Instructors
Data Science A-Z™ For digital marketing and finance
Data Science, Data Analysis, Data Analytics, Data Analyst, Data Mining, Tableau, Statistics, Modeling, SQL, SSIS
420 STUDENTS ENROLLED
Welcome to the world’s best-selling Data Science course, hugely popular with Marketing & Finance professionals – now, an integral part of any Digital marketers skill set.
“…Extremely Hands-On… Incredibly Practical… Unbelievably Real..”
“….The shortest route to meet the skills gap for modern marketers…”
This is way more than just a theoretical approach. We take a deep dive into all the practical elements of how to be a great data scientist. 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! However we also provide complete solutions, with practical exercises to raise your level of competence in the field.
This course will give you a full overview of the Data Science journey. Upon completing the training 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.
After helping thousands of marketers, including members of the American Marketing association, CIM and the IDM to upgrade their digital skills to include data science, we’d love to do the same for you.
The choice is yours. Join the class and start learning today!
See you inside.
Sincerely,
Kirill Eremenko
Course Curriculum
| Section 1. Get Excited | |||
| Welcome to Data Science A-Z™ | FREE | 00:04:00 | |
| Section 2. What is Data Science? | |||
| Introduction to Section 2 | 00:00:44 | ||
| Profession of the future | FREE | 00:07:00 | |
| Areas of Data Science | 00:06:00 | ||
| IMPORTANT: Course Pathways | 00:06:00 | ||
| Section 3. Part 1 - Visualisation | |||
| Welcome to Part 1 | 00:02:00 | ||
| Section 4. Introduction to Tableau | |||
| Introduction to Section 4 | 00:00:28 | ||
| Installing Tableau Desktop and Tableau Public | 00:06:00 | ||
| Challenge description + view data in file | 00:02:32 | ||
| Connecting Tableau to a Data file – CSV file | 00:05:00 | ||
| Navigating Tableau – Measures and Dimensions | 00:08:42 | ||
| Creating a calculated field | 00:06:15 | ||
| Adding colours | 00:07:38 | ||
| Adding labels and formatting | 00:11:00 | ||
| Exporting your worksheet | 00:07:40 | ||
| Section 4 Recap | 00:03:34 | ||
| Section 5. How to use Tableau for Data Mining | |||
| Introduction to Section 5 | 00:00:44 | ||
| Get the Dataset + Project Overview | 00:07:13 | ||
| Connecting Tableau to an Excel File | 00:03:57 | ||
| How to visualise an ad-hoc A-B test in Tableau | 00:06:29 | ||
| Working with Aliases | 00:04:06 | ||
| Adding a Reference Line | 00:06:33 | ||
| Looking for anomalies | 00:08:36 | ||
| Handy trick to validate your approach / data | 00:09:14 | ||
| Section 5 Recap | 00:05:05 | ||
| Section 6. Advanced Data Mining With Tableau | |||
| Introduction to Section 6 | 00:00:44 | ||
| Creating bins and Visualizing distributions | 00:09:55 | ||
| Creating a classification test for a numeric variable | 00:04:25 | ||
| Combining two charts and working with them in Tableau | 00:07:06 | ||
| Validating Tableau Data Mining with a Chi-Squared test | 00:10:29 | ||
| Chi-Squared test when there are more than 2 categories | 00:08:16 | ||
| Visualising Balance and Estimated Salary distribution | 00:11:05 | ||
| Bonus: Chi-Squared Test (Stats Tutorial) | 00:19:00 | ||
| Bonus: Chi-Squared Test Part 2 (Stats Tutorial) | 00:09:11 | ||
| Section 6 Recap | 00:05:54 | ||
