• Team Introduction

• Project Description

• Objective

• Process

• Insight and Findings

• Recommendation and Conclusion

• Dashboard

Introduction

Team DataZen is soaking it all in. This is the week to relive this interesting journey and then make one final push for 100%. The team scored a brilliant 95% for its effort at the Monday review meeting. However, 95 is not 100. This left most team members perplexed, one would think the team was due for the holy grail.

Reliving, the team has only progressed throughout this journey. An almost perfect collaborative effort where everyone plays according to their strength is solely responsible for this. It was a herculean task blending – quickly – random group of individuals to make a winning team.

For this, team DataZen absolutely deserve the best team award for progressively staying in the game, no other team has competed like this LOL.

The final challenge was released on Monday as usual, and as in a final game of a tournament, there were nerve wracking about expectation. Most team members felt a bit of relief more than anything else after seeing the task. What data is better to analyse (asides food) than one on people and performance?

Here’s the challenge.

Objective

The main objective is to analyse the data set exploring the salary, demographics, hires and terminations and any other areas. We are expected to do this by gaining insights into factors that affect employee retention and performance and identify areas for improvement.

Process

The Data was giving to us by our Bootcamp Manager Damilare Oyetade AKA Drey. It was loaded into Power BI and cleaned using Power Query. We proceeded to create our calendar table and DAX measures to aid our insights.

Insights and Findings

This is the summary of our insights. We were able to know that the total number of Employees is 1,470, those still in the company’s employment is 1,233, those who resigned from the company were 180, 57 employees – both full time and contractor – were terminated or their contract ended.

Active employees (18 to 40 years old) are 768, while those near retirement are 465. There are 882 male employees, representing 60% of the workforce, while female employees are 588 representing 40% of the workforces across all departments.

There are three departments (Sales, Human Resources, and Research and Development). Sales department have 446 personnel, Human Resources department have 63, while Research and Development 961. Average age of employees is 37, average number of years before an employee is promoted 2.13 years, average years worked is 6.47 years, average training period is 2.80 years, and average years with current managers is 4.65 years.

Average salaries by roles show that managers – irrespective of their department – earn the highest ($17,181.68). While the lowest earners are the Sales Representatives with $2,626.

Since there is need to sell what the company is offering, Sales Executives travel the most (within and outside the work city). Research Directors, whose work are mostly in the laboratory, travel less.

Among the employees, 673 are married, 470 are still singles, while 327 of them are divorced.

1,177 people came in through the company’s general hire exercise while 293 of them were recruited by third party recruiters.

A clustered bar chart showing salary disparity among the different gender in their various roles, suggests that female personnel start to earn less than their male counterparts as they grow to managerial roles. Male staff, who are Human Resources Officers, earn less than their female colleagues, while Laboratory Technicians have a uniform pay.

By department, salary disparity shows men earn far less in Human Resources department.

528 male and 365 female employees say they are experiencing better work life balance. Only 96 male and 57 female employees say they have the best work life balance.

Just like work life balance, more male employees (289) than female employees (170) are having the highest satisfaction in their jobs.

180 employees, who are full time and contract personnel, left the company of their own volution while 57 were terminated of their appointments.

Also, on a scale of very ‘high’ to ‘low’ for job involvement, 91 persons ranked very high, 523 high, 216 medium, and 52 personnel put in the lowest work time.

Similarly, 224 employees had outstanding performance, 1,057 had excellent performance, 128 had good performance, and of course the underperformers are 61. In terms of education level, 170 are below college, college is 282, 572 are bachelor’s degree holders, 398 master’s degree, while only 48 have doctorate degree.

For Relationship satisfaction 432 employees ranked very high while 276 ranked low. For Environmental Satisfaction, 446 personnel (very high) find their work environment engaging while 284 don’t.

Staff of Research & Development put in more overtime compared to Human Resources that rarely put in overtime.

Only 1 person has spent 37 years while 170 have spent 1 year.

Recommendation

The data shows that the company have pay disparity for roles among genders in top level positions. The company needs to rework their salary structure to a uniform rate for all positions irrespective of gender.

Dashboard

TABLE OF CONTENT

• Team Introduction

• Objective

• Business Problem

• Process

• Insight and Findings

• Recommendation and Conclusion

• Dashboard

Introduction

After the Side Hustle Internship 7.0 program, 7 strangers were paired to form the Data-Analysis-Team-5 for the bootcamp phase of the program.

Our first week together was one of scraping, wrangling, analysing, visualizing, off course jumping inti conclusion – oops that came out – and reporting among strangers; they said. You might want to grab your popcorn. The team members are chill so far at least.

After cleaning, modelling, and visualization, we are so excited to show what we came up with.

Objective

The objective of week 1 project was to analyse data from one the world’s top retail sector fast food business, McDonald’s Menu.

To achieve this, we are to analyze the data based on its categories, items and factors like Sugar and Fiber.

Methodology

The data was downloaded from Kaggle website and was shared with the team for collaboration and insights. The data was loaded unto Microsoft’s Power BI for cleaning, modelling, and visualization.

The cleaning and modelling were done on Power BI’s Power Query and Model View, and the visualization was done with the Report View.

Through the processes, we had several meetings and tasks assigned to decide the best approach to adopt for the storytelling for the presented data, McDonald’s Menu.

Business Problem

The business problem was to understand the nutritional value of the menu and categorize them according to their nutrients.

The analysis seeks to understand the average nutrient of each category from the menu, the percentage daily intake of nutrients and other nutritional insights.

The Process

The CSV format of the McDonald’s Menu data was downloaded from Kaggle website (https://www.kaggle.com/datasets/mcdonalds/nutrition-facts). It was imported into Power BI via the ‘Get Data’, then the data was loaded into Power Query for transformation. The data was cleaned by duplicating the table into several tables, which were distinct.

The tables formed the Fact and Dimensions tables.

The clean data was loaded unto Power BI from Power Query.

Under the Model View, relationships were created to model the data.

The Data Analysis Expressions (DAX) function was used to create measures required to develop the visuals. After which, under the Report View, dashboards were created.

Finding & Insights

The following charts presents our findings

This card shows the total item on the menu to be analyzed

These charts show the average level of Calories, Dietary Fiber, and Sugar for the various categories of the menu items:

From our findings, Smoothies have the highest sugar content; for Salad, Dietary Fiber; and for Chicken & Fish, Calories.

This Donut Chart shows the top 3 cholesterol category with breakfast category having the highest total cholesterol of 59.83%.

Average Daily Nutrients Analysis is represented in the table below. It is showing the average percentage daily value for each category:

We categorized the nutrients into Macronutrients (Carbohydrates, Sugars, Fiber, Fat and Protein), and Micronutrients (Sodium, Calcium, Iron, Vitamin A, and Vitamin C) for each menu items with an option to filter based on menu categories:

This Donut Chart shows the top 3 calorie distribution among the categories. Breakfaskfastis the highest among the top three categories containing39.25% with smothies & shakes following closely behind with 21.15%:

The table below shows the menu items and their serving sizes. This is necessary to paint a picture of the quantity of each item on the menu.

Recommendation and Conclusion

This analysis provides information on the number of calories and nutrients in each category and item. To eliminate bias in our category analysis, we calculated the average.

Based on our analysis, the smoothies category has a higher sugar content compared to the other categories. Consequently, consumers with diabetes might be unfit to consume from this category, therefore new products from this category should be designed specifically for them.

Furthermore, the cholesterol content in the breakfast category is enormous. As a result, this should be reconsidered to minimize cholesterol levels.

The menu should be inclusive of all populations while also keeping people's health in consideration.

Dashboard