Data Analysis Challenge


You are working as an analyst for one of the Pokemon Training Teams to help them hire better trainers, more quickly.

Trainers fall into one of 8 classes (Curmudgeon, Doctor, Dragon Tamer, Engineer, Nurse, Pokemon Ranger, Scientist and Skier). In each region of Pokemon world, and for each Trainer Class, there’s a distinct team that’s hiring (regions are Kanto,Johto,Hoenn,Sinnoh,Unova, Kalos, Alola and the Sevii Islands). A team can be uniquely identified by trainer class and region.


The team you are working for is Engineer Johto. Given data from job applications for Pokemon Trainers, please write a short email to your internal hiring team with your findings of the dataset including:

1. Current state of the data (e.g. Are there any missing data from the dataset? What assumptions did you make? Are there any other data issues?)

2. How competitive is your team compared to other teams?(Use your best judgement to decide which teams to be included in the comparison and include visualization if needed.)

3. Construct and visualize two KPIs based on the data, these should be metrics that you think would be valuable to track over time and offer insight into your applicant pool.

4. Explain why those 2 KPIs are important and how they can help your hiring team to make better decisions.

5. Are there any other data columns that could help you to get more insights but not in the dataset and how would you like to use them?

Please aim to spend no more than 4 hours on the entire exercise. Explain your thought process and any choices you make, such as how to deal with missing values and which columns to use, etc. Make sure to touch on all of the above points in your email to the HR team and write it in a way that is easy for a non-technical audience to understand. If you think action items are appropriate, please include them in your email.


A zip folder containing:

1. Reproducible Code/Workbook: A Jupyter Notebook, Tableau Workbook, Markdown file, Excel worksheet, or Google Sheet that you used to do your analysis. While we prefer candidates to use either R or Python as the tool for data cleaning and transformation, use the tool of your choice.

2. Readme: Together with your analysis, please include a short readme file to explain your analysis and set-up.

3. Email: An email to the HR Team in a separate pdf, Word, or txt document. If you would include visuals in your email, please note what visuals you would include (and where they would be) and include them as images in the zip folder.


The applications of aspiring Pokemon Trainers can be found in the file: [login to view URL]

Column Descriptions:

Hired: 0 or 1, where 1 means "hired"

PokemonWorldRegion, PokemonTrainerClass: Columns indicating team are a combination of region in Pokemon world and Trainer class

Furthermore, there are many more columns, of which you should decide whether you want to use them or not. Some have to do with a trainer's previous education or training experience, or which Pokemon they have won badges with (for instance GymBadge1Pokemon). Other columns may contain details of the position for which they're applying (for instance PositionForTrainingPokemon which states which Pokemon they will be training), or details about the application itself (for instance ApplicationType or ApplyDate).

You don’t need to know anything about Pokemon to do this exercise, but for your information, there are three types of Pokemon-specific names. These are present in the following columns:

Region names appear in columns: PokemonWorldRegion

Trainer classes appear in columns: PokemonTrainerClass

Pokemon names appear in columns: CurrentPokemonTraining, PositionForTrainingPokemon, GymBadge1Pokemon,...,GymBadge4Pokemon.

A list of Pokemon names can be found in the file [login to view URL]

Taidot: Data Analytics, Data Analysis, Datatiede, Data Management, Data Governance

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