Hello Community Members,
A solution to last week’s challenge can be found here.
This challenge was submitted by Jacob Ward. Thank you, @SauronsBane, for this excellent contribution.
As the year wraps up, your role as a business analyst at a rapidly growing start-up takes a new turn. The VP of Finance has reached out, seeking help with the massive general ledger (GL) data that has grown beyond what the financial analysts can manage in Excel. Your task? Automate the workflow to create a consolidated Profit and Loss (P&L) statement for monthly reporting.
You have been provided with 2024’s GL data and the chart of accounts. Now, it is your job to organize the transaction data monthly according to the GL classes to streamline the financial reporting process.
Need a refresher? Review the following lessons in Academy to gear up:
Summarizing Data
Changing Data Layouts
Happy new year!
Data source: https://www.kaggle.com/datasets/irfansharif/generalledger (adjusted for challenge goals).
The Academy Team
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A solution to last week’s challenge can be found here.
This challenge comes to us from @Gurpartap0710 . Thank you for your contribution, Gurpartap!
Use Designer Desktop or Designer Cloud, Trifacta Classic to solve this week's challenge.
The Data Analytics team has been provided with a CSV file that contains monthly sales records for each employee throughout the year. The objective is to determine the average sales for each employee, considering ONLY the first 3 consecutive months beginning from the month they made their first sale of the year.
For example, if an employee did not make any sales in January and February but made some sales in March, the calculation would start from March. Therefore, the average would be computed based on the sales recorded in March, April, and May, even if there were no sales in April or May.
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We hope you enjoyed last week's challenge. The solution has been posted here. For the second challenge lets look at removing characters and splitting data into columns based on delimiters.
Many products will export textual data with delimiters such as quotes. This is done so that strings can contain delimiters or control characters within them. Having more than one type of delimiter can be hard for ETL programs to interpret. In the input text file, there are two different delimiters (double quotes, single quotes) and they surround different data types.
Use Alteryx to strip out the delimiters as superfluous and format the data as represented in the output.
You may notice that we have started classifying the exercises into beginner, Intermediate and advanced. This classification is used by Alteryx internally to sequence exercises as users advance.
Update 11/23/2015:
The solution has been uploaded.
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Hello Community members!
A solution to last week’s challenge can be found here.
Thank you, @Qiu , for creating this fun challenge. Your submission was so extensive, we had to divide it into two parts! We appreciate your contributions to our Weekly Challenges!
Welcome to the first of two challenges where you will dive deep into the world of LEGO®! Get ready to uncover some cool facts about LEGO sets, themes, and part counts. Use the provided datasets to solve this challenge. Let’s go!
The datasets contain the following information:
sets.csv: Data on LEGO sets, including a unique set number, name, release year, theme ID, part count, and image URL.
themes.csv: Data on LEGO themes, with each theme having a unique ID, a name, and a reference to its parent theme when applicable.
You have three tasks to complete in this challenge:
Determine which LEGO set contains the largest number of parts.
Determine which LEGO theme has the largest number of sets.
Calculate the average number of parts per set for each year. Use integer as the data type for this task.
Good luck!
The Academy Team
Download Start File
Data Sources:
https://rebrickable.com/downloads/
https://www.kaggle.com/code/andycapp29/best-bang-for-your-buck-lego-dataset-analysis/notebook
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Hello Community Members,
A solution to last week’s challenge can be found here.
This challenge was submitted by our valued Community contributor, Jifeng Qiu. @Qiu, we appreciate your continued creativity in submitting engaging challenges.
May the Force be with us once more!
You have access to a survey about Star Wars, with responses collected from fans before the release of the seventh episode in the franchise, Star Wars: The Force Awakens.
Before diving into the challenge questions, you need to complete a few steps to clean and prepare the data for analysis.
HINT: For each task, you will be required to work on a unique question, outlined as follows:
Which of the following Star Wars films have you seen? Please select all that apply.
Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.
Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.
However, the data is not straightforward. Each question may have multiple answers, and the fields related to the same question follow a pattern: the question itself, followed by unnamed fields that appear immediately after.
Once the data is prepared, you are ready to complete the following tasks:
Determine which Star Wars episode among the six films was the most viewed by survey respondents.
Create a graph to evaluate the correlation between how often films were viewed (from Task 1) and the rankings provided by respondents.
Identify the top two favorite characters (rated "Very Favorably") and the top two most disliked characters (rated "Very Unfavorably") based on unique respondents in the survey data.
Need a refresher? Review the following lessons in Academy to gear up:
Changing Data Layouts
Summarizing Data
Happy solving!
Data source: https://www.kaggle.com/code/samaxtech/cleaning-analyzing-star-wars-survey-data/input
The Academy Team
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