| Section 7. Part 2 - Modelling | |||
| Welcome to Part 2 | 00:03:54 | ||
| Section 8. Stats Refresher | |||
| Introduction to Section 8 | 00:00:30 | ||
| Types of variables: Categorical vs Numeric | 00:05:26 | ||
| Types of regressions | 00:08:09 | ||
| Ordinary Least Squares | 00:03:11 | ||
| R-squared | 00:05:12 | ||
| Adjusted R-squared | 00:09:57 | ||
| Section 9. Simple Linear Regression | |||
| Introduction to Section 9 | 00:00:37 | ||
| Introduction to Gretl | 00:02:35 | ||
| Get the dataset – salary data | 00:04:04 | ||
| Import data and run descriptive statistics | 00:04:26 | ||
| Reading Linear Regression Output | 00:04:49 | ||
| Plotting and analysing the graph | 00:04:23 | ||
| Section 10. Multiple Linear Regression | |||
| Introduction to Section 10 | 00:01:15 | ||
| Caveat: assumptions of a linear regression | 00:01:48 | ||
| Get the dataset – 50 startups | 00:04:12 | ||
| Dummy Variables | 00:08:06 | ||
| Dummy Variable Trap | 00:02:11 | ||
| Ways to build a model: BACKWARD, FORWARD, STEPWISE | 00:15:42 | ||
| Backward Elimination – Practice time | 00:16:08 | ||
| Using Adjusted R-squared to create Robust models | 00:10:17 | ||
| Interpreting coefficients of MLR | 00:12:47 | ||
| Section 10 Recap | 00:03:09 | ||
| Section 11. Logistic Regression | |||
| Introduction to Section 11 | 00:01:35 | ||
| Get the dataset – email offer | 00:04:14 | ||
| Binary outcome: Yes/No-Type Business Problems | 00:09:09 | ||
| Logistic regression intuition | 00:17:00 | ||
| Your first logistic regression | 00:08:05 | ||
| False Positives and False Negatives | 00:08:00 | ||
| Confusion Matrix | 00:04:57 | ||
| Interpreting coefficients of a logistic regression | 00:10:04 | ||
| Section 12. Building a robust geodemographic segmentation model | |||
| Introduction to Section 12 | 00:01:02 | ||
| Get the dataset – churn modelling | 00:07:33 | ||
| What is geo-demographic segmenation? | 00:05:06 | ||
| Let’s build the model – first iteration | 00:08:27 | ||
| Let’s build the model – backward elimination: STEP-BY-STEP | 00:11:11 | ||
| Transforming independent variables | 00:10:09 | ||
| Creating derived variables | 00:06:09 | ||
| Checking for multicollinearity using VIF | 00:08:12 | ||
| Correlation Matrix and Multicollinearity Intuition | 00:08:21 | ||
| Model is Ready and Section Recap | 00:06:28 | ||
| Section 13. Assessing your model | |||
| Introduction to Section 13 | 00:00:36 | ||
| Accuracy paradox | 00:02:12 | ||
| Cumulative Accuracy Profile (CAP) | 00:11:17 | ||
| How to build a CAP curve in Excel | 00:14:48 | ||
| Assessing your model using the CAP curve | 00:07:12 | ||
| Get my CAP curve template | 00:06:20 | ||
| How to use test data to prevent overfitting your model | 00:03:35 | ||
| Applying the model to test data | 00:08:10 | ||
| Comparing training performance and test performance | 00:11:34 | ||
| Section 13 Recap | 00:03:33 | ||
| Section 14. Drawing insights from your model | |||
| Introduction to Section 14 | 00:00:34 | ||
| Power insights from your CAP | 00:13:53 | ||
| Coefficients of a Logistic Regression – Plan of Attack (advanced topic) | 00:03:47 | ||
| Odds ratio (advanced topic) | 00:08:30 | ||
| Odds Ratio vs Coefficients in a Logistic Regression (advanced topic) | 00:07:00 | ||
| Deriving insights from your coefficients (advanced topic) | 00:13:00 | ||
