1) Product Sense via Machine Learning
Practical examples on to use regressions, ML, partial dependence plots, and rulefit to drive product development and come up with ideas for new product features
2) Product and Metrics – Case Studies
18 case studies on how to design actionable metrics, understand what drives them, and figure out how to improve them via new product features
In depth practical exercise on how to use machine learning to build a data product personalized at the user level. This is the framework used to optimize almost all data products
4) Unbalanced Classes
Almost all tech company data have unbalanced classes, i.e. fraud, ad clicks, conversation rate, email clicks, etc. These exercise explain how to deal with that
5) Missing data in tech
Most of missing data in tech are non-random, i.e. users choose to not provide certain information about themselves.These lessons explain how to deal with biased missing data. Include Uber and Airbnb case studies
6) Fraud – Case Studies
Fraud is one of the most common data science application. These case studies explain how to set up the problem from a ML standpoint as well the how to build a product around it
7) A/B Testing – Practice
A series of lessons covering all that’s needed to know about A/B testing. Includes statistical inference relevant theory as well as very practical tech problems (novelty effect, randomization, sample size, testing by market, etc.)
8) A/B testing – Case Studies
12 case studies describing how top tech companies design their A/B tests. it focuses on the most common issues tech companies face, like how to test in social networks or marketplaces, how to estimate long term effects, when A/B tests fail, etc.
9) Collection of tech company blog posts/case studies
This is a collection of company write-ups, tutorials, and blog posts. Includes Airbnb, FB, Linkedin, Google, Netflix and many more other companies describing how they design A/B tests and use DS to drive product development
10) Projects with solutions
Full product data science projects. Includes how to come up with ideas to improve conversion rate, how to predict fraud, and how to come up with ideas to increase retention
11) Final projects
12 final projects. They come from the “Collection of data science takehome challenges” book. They touch all the topics taught in the course
When to choose a logistic regression, how to interpret it, and how to use its output to come up with new test ideasSee the lesson
A/B tests: Sample Size
How to estimate for how many days you should run an A/B test, from both a statistical and business perspectiveSee all the lesson
Everything you can possibly need to know to work as a data scientist in product or analytics
$ 3250 full payment
☑ Lifetime access to course curriculum and all its future updates
☑ Unlimited 1:1 support from course author for 1 year
Any questions you have about the course material or anything related to product data science, you can send an email, skype chat, or share a Google doc with all the questions. You will get a prompt reply
☑ Personalized feedback
Send your solution for all the exercises in the course. You’ll get a detailed feedback on your work
Enroll in course
I am buying this with my employee training budget, do you provide a certificate or invoice that I can show to my employer?
Yeah, definitely. Can provide certificate, invoice, or really anything you need to show your employer to get reimbursed. Just ask for it.
To buy the course with my training budget, I need to show that the course content is relevant to my job duties. Can you help?
Yes, absolutely. Just email email@example.com with a brief overview of the most important data science projects you currently have at work.
If there is a match between the course content and your job duties, I will get back to you with clear examples of how your employer would benefit from you taking the course.
Yeah, all those challenges and product questions are here too. However, this course includes much more than that. Its main focus is on teaching product data science via a combination of theoretical lessons and practical examples. The challenges then come at the end to make sure things were learned properly.