Repository containing my portfolio of data analytics projects.
Fictitious bike-share company in Chicago called Cyclistic wants to understand how casual riders and annual members use Cyclistic bikes differently. Public data from City of Chicago’s (“City”) Divvy bicycle sharing service was used to generate insights and data visualisations for this project. A report was generated using R Markdown which includes the code used throughout the data cleaning, manipulation, analysis and visualisation steps. This case study was completed as part of the Google Data Analytics Certificate.
The key findings from this project were:
- Most casual riders ride on the weekends, whereas annual members ride most on the weekdays.
- Casual riders have a far greater average ride duration compared to annual members.
Bellabeat, a high-tech manufacturer of health-focused products for women are looking at fitness user data to see what trends can be found and how these findings can inform their marketing strategy. This case study was completed as part of the Google Data Analytics Certificate.
The key findings from this project were:
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The average number of steps per day taken by the users was 7638, which is between the 7000 to 8000 steps recommended by the CDC. Bellabeat can send users a notification if the daily number of steps has not been reached. CDC research findings show that more steps taken decreases the mortality rate. For more reading of the CDC research click this link.
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The average sedentary time from the data analysed was around 16.5 hours. Notifications could be set up on the device to remind users to decrease their sedentary time.
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Users had an average sleep time of less than 7 hours a day. A notification could be sent to users showing their sleep time from the previous day/week. Alarms can be set up by users 30 minutes or an hour before the users' desired sleep time.
I wanted to complete some exploratory data analysis on a data set from the US Food and Drug Administration. Used this exploratory data analysis (EDA) to utilise my data analysis skillset that I'm continuously working to improve on, combined with my experience in dealing with customer complaints in the food industry where some of the symptoms seen in the adverse event reports can be quite common.
The adverse food event reports were from 2004 - mid 2017 and are part of the CFSAN Adverse Event Reporting System (CAERS). CAERS is a database that contains information on adverse event and product complaint reports submitted to FDA for foods, dietary supplements, and cosmetics. The database is designed to support CFSAN's safety surveillance program.
The key findings from the EDA were:
- Product that led to most deaths were raw oysters
- Supplements were the most common products that led to adverse reactions
- More females reported adverse reactions compared to males
- Diarrhoea , Vomiting , Nausea , Abdominal Pain were the most common symptoms
Found a data set on Kaggle that has information such as player ratings and attributes that FIFA has given each player in FIFA 22. I like playing FIFA, so I'm including this here and also to show the SQL queries I ran to get the information I wanted from this data.
Click on the link above to see the visualisations I have made in Tableau.