Master Data Science from Start to Finish with Practical Real-World Projects

Getting Started with Confidence

1. Welcome To Data Science A-Z™

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2. BONUS Learning Paths

Understanding the Fundamentals of Data Science

Section 1: Data Visualisation

Getting Started with Tableau

1. Intro (What You Will Learn In This Section)

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2. Installing Tableau Desktop And Tableau Public (Free)

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3. Challenge Description + View Data In File

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4. Connecting Tableau To A Data File – Csv File

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5. Navigating Tableau – Measures And Dimensions

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6. Creating A Calculated Field

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7. Adding Colours

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8. Adding Labels And Formatting

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9. Exporting Your Worksheet

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10. Section Recap

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11. Tableau Basics

Using Tableau for Effective Data Mining

1. Intro (What You Will Learn In This Section)

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2. Get The Dataset + Project Overview

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3. Connecting Tableau To An Excel File

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4. How To Visualise An Ab Test In Tableau

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5. Working With Aliases

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6. Adding A Reference Line

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7. Looking For Anomalies

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8. Handy Trick To Validate Your Approach Data

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9. Section Recap

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Advanced Data Mining Techniques Using Tableau

1. Intro (What You Will Learn In This Section)

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2. Creating Bins & Visualizing Distributions

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3. Creating A Classification Test For A Numeric Variable

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4. Combining Two Charts And Working With Them In Tableau

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5. Validating Tableau Data Mining With A Chi-Squared Test

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6. Chi-Squared Test When There Is More Than 2 Categories

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7. Visualising Balance And Estimated Salary Distribution

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8. Bonus Chi-Squared Test (Stats Tutorial)

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9. Bonus Chi-Squared Test Part 2 (Stats Tutorial)

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10. Section Recap

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11. Part Completed

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Section 2: Data Modelling

Statistics Fundamentals Refresher

Introduction to Simple Linear Regression

Understanding Multiple Linear Regression

1. Intro (What You Will Learn In This Section)

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2. Caveat Assumptions Of A Linear Regression

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3. Get The Dataset

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4. Dummy Variables

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5. Dummy Variable Trap

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6. Ways To Build A Model Backward, Forward, Stepwise

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7. Backward Elimination – Practice Time

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8. Using Adjusted R-Squared To Create Robust Models

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9. Interpreting Coefficients Of Mlr

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10. Section Recap

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Exploring Logistic Regression Models

Creating a Reliable Geodemographic Segmentation Model

1. Intro (What You Will Learn In This Section)

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2. Get The Dataset

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3. What Is Geo-Demographic Segmenation

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4. Let’s Build The Model – First Iteration

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5. Let’s Build The Model – Backward Elimination Step-By-Step

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6. Transforming Independent Variables

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7. Creating Derived Variables

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8. Checking For Multicollinearity Using Vif

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9. Correlation Matrix And Multicollinearity Intuition

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10. Model Is Ready And Section Recap

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Evaluating Model Performance

1. Intro (What You Will Learn In This Section)

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2. Accuracy Paradox

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3. Cumulative Accuracy Profile (Cap)

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4. How To Build A Cap Curve In Excel

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5. Assessing Your Model Using The Cap Curve

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6. Get My Cap Curve Template

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7. How To Use Test Data To Prevent Overfitting Your Model

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8. Applying The Model To Test Data

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9. Comparing Training Performance And Test Performance

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10. Section Recap

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Extracting Valuable Insights from Your Model

Maintaining and Optimising Your Model

Section 3: Data Preparation

Introduction to Business Intelligence (BI) Tools

ETL Phase 1: Data Wrangling Before Loading

ETL Phase 2: Uploading Data with SSIS – A Step-by-Step Guide

Resolving Common ETL Errors in Phases 1 & 2

1. Intro (What You Will Learn In This Section)

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2. Download The Dataset For This Section

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3. How Excel Can Mess Up Your Data

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4. Bulletproof Blueprint For Data Wrangling Before The Load

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5. Ssis Error Text Qualifier Not Specified

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6. What Do You Do When Your Source File Is Corrupt (Part 1)

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7. What Do You Do When Your Source File Is Corrupt (Part 2)

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8. Ssis Error Data Truncation

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9. Handy Trick For Finding Anomalies In Sql

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10. Automating Error Handling In Ssis Conditional Split

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11. Automating Error Handling In Ssis Conditional Split (Level 2)

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12. How To Analyze The Error Files

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13. Due Dilligence The One Thing You Have To Do Every Time

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14. Types Of Errors In Ssis

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15. Summary

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16. Homework

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SQL Programming Essentials for Data Science

1. Intro (What You Will Learn In This Section)

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2. Download The Dataset For This Section

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3. Getting To Know Ms Sql Management Studio

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4. Shortcut To Upload The Data

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5. Select Statement

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6. Using The Where Clause To Filter Data

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7. How To Use Wildcards Regular Expressions In Sql (% And )

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8. Comments In Sql

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9. Order By

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10. Data Types In Sql

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11. Implicit Data Conversion In Sql

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12. Using Cast() Vs Convert()

