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Answers

Before looking at the tasks

  • My main Hypothesis is on actionable
  • My main queries are on answer.sql
  • Anything tagged as either .png or .gif is an image related to my answer
  • Anything tagged .py is a command to run a specific query to generate the graphs; daily_average_users.py and status_changes_by_card.py
  • And finally, here's the walkthrough of what other feature shiptivity can improve on. This is based on hypothesis one.

hypothesis 1 image



Task Overview | Installation Instructions | Link to Module 3 | Link to Y Combinator Program

Introduction

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Module 3 Task Overview

Analytics - Analyse the latest feature releases

Aim:Create insights and ideas backed by data

Please clone this repository to start the task. Edit file answer.sql with your SQL query to produce meaningful data

On 2018-06-02, we released the Kanban Board on Shiptivity app. It has a major impact on our stats and we want to iterate on it

Purpose

We want to generate a few key ideas to move us on one of our core metrics: daily active users.

Acceptance Criteria

  1. This task is complete when we have a graph created that maps out the daily average users before and after the feature change
  2. This task is complete when we have a graph of the number of status changes by card
  3. This task is complete when you upload the SQL queries that can generate this information
  4. This task is complete when we have three actionable ideas stated in the format below:
    1. Hypothesis
    2. Expected Impact
    3. What the feature is (a quick explanation of the feature)
  5. Sketches and flows of these features are optional but helpful (wireframe quality is enough)

Installation

You will need to have sqlite3 installed

If you do not have sqlite3 installed, follow this guide on how to install sqlite3.

On your terminal, try running sqlite3 ./shiptivity.db.

You can start running SQL command on the terminal.

Tips: try .schema to see a list of CREATE TABLE commands for the db.

If you want to go back to tthe original state of db, there is a dump called shiptivity.dump that you can run to restore the data to its unchanged state.

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  • Python 100.0%