Bassam Wahib

For a sales manager at Maven Tech, which sells computer hardware to large businesses, he needs to track his team's quarterly performance. The CRM system is not visible outside the platform, so he needs an easy and fast way to track the data.

I created the dashboard to transform the CRM data into visuals that can help him understand the quarterly performance compared to the previous quarter. Also the percentage of won to lost opportunities per quarter as well as the performance of each sales agent and focusing by region and by manager.

So, we can get insights that help take corrective actions to improve sales in the defective area and create action plans for the least-achieving agents.

As a data analyst at Maven Environmental, I created a dynamic dashboard, transforming the CO2 emissions data from 1750 to 2022 to use to identify patterns, trends, and drivers of global CO2 emissions.

Here are some conclusions for insights and trends:

1. The UK was the biggest contributor till the end of the 19th century when the USA surpassed it.
2. By the 21st century, China became the biggest contributor.
3. CO2 emissions have a positive correlation with population.
4. Africa and South America have the lowest CO2 emissions per capita while Asia and North America have the highest.

As per request of a franchise owner at Maven Roasters with three locations in New York City, I created a dynamic dashboard, transforming the transaction data from Jan. to Jun. 2023 to improve business and discover opportunities.

Here are some conclusions for insights and trends:

1. Revenue increases directly proportional to the months.
2. Saturdays have the lowest demand while Fridays, Thursdays, and Mondays have the highest demand.
3. Morning hours from 7 to 10 am have the highest demand, while 8 pm has the lowest.
4. The top-selling categories are coffee, tea, and bakery.

5. The most ordered products in all branches are Brewed Chai tea, Gourmet brewed coffee, and Barista Espresso, but they differ in the order in each branch.

   These insights can help streamline operations by increasing the service team in the peak days and hours, planning the sourcing and recruiting for the next months according to the expected revenue increase, and saving adequate stocks for the highest ordered products.

Created an interactive Python script to show statistical analysis upon the user request after he choose the city, month and weekday required.

My objective was to answer some questions to discover related factors that affect attending the appointment.
The dataset is a collected data for doctor appointments and associated characteristics including if the patient show-up or no-show the appointment and some other factors.