| Section 14 Recap | 00:03:26 | ||
| Section 15. Model maintenance | |||
| Introduction to Section 15 | 00:00:38 | ||
| What does model deterioration look like? | 00:04:37 | ||
| Why do models deteriorate? | 00:15:26 | ||
| Three levels of maintenance for deployed models | 00:08:21 | ||
| Section 15 Recap | 00:01:39 | ||
| Section 16. Part 3 - Data Preparation | |||
| Welcome to Part 3 | 00:02:25 | ||
| Section 17. Business Intelligence (BI) Tools | |||
| Introduction to Section 17 | 00:00:24 | ||
| Working with Data | 00:01:16 | ||
| What is a Data Warehouse? What is a Database? | 00:03:29 | ||
| Setting up Microsoft SQL Server 2014 for practice | 00:08:00 | ||
| Important: Practice Database | 00:09:44 | ||
| ETL for Data Science – what is Extract Transform Load (ETL)? | 00:02:01 | ||
| Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS? | 00:04:01 | ||
| Installing SSDT with MSVS Shell | 00:04:25 | ||
| Section 18. ETL Phase 1 - Data Wrangling before the Load | |||
| Introduction to Section 18 | 00:00:48 | ||
| Preparing your folder structure for your Data Science project | 00:02:21 | ||
| Download the dataset for section 18 | 00:01:28 | ||
| Two things you HAVE to do before the load | 00:04:56 | ||
| Notepad ++ | 00:01:01 | ||
| Editpad Lite | 00:01:11 | ||
| Section 19. ETL Phase 2 - Step-by-step guide to uploading data using SSIS | |||
| Introduction to Section 19 | 00:00:51 | ||
| Starting and navigating an SSIS Project | 00:01:47 | ||
| Creating a flat file source task and OLE DB destination | 00:01:54 | ||
| Setting up your flat file source connection | 00:06:09 | ||
| Setting up your database connection and creating a RAW table | 00:07:43 | ||
| Run the Upload & Disable | 00:02:40 | ||
| Due Diligence: Upload Quality Assurance | 00:02:02 | ||
| Section 20. Handling errors during ETL (Phases 1 & 2) | |||
| Introduction to Section 20 | 00:00:50 | ||
| Download the dataset for section 20 | 00:00:47 | ||
| How excel can mess up your data | 00:03:46 | ||
| Bulletproof Blueprint for Data Wrangling before the Load | 00:07:13 | ||
| SSIS Error: Text qualifier not specified | 00:07:16 | ||
| What do you do when your source file is corrupt? (Part 1) | 00:18:00 | ||
| What do you do when your source file is corrupt? (Part 2) | 00:06:10 | ||
| SSIS Error: Data truncation | 00:15:56 | ||
| Handy trick for finding anomalies in SQL | 00:03:46 | ||
| Automating Error Handling in SSIS: Conditional Split | 00:08:20 | ||
| Automating Error Handling in SSIS: Conditional Split (Level 2) | 00:09:03 | ||
| How to analyze the error files | 00:16:41 | ||
| Due Diligence: the one thing you HAVE to do every time | 00:04:57 | ||
| Types of Errors in SSIS | 00:04:01 | ||
| Section 20 Summary | 00:19:07 | ||
| Section 20 Homework | FREE | 00:03:39 | |
| Section 21. SQL Programming for Data Science | |||
| Introduction to Section 21 | 00:00:32 | ||
| Download the dataset for section 21 | 00:00:38 | ||
| Getting To Know MS SQL Management Studio | 00:02:14 | ||
| Shortcut to upload the data | 00:04:20 | ||
| SELECT * Statement | 00:05:53 | ||
| Using the WHERE clause to filter data | 00:05:50 | ||
| How to use Wildcards / Regular Expressions in SQL (% and _) | 00:04:39 | ||
| Comments in SQL | 00:02:44 | ||
| Order By | 00:05:49 | ||
| Data Types in SQL | 00:07:54 | ||
| Implicit Data Conversion in SQL | 00:03:35 | ||
| Using Cast() vs Convert() | 00:03:51 | ||
