Danny was scrolling through his Instagram feed when something really caught his eye - “80s Retro Styling and Pizza Is The Future!”
Danny was sold on the idea, but he knew that pizza alone was not going to help him get seed funding to expand his new Pizza Empire - so he had one more genius idea to combine with it - he was going to Uberize it - and so Pizza Runner was launched!
Danny started by recruiting “runners” to deliver fresh pizza from Pizza Runner Headquarters (otherwise known as Danny’s house) and also maxed out his credit card to pay freelance developers to build a mobile app to accept orders from customers.
Because Danny had a few years of experience as a data scientist - he was very aware that data collection was going to be critical for his business’ growth.He has prepared for us an entity relationship diagram of his database design but requires further assistance to clean his data and apply some basic calculations so he can better direct his runners and optimise Pizza Runner’s operations.
All datasets exist within the pizza_runner database schema - be sure to include this reference within your SQL scripts as you start exploring the data and answering the case study question
please find full case Study from here: https://8weeksqlchallenge.com/case-study-2/
Data Mart is Danny’s latest venture and after running international operations for his online supermarket that specialises in fresh produce - Danny is asking for your support to analyse his sales performance.
In June 2020 - large scale supply changes were made at Data Mart. All Data Mart products now use sustainable packaging methods in every single step from the farm all the way to the customer.
Danny needs your help to quantify the impact of this change on the sales performance for Data Mart and it’s separate business areas.
The key business question he wants you to help him answer are the following:
What was the quantifiable impact of the changes introduced in June 2020? Which platform, region, segment and customer types were the most impacted by this change? What can we do about future introduction of similar sustainability updates to the business to minimise impact on sales?
For this case study there is only a single table: data_mart.weekly_salesThe Entity Relationship Diagram is shown below with the data types made clear, please note that there is only this one table - hence why it looks a little bit lonely!
- Data Mart has international operations using a multi-region strategy
- Data Mart has both, a retail and online platform in the form of a Shopify store front to serve their customers
- Customer segment and customer_type data relates to personal age and demographics information that is shared with Data Mart
- transactions is the count of unique purchases made through Data Mart and sales is the actual dollar amount of purchases
- Each record in the dataset is related to a specific aggregated slice of the underlying sales data rolled up into a week_date value which represents the start of the sales week.
Analysis which is related to certain key events which can have a significant impact on sales or engagement metrics is always a part of the data analytics menu. Learning how to approach these types of problems is a super valuable lesson and hopefully these ideas can help you next time you’re faced with a tough problem like this in the workplace!
Please find the full Case study here: https://8weeksqlchallenge.com/case-study-5/