Data Science tasks are graded manually, so the hiring manager or recruiter will need to assign a score after a candidate submits their test. Here are some things to look for when grading a data science task submission:

-   Are they able to familiarise themselves with a new dataset
    through meaningful exploration and visualisations of the data

-   Are they  to wrangle large amounts of data and restructure it to fit
    their modelling purposes. In this case, several datasets must be
    joined and transformed in order to get the variables on net bike
    stocks in stations

-   Choose a type of model from a large variety of available modelling
    techniques. A clear justification of the choice of a certain type of model
    over another shows a good understanding of the theoretical and practical

-  Show that their results are generalisable and thus useful for the
   business. In order to do this some form of test set must be set up
   (see below for more on this point) and the results should be clearly

After looking over the candidate's solution, the reviewer can assign a score by clicking on the task score's edit button. If you have any questions, email us at!

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