 12 How to analyze the error files
Unit 13 Due Dilligence: the one thing you HAVE to do every time
Unit 14 Types of Errors in SSIS
Unit 15 Summary
Unit 16 Homework
Module 21 Section 21: SQL Programming for Data Science
Unit 1 Intro (what you will learn in this section)
Unit 2 Download the dataset for this section
Unit 3 Getting To Know MS SQL Management Studio
Unit 4 Shortcut to upload the data
Unit 5 SELECT * Statement
Unit 6 Using the WHERE clause to filter data
Unit 7 How to use Wildcards / Regular Expressions in SQL (% and _)
Unit 8 Comments in SQL
Unit 9 Order By
Unit 10 Data Types in SQL
Unit 11 Implicit Data Conversion in SQL
Unit 12 Using Cast() vs Convert()
Unit 13 Working with NULLs
Unit 14 Understanding how LEFT, RIGHT, INNER, and OUTER joins work
Unit 15 Joins with duplicate values
Unit 16 Joining on multiple fields
Unit 17 Practicing Joins
Module 22 Section 22: ETL Phase 3: Data Wrangling after the load
Unit 1 Intro (what you will learn in this section)
Unit 2 RAW, WRK, DRV tables
Unit 3 Download the dataset for this section
Unit 4 Create your first Stored Proc in SQL
Unit 5 Executing Stored Procedures
Unit 6 Modifying Stored Procedures
Unit 7 Create table
Unit 8 Insert INTO
Unit 9 Check if table exists + drop table + Truncate
Unit 10 Intermediate Recap - Procs
Unit 11 Create the proc for the second file
Unit 12 Adding leading zeros
Unit 13 Converting data on the fly
Unit 14 How to create a proc template
Unit 15 Archiving Procs
Unit 16 What you can do with these tables going forward [drv files etc.]
Module 23 Section 23: Handling errors during ETL (Phase 3)
Unit 1 Intro (what you will learn in this section)
Unit 2 Download the dataset for this section
Unit 3 Upload the data to RAW table
Unit 4 Create Stored Proc
Unit 5 How to deal with errors using the isnumeric() function
Unit 6 How to deal errors using the len() function
Unit 7 How to deal with errors using the isdate() function
Unit 8 Additional Quality Assurance check: Balance
Unit 9 Additional Quality Assurance check: ZipCode
Unit 10 Additional Quality Assurance check: Birthday
Unit 11 Part Completed
Unit 12 ETL Error Handling "Vehicle Service" Project
Module 24 Section 24: --- Part 4: Communication ---
Unit 1 Welcome to Part 4
Module 25 Section 25: Working with people
Unit 1 Intro (what you will learn in this section)
Unit 2 Cross-departmental Work
Unit 3 Come to me with a Business Problem
Unit 4 Setting expectations and pre-project communication
Unit 5 Go and sit with them
Unit 6 The art of saying "No"
Unit 7 Sometimes you have to go to the top
Unit 8 Building a data culture
Module 26 Section 26: Presenting for Data Scientists
Unit 1 Intro (what you will learn in this section)
Unit 2 Case study
Unit 3 Analysing the intro
Unit 4 Intro dissection - recap
Unit 5 REAL Data Science Presentation Walkthrough - Make Your Audience Say "WOW"
Unit 6 My brainstorming method
Unit 7 How to present to executives
Unit 8 The truth is not always pretty
Unit 9 Passion and the Wow-factor
Unit 10 Bonus: my full presentation | LIVE 2015
Module 27 Section 27: Homework Solutions
Unit 1 Advanced Data Mining with Tableau: Visualising Credit Score & Tenure
Unit 2 Advanced Data Mining with Tableau: Chi-Squared Test for Country
Unit 3 ETL Error Handling (Phases 1 and 2)
Unit 4 ETL Error Handling "Vehicle Service" Project (Part 1 of 3)
Unit 5 ETL Error Handling "Vehicle Service" Project (Part 2 of 3)
Unit 6 ETL Error Handling "Vehicle Service" Project (Part 3 of 3)
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Data Science A-Z

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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!