| Working with NULLs | 00:05:04 | ||
| Understanding how LEFT, RIGHT, INNER, and OUTER joins work | 00:06:18 | ||
| Joins with duplicate values | 00:02:33 | ||
| Joining on multiple fields | 00:05:21 | ||
| Practicing Joins | 00:05:01 | ||
| Section 22. ETL Phase 3 - Data Wrangling after the load | |||
| Introduction to Section 22 | 00:00:57 | ||
| RAW, WRK, DRV tables | 00:04:55 | ||
| Download the dataset for section 22 | 00:01:32 | ||
| Create your first Stored Proc in SQL | 00:03:31 | ||
| Executing Stored Procedures | 00:02:50 | ||
| Modifying Stored Procedures | 00:08:26 | ||
| Create table | 00:09:30 | ||
| Insert INTO | 00:05:42 | ||
| Check if table exists + drop table + Truncate | 00:06:00 | ||
| Intermediate Recap – Procs | 00:04:16 | ||
| Create the proc for the second file | 00:11:36 | ||
| Adding leading zeros | 00:07:29 | ||
| Converting data on the fly | 00:10:22 | ||
| How to create a proc template | 00:07:52 | ||
| Archiving Procs | 00:04:38 | ||
| What you can do with these tables going forward [drv files etc.] | 00:13:50 | ||
| Section 23. Handling errors during ETL (Phase 3) | |||
| Introduction to Section 23 | 00:00:54 | ||
| Download the dataset for section 23 | 00:00:47 | ||
| Upload the data to RAW table | 00:11:03 | ||
| Create Stored Proc | 00:05:09 | ||
| How to deal with errors using the isnumeric() function | 00:07:45 | ||
| How to deal errors using the len() function | 00:07:37 | ||
| How to deal with errors using the isdate() function | 00:07:40 | ||
| Additional Quality Assurance check: Balance | 00:03:52 | ||
| Additional Quality Assurance check: ZipCode | 00:03:17 | ||
| Additional Quality Assurance check: Birthday | 00:04:09 | ||
| Part Completed | 00:09:53 | ||
| ETL Error Handling “Vehicle Service” Project | 00:07:45 | ||
| Section 24. Part 4 - Communication | |||
| Welcome to Part 4 | 00:01:32 | ||
| Section 25. Working with people | |||
| Introduction to Section 25 | 00:00:45 | ||
| Cross-departmental Work | 00:04:13 | ||
| Come to me with a Business Problem | 00:02:11 | ||
| Setting expectations and pre-project communication | 00:03:30 | ||
| Go and sit with them | 00:05:20 | ||
| The art of saying “No” | 00:05:24 | ||
| Sometimes you have to go to the top | 00:02:37 | ||
| Building a data culture | 00:05:08 | ||
| Section 26. Presenting for Data Scientists | |||
| Introduction to Section 26 | 00:01:42 | ||
| Case study | 00:02:00 | ||
| Analysing the intro | 00:03:34 | ||
| Intro dissection – recap | 00:09:27 | ||
| REAL Data Science Presentation Walkthrough | 00:16:29 | ||
| My brainstorming method | 00:03:03 | ||
| How to present to executives | 00:05:27 | ||
| The truth is not always pretty | 00:00:00 | ||
| Passion and the Wow-factor | 00:01:59 | ||
| Bonus: my full presentation | LIVE 2015 | 00:16:20 | ||
| Section 27. Homework Solutions | |||
| Advanced Data Mining with Tableau: Visualising Credit Score & Tenure | 00:05:44 | ||
| Advanced Data Mining with Tableau: Chi-Squared Test for Country | 00:04:18 | ||
| ETL Error Handling (Phases 1 and 2) | 00:20:00 | ||
| ETL Error Handling “Vehicle Service” Project (Part 1 of 3) | 00:19:00 | ||
| ETL Error Handling “Vehicle Service” Project (Part 2 of 3) | 00:10:00 | ||
| ETL Error Handling “Vehicle Service” Project (Part 3 of 3) | 00:14:00 | ||
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