Win a $500 Amazon Voucher from our sponsors Pomerol, Qlik, TimeXtender, Astrato, & NovyPro, a DATAcated Circle membership worth $497 and 2 data books from our sponsors Packt and ColorWise by Kate Strachnyi!
A winning entry will be chosen based on best visualization and successfully following the entry rules – winning a $500 Amazon voucher code courtesy of Pomerol Partners, Qlik, TimeXtender Astrato, DATAcated & NovyPro, a DATAcated circle membership worth $497 and 2 eBooks from packt and ColorWise by Kate Strachnyi!
The top 5 entries will also receive 2 data eBooks from our sponsors Packt Plus, all entries submitted using Astrato will be entered into a special mini-competition. And the winning entry will receive an exclusive Astrato goodie bag!
We also now have amazing certificates to be won!
How to submit your entry:
- Follow Pomerol Partners on LinkedIn (it’s OK if you already follow Pomerol Partners)
- Share a LinkedIn post on your profile that contains both a direct @ mention to @Pomerol Partners, @Qlik, @TimeXtender, @Astrato Analytics, @NovyPro, @DATAcated and the hashtag #dataDNA
- In your post, share an image of your visualization or dashboard (remember, it must be a single image)
- Tag, mention, and invite at least 1 connection to view your post or play along
How to Create an Astrato Entry:
Want to try your hand at Astrato? The December DataDNA Challenge is the perfect dataset for fun, fast, and creative exploration!
This month, you’ll find the data available in the Astrato DemoData Connection. Follow the steps below to get started:
To Connect to Sample Sales Data
- Log in to Astrato
- If this is your first time logging in, make sure that you import the demo workbooks by clicking on the demo workbook card from the workbook section to establish a connection to the demo data!
- Click “New Workbook” at the top right corner of your workspace
- On the next screen, select “New Data View”
- Select the Demodata Snowflake connection from the Data Source list. This will open the Data View Editor.
- Select the iPhone Reviews Data Table and begin to create your masterpiece!
iPhone Reviews Dataset
Welcome to the DataDNA iPhone Reviews Dataset Challenge!
Objective
Are customers generally satisfied or dissatisfied with Apple’s iPhone?
About
This 5,000+ reviews dataset for Apple iPhone from Amazon.com provides insights and comprehensive opinion data that can be used to understand current customer sentiment towards the product. With helpful_count as one of the columns, this dataset provides an opportunity to find out which reviews are most helpful for customers and highlights the key areas of improvement for other brands in a similar product range. Exceptional review ratings and detailed text reviews give readers an idea about why customers liked or disliked the product, providing valuable market feedback information such as what went wrong (or right). Alongside this, knowledge about where a review was made gives better context on whether comments should be taken lightly or with more pressing importance. An invaluable resource for industry stakeholders and researchers alike, use this dataset to gain a clearer picture of customer satisfaction surrounding Apple’s latest release – The iPhone!
Credit to the original authors.
Data Source
Data Dictionary
| Column Name | Description |
| product | The product being reviewed. (String) |
| helpful_count | The number of people who found the review helpful. (Integer) |
| total_comments | The total number of comments on the review. (Integer) |
| url | The URL of the review post. (String) |
| review_country | The country from which the review was posted. (String) |
| reviewed_at | The date and time of the review submission. (DateTime) |
| review_text | The text of the review. (String) |
| review_rating | The rating given to the product by the reviewer. (Integer) |
| product_company | The company that manufactured the product. (String) |
| profile_name | The name of the reviewer. (String) |
| review_title | The title of the review. (String) |
DataDNA Dataset Challenge March 2023
Select “Get Data” below to download the dataset
Please read the challenge terms and conditions before participating.
