Asana-s-early-career-data-science-take-home-assessment

Take-Home Prompt There are two data files as described below. An example of how to read the two files into your notebook can be seen in this template we’ve made to help structure your assignment. A user file ("takehome_users") with data on 12,000 users who signed up for the product in the last two years. This table includes:

1. name: the user's name
2. object_id: the user's id
3. email: email address
4. email_domain: domain of email address, e.g. gmail.com
5. creation_source: how they signed up for the product. This takes on one of 5 values:
6. PERSONAL_PROJECTS: invited to join another user's personal workspace
7. GUEST_INVITE: invited to an organization as a guest (limited permissions)
8. ORG_INVITE: invited to an organization (as a full member)
9. SIGNUP: signed up via asana.com
10. SIGNUP_GOOGLE_AUTH: signed up using Google
11. Authentication (using a Google email account for their login id)
12. creation_time: when they created their account
13. last_session_creation_time: unix timestamp of last login
14. opted_in_to_mailing_list: whether they have opted into receiving marketing emails
15. enabled_for_marketing_drip: whether they are on the regular marketing email drip
16. org_id: the organization (group of users) they belong to
17. invited_by_user_id: which user invited them to join (if applicable). A usage summary file ("takehome_user_engagement") that has a row for each day that a user logged into the product.

We define an "adopted user" as a user who has logged into the product on three separate days in at least one seven-day period. Because we believe that adopted users are more likely to be successful at using Asana in the long term than those that are not adopted, we want to know what things are likely indicators of future adoption. With this in mind, we'd like you to identify which factors predict user adoption. Applicants generally take on a modeling approach to answer this question, but you may choose whatever method you prefer. For your convenience, we’ve broken out the task into 4 different sections:

1. Calculating adoption rate

1. Include code and ensure that it is clear what the adoption rate you calculated is.

2. Methodology

1. Include code, write-up, and visualizations (if applicable)

3. What factors predict user adoption?

1. Include code, write-up, and visualizations (if applicable)

4. Additional commentary (optional)