The document describes an analytics project to investigate metric spikes by analyzing operational data from a company. The approach involves importing data into a MySQL database, writing queries to answer questions from different departments, executing the queries and fixing any errors. The tech stack used includes MySQL, Google Drive, and PowerPoint. Sample insights include calculating throughput metrics like jobs reviewed per hour and language usage percentages. The project also examines duplicate rows and user engagement metrics by week, device, and email.

1. OPERATION
ANALYTICS AND
INVESTIGATING
METRIC SPIKE

2. PROJECT DESCRIPTION
• The analysis of the complete end-to-end operations of a company
• By analyzing data, the company then finds the areas in which it must
improve upon
• Help other departments to derive insight from the data they collect.
• predict the overall growth or decline of a company’s fortune.
• Being a data Analyst I must be able to understand or make other teams
understand questions as asked • Answer the questions asked by different
departments.

3. APPROACH
1.Get the information from given description about data and understood the problem
2. Go through the row data and understand the variable and attribute as given
3. Used MySQL Workbench I import files in the new database Ops_spike and I started
writing my queries as questions asked to achieve the result.
4. Executed the Queries and if there are any errors in the code, I modified the code and
fixed the code without any errors.
5. Once done with all queries I cross-checked all the queries and save them in the file.
6. Finally, I attached my code to the file.

4. TECH-STACK USED
1. My SQL
2. Google Drive
3. Ms. PowerPoint

5. INSIGHTS & RESULT
1. Calculate the number of jobs reviewed per hour per day for November 2020?

6. 2. Let’s say the above metric is called throughput. Calculate 7 day rolling average of
throughput? For throughput, do you prefer daily metric or 7-day rolling and why?

7. 3. Calculate the percentage share of each language in the last 30 days?

8. 4. Let’s say you see some duplicate rows in the data. How will you display duplicates from
the table?

9. CASE STUDY 2 (INVESTIGATING METRIC
SPIKE)
1. Calculate the weekly user engagement?

10. 2. Calculate the weekly engagement per device?

11. 3. Calculate the email engagement metrics?

12. 4. Calculate the weekly retention of users sign-up cohort?

13. THANK
YOU