Загрузка страницы

How to make a data check in Tableau: A quick data check is better than no data check

👉🏻 Download Our Free Data Science Career Guide: https://bit.ly/2POLaN8
👉🏻 Sign up for Our Complete Data Science Training with 57% OFF: https://bit.ly/3iKD0lv

In this lesson, we would like to make sure that the data we’ve loaded through a join is truly representative of the figures we have in the two source files.
Let’s open Sheet 1 and do a few checks.
First off, I would like to see how many sale transactions were registered in total. Let’s drag the “number of records” field into the workspace area. Tableau is really quick and tells us that there are 110,570 rows with transaction ID information. These are the actual sales of audiobooks that occurred throughout the entire period of analysis.
A quick look into the “sales” Excel file shows us that this number is precisely the one we should have.
Next, I’ll test for the number of ratings we have in the Reviews file. I’ll simply drop the ratings field into the workspace area and the result we have is 96,897, which is too high.
Why is that?
Well, we are summing, not counting. This is the actual sum of all ratings that have been left by students. We want to count the number of ratings instead. Here. That’s much better. People who bought our audiobooks left a total of 10,798 ratings.
One final check and we are good to go.
Let’s add the “Date of purchase” field to the columns of our work space. Moreover, I’ll increase the level of granularity of our data and will opt for a monthly breakdown. That’s something we can do fairly easily and is one of Tableau’s strongest features.
Here’s the monthly breakdown of reviews.
Wait.
There is something strange. According to Tableau we did not receive any reviews in December 2017. However, I do know for a fact that we did.
What happened?
Tableau gets confused pretty easily when we join the data and then use a dimension such as “purchase date” from the Sales file, and another field such as “rating” from the reviews file. For some reason, the date fields of the two tables we joined do not match up with each other correctly. Whenever you experience such issues, it is best to use data blending (an alternative to Tableau joins).
That’s precisely what we will do in our next lesson.

► Consider hitting the SUBSCRIBE button if you LIKE the content: https://www.youtube.com/c/365DataScience?sub_confirmation=1

► VISIT our website: https://bit.ly/365ds

🤝 Connect with us LinkedIn: https://www.linkedin.com/company/365datascience/

365 Data Science is an online educational career website that offers the incredible opportunity to find your way into the data science world no matter your previous knowledge and experience. We have prepared numerous courses that suit the needs of aspiring BI analysts, Data analysts and Data scientists.

We at 365 Data Science are committed educators who believe that curiosity should not be hindered by inability to access good learning resources. This is why we focus all our efforts on creating high-quality educational content which anyone can access online.

Check out our Data Science Career guides: https://www.youtube.com/playlist?list=PLaFfQroTgZnyQFq4nUfb-w2vEopN3ULMb

#datascience #datablending #tableau

Видео How to make a data check in Tableau: A quick data check is better than no data check канала 365 Data Science
Показать
Комментарии отсутствуют
Введите заголовок:

Введите адрес ссылки:

Введите адрес видео с YouTube:

Зарегистрируйтесь или войдите с
Информация о видео
7 июня 2018 г. 20:01:08
00:03:49
Яндекс.Метрика