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