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A solution to last week’s challenge can be found here.
In this challenge, you have a dataset with statistics from men’s singles tennis tournaments around the world. Your task is to determine who won the final with the biggest average rank difference in a tournament that had at least 20 final rounds.
To find the answer, use the Match Data dataset to complete the following tasks:
Find the average rank difference between the winner and loser in tournaments with data for 20 or more finals. Only consider tournaments where the Round value = The Final.
Identify the tournament that most likely caused an upset. This will be the tournament with the highest average rank difference.
Determine the name of the player who had the most wins in the tournament final identified in the prior task and how many times this player won this particular tournament.
Good luck!
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A solution to last week's challenge can be found here.
Use Designer Desktop or Designer Cloud, Trifacta Classic to solve this week's challenge.
Recently, your local arcade introduced a new virtual reality (VR) experience for people of all ages. They wanted to know how well the VR headsets were performing, so they conducted a survey last week to gather feedback from users. The arcade wants you to use the dataset from the survey to determine which brand of headsets had the highest rating. They will purchase more of those headsets in the future.
The arcade is currently working with three brands: HTC Vive, PlayStation VR, and Oculus Rift. They would like you to conduct an analysis for each brand.
The arcade owner also wants you to categorize users based on their age ranges. The age groups are as follows:
18–28 years old
29–39 years old
40–50 years old
51 years old and older
In the dataset, you have values for each user that include Duration (the length of time the user spent in the VR experience in minutes), and a Motion Sickness Rating, which is a reported value from 1-10, with higher values indicating a higher level of motion sickness. Ideally the users would feel very little motion sickness regardless of how long they are using the headsets. You have a Fun Score formula to apply to determine the correlation between the duration of time on the VR and the reported motion sickness.
Fun Score = Motion Sickness/60 * Duration in Minutes
Using the Fun Score formula, list the brands and age groups that have more than 20 people providing a fun score of <1 = LIFE CHANGING!
Fun Scores:
9 or above = Refund
8 or above = Really sick
7 or above = Sick
6 or above = Dizzy
5 or above = Feeling weird
4 or above = Pretty good
3 or above = Fun
2 or above = Great!
1 or above = AMAZING
<1 = LIFE CHANGING!
Source: https://www.kaggle.com/datasets/aakashjoshi123/virtual-reality-experiences
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A solution to last week's challenge can be found here.
This week's challenge marks the beginning of a trilogy of challenges inspired by the 2023 Inspire Grand Prix. These challenges delve into real-life scenarios that numerous companies encounter on a regular basis. The initial challenge focuses on the preparation and integration of data, and the second challenge revolves around spatial problem-solving. The third and final challenge entails tackling a predictive case.
If you are eager to experience the same exhilaration our racers feel in Las Vegas, take a quick, 2-minute glance at the instructions, start your timer, and record how long it takes you to determine the correct answers! Remember to share your time when you submit your workflow.
Let’s start now: 3, 2, 1, Go!
A company called ACE collects donated food products and delivers them to customers in different locations. They calculate the weight of each product by product type. Using the provided datasets:
Considering only trips where products were collected with a successful Closed Reason, determine the highest total weight collected by a customer on a single day (all product types combined).
What is the highest weight of product collected from a single customer on a single day? Note that some customers have multiple trips in a day.
What is that customer's ID and the collection date?
Next, calculate the total successfully collected weight for all customers on that date.
What is the total weight of products collected from all customers that day?
What percentage (for example 23%, not 0.23) of the products collected that day did the customer from Question 1 contribute? Round your answer to the nearest integer.
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This challenge comes to us from @Kenda . Thank you for your contribution, Kenda!
Use Designer Desktop or Designer Cloud, Trifacta Classic to solve this week's challenge.
Imagine you work as a data analyst for a company that develops video games. They want to create a strategy to increase profitability. The company has asked you to use industry sales data to determine whether the number of new video games released by a company impacts its profit for that year. You have a dataset (video_game_data.csv) that contains historical sales data of video games by multiple publishers around the world across several platforms. Your challenge is to do the following:
• Determine the years when the same video game publisher released the most game titles (based on the exact value in the Name column) AND had the most sales globally (a value you need to calculate).
Source: https://www.kaggle.com/datasets/gregorut/videogamesales
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A solution to last week's challenge can be found here.
Use Designer Desktop or Designer Cloud, Trifacta Classic to solve this week's challenge.
Are you ready to showcase your analytical skills and uncover valuable insights from the exciting world of international women’s football (or soccer as it is called in the United States)? This Weekly Challenge will put your data manipulation and analysis prowess to the test.
We will be using data gathered from over 4,000 women’s football match results from around the world.
Your challenge is to create a league table, for only the FIFA World Cup tournaments, that pulls together a number of statistics for each team: Games Played (GP), Wins, Draws, Losses, Goals For (GF), Goals Against (GA), Win %, and Average Goals scored per game. From this table, you will be able to answer three intriguing questions:
Which team has the best winning percentage at World Cup tournaments?
Which team scores the most goals on average?
Which team has the worst defense; that is, the most goals conceded?
Using the shootouts.csv dataset, we would also like you to answer this question on penalty shootouts:
Which team has the best penalty shootout performance?
Source: Kaggle Women's International Football Results
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