Globalcademy

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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