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Advanced Data Science: Master Deep Web Experiment Analysis!

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Become the data scientist in demand by learning much deeper and agile experiment analysis methods using SQL and Tableau!

364 students enrolled

- 5 hours on-demand video
- 4 Supplemental Resources
- 13 Coding exercises
- Full lifetime access
- Access on mobile and TV

- Certificate of Completion

What Will I Learn?

- Understand the key underlying concepts for advanced experiment analytics
- Build the back end and front end for a deep and agile experiment analytics dashboard
- Understand the general strategy for how to explore experimental metrics.

Requirements

- The intro level course by the instructor, "Data Science 4 Newbs: Skills + Basic Web Experiment Analysis", or equivalent, is highly recommended.
- A Mac or Windows computer with internet access.
- Some experience with Unix, SQL, Tableau, and basic statistics.

Description

This course teaches students how to compute and visualize metrics from controlled experiments in a deeper way than is usually taught using a project and assessment based approach. This course will be extremely useful for anybody who needs to compute or understand experiment analytics, especially in consumer Internet. The course includes analysis exercises and a final exam. The analysis exercises will ensure a rigorous learning process for the students who have the determination to complete the course. This course will enable the student to become the most highly sought after data scientist in the company.

Who is the target audience?

- This advanced course is designed for folks who would like to learn bring their data science career to the next level.

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Curriculum For This Course

Expand All 27 Lectures
Collapse All 27 Lectures
05:01:41

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Free intro to the instructor and course
5 Lectures
50:11

Check out the instructor and understand his approach to data science. Larry describes his qualifications and gives a concrete definition of 'data science.'

Preview
08:39

Walk thru of the topics you will learn in the course. This lecture ties together the curriculum into a coherent whole and explains the vision for the course.

Preview
12:06

Understand the rationale for the data used in this course. The data sets were carefully designed and constructed by the instructor to enable the students to participate in the calculations and get accurate feedback in the assessments.

Preview
09:45

Make sure you can download and use the free SQL tool. This tool is nice because you don't need to setup any database, but still benefit from the compact notation of SQL for complex data processing.

Preview
09:30

Test drive the SQL command line tool

1 question

Download and test the free tool for creating visualizations and doing analysis in this course. Tableau is an excellent and relatively straightforward tool for building sophisticated interactive dashboards.

Preview
10:11

Test drive the Tableau Public application

1 question

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Creating and using statistical units
5 Lectures
01:11:02

Walk through the material covered in this section. Understand how the lectures in this section build upon each other and the overall vision for creating and using statistical units in an analytics hypercube.

Preview
08:08

Use SQL to compute statistical units and key business metrics. This lecture shows you how to create the table used for self testing in the next lecture.

The core statistical unit

09:33

Use SQL to compute test metrics for input into the coding exercise. This lecture shows you how to do some basic calculations for the following assessment using the table built in the previous lecture.

The core statistical unit: test yourself

18:43

The core statistical unit

1 question

Add experiment variant and other exploratory features to the statistical units. This lecture shows you how to make the table as well as run some calculations on it for input into the following assessment.

Add features to statistical units

15:54

Add features to statistical units

1 question

Learn how to compress the experiment analytics into a hypercube. This lecture wraps up the section and shows you how to run some calculations on the hypercube table for input into the following assessment.

The analytics hypercube

18:44

The analytics hypercube

1 question

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Visualization and analysis of experiments
9 Lectures
01:41:43

This lecture walks through the material covered in this section. The lecture describes how the lectures in this section build on each other and culminate in the creation of the interactive dashboard.

Preview
02:56

Compute the trend and deviations of variant traffic in Tableau. Learn how to build a basic visualization.

Experiment integrity analysis

12:45

Learn how to compute and visualize statistical significance of traffic fluctuations. Extract some computed numbers from Tableau for input into the following assessment.

Experiment integrity analysis: significance of fluctuations

15:44

Experiment integrity analysis

1 question

Learn how to setup variables for dynamically computing statistical significance in Tableau, as well as other key metrics.

Calculate statistics on the fly

15:27

Learn how to visualize statistical significance in Tableau. Extract some computed numbers from Tableau for input into the following assessment.

Calculate statistics on the fly: visualization

14:47

Calculate statistics on the fly

1 question

Learn how to compute dynamically populated dimension metrics in Tableau. Extract some calculated numbers from Tableau for input into the following assessment.

Dynamic exploration of dimensions

09:18

Dynamic exploration of dimensions

1 question

Build all the waterfall metric component tables in Tableau using multiple worksheets. Understand the relationship between the waterfall metrics.

Calculate and visualize waterfall metrics

13:48

Pull together all the waterfall metric components into a dashboard. Extract some computed numbers from Tableau for input into the following assessment.

Calculate and visualize waterfall metrics: dashboard

07:41

Calculate and visualize waterfall metrics

1 question

Learn how to add filters to the Tableau dashboard. Slice the waterfall metrics simultaneously by statistical unit features. Extract some calculated numbers from the dashboard for input into the following assessment.

Add filters to the dashboard

09:17

Add filters to the dashboard

1 question

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Deep experiment analytics
6 Lectures
01:06:50

This lecture walks through the material covered in this section. The lecture describes how the lectures in this section build upon each other and the overall vision.

Preview
03:30

Learn how to build an analytic unit feature hypercube table for each feature. This lecture gives a whiteboard description of the calculations used in the screencast in the following lecture.

Creating analytic unit feature hypercubes

16:54

This lecture shows the screencast for the queries used for creating the analytic unit feature hypercubes. The lecture also shows how to extract some numbers for input into the following assessment.

Creating analytic unit feature hypercubes: screencast

08:29

Creating analytic unit feature hypercubes

1 question

This lecture shows how to combine multiple analytic unit feature hypercube tables into a multi-cube table. A description is given of how to get some numbers for input into the following assessment.

Assembling the multi-cube

12:32

Assembling the multi-cube

1 question

This lecture shows a whiteboard description of how to add analytics unit feature dimensions to the experiment dashboard. This will help the student to better follow the screencast shown in the following lecture.

Dynamic exploration of analytic unit features

10:45

This lecture has a screencast showing how to add analytic unit feature dimensions to the experiment dashboard. Some calculated numbers are extracted from the dashboard for input into the following assessment.

Dynamic exploration of analytic unit features: screencast

14:40

Dynamic exploration of analytic unit features

1 question

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Course wrap up
2 Lectures
11:55

This lecture gives a brief overview of the final exam contained in the following quiz.

Preview
01:34

The final exam is composed of 10 multiple choice questions. The first 6 questions will exercise your use of the experiment dashboard built in the course. The last 4 questions help you think about a few key concepts introduced in the course. Have fun!

Final exam

10 questions

This bonus lecture gives some practical advice and best practices for data science experimentation in consumer internet, assuming that the student intends to use the knowledge gained from this course in a real life application.

Bonus lecture: words of wisdom

10:21

About the Instructor

Chief Data Scientist at Udemy

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