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13. Working With Nulls

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14. Understanding How Left, Right, Inner, And Outer Joins Work

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15. Joins With Duplicate Values

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16. Joining On Multiple Fields

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17. Practicing Joins

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ETL Phase 3: Data Wrangling After Loading

1. Intro (What You Will Learn In This Section)

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2. Raw, Wrk, Drv Tables

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3. Download The Dataset For This Section

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4. Create Your First Stored Proc In Sql

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5. Executing Stored Procedures

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6. Modifying Stored Procedures

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7. Create Table

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8. Insert Into

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9. Check If Table Exists + Drop Table + Truncate

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10. Intermediate Recap – Procs

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11. Create The Proc For The Second File

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12. Adding Leading Zeros

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13. Converting Data On The Fly

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14. How To Create A Proc Template

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15. Archiving Procs

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16. What You Can Do With These Tables Going Forward [Drv Files Etc.]

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Troubleshooting ETL Issues in Phase 3

1. Intro (What You Will Learn In This Section)

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2. Download The Dataset For This Section

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3. Upload The Data To Raw Table

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4. Create Stored Proc

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5. How To Deal With Errors Using The Isnumeric() Function

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6. How To Deal Errors Using The Len() Function

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7. How To Deal With Errors Using The Isdate() Function

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8. Additional Quality Assurance Check Balance

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9. Additional Quality Assurance Check Zipcode

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10. Additional Quality Assurance Check Birthday

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11. Part Completed

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12. Etl Error Handling Vehicle Service Project

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Section 4: Communication Skills

Effective Collaboration and Teamwork

Presentation Skills for Data Scientists

1. Intro (What You Will Learn In This Section)

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2. Case Study

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3. Analysing The Intro

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4. Intro Dissection – Recap

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5. Real Data Science Presentation Walkthrough – Make Your Audience Say Wow

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6. My Brainstorming Method

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7. How To Present To Executives

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8. The Truth Is Not Always Pretty

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9. Passion And The Wow-Factor

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10. Bonus My Full Presentation Live 2015

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Course Content

Getting Started with Confidence

1. Welcome To Data Science A-Z™

2. BONUS Learning Paths

Understanding the Fundamentals of Data Science

1. Intro (What You Will Learn In This Section)

2. Updates On Udemy Reviews

3. Profession Of The Future

4. Areas Of Data Science

5. Important Course Pathways

6. Some Additional Resources

Section 1: Data Visualisation

1. Welcome To Part 1

Getting Started with Tableau

1. Intro (What You Will Learn In This Section)

2. Installing Tableau Desktop And Tableau Public (Free)

3. Challenge Description + View Data In File

4. Connecting Tableau To A Data File – Csv File

5. Navigating Tableau – Measures And Dimensions

6. Creating A Calculated Field

7. Adding Colours

8. Adding Labels And Formatting

9. Exporting Your Worksheet

10. Section Recap

11. Tableau Basics

Using Tableau for Effective Data Mining

1. Intro (What You Will Learn In This Section)

2. Get The Dataset + Project Overview

3. Connecting Tableau To An Excel File

4. How To Visualise An Ab Test In Tableau

5. Working With Aliases

6. Adding A Reference Line

7. Looking For Anomalies

8. Handy Trick To Validate Your Approach Data

9. Section Recap

Advanced Data Mining Techniques Using Tableau

1. Intro (What You Will Learn In This Section)

2. Creating Bins & Visualizing Distributions

3. Creating A Classification Test For A Numeric Variable

4. Combining Two Charts And Working With Them In Tableau

5. Validating Tableau Data Mining With A Chi-Squared Test

6. Chi-Squared Test When There Is More Than 2 Categories

7. Visualising Balance And Estimated Salary Distribution

8. Bonus Chi-Squared Test (Stats Tutorial)

9. Bonus Chi-Squared Test Part 2 (Stats Tutorial)

10. Section Recap

11. Part Completed

Section 2: Data Modelling

1. Welcome To Part 2

Statistics Fundamentals Refresher

1. Intro (What You Will Learn In This Section)

2. Types Of Variables Categorical Vs Numeric

3. Types Of Regressions

4. Ordinary Least Squares

5. R-Squared

6. Adjusted R-Squared

Introduction to Simple Linear Regression

1. Intro (What You Will Learn In This Section)

2. Introduction To Gretl

3. Get The Dataset

4. Import Data And Run Descriptive Statistics

5. Reading Linear Regression Output

6. Plotting And Analysing The Graph

Understanding Multiple Linear Regression

1. Intro (What You Will Learn In This Section)

2. Caveat Assumptions Of A Linear Regression

3. Get The Dataset

4. Dummy Variables

5. Dummy Variable Trap

6. Ways To Build A Model Backward, Forward, Stepwise

7. Backward Elimination – Practice Time

8. Using Adjusted R-Squared To Create Robust Models

9. Interpreting Coefficients Of Mlr

10. Section Recap

Exploring Logistic Regression Models

1. Intro (What You Will Learn In This Section)

2. Get The Dataset

3. Binary Outcome Yesno-Type Business Problems

4. Logistic Regression Intuition

5. Your First Logistic Regression