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City of Cape Town - Data Science Unit Code Challenge

Purpose

The purpose of this challenge is to evaluate the skills of prospective Data Scientists, Engineers, Analysts and Front End Developer for positions in the City of Cape Town's Data Science unit.

Intended audience

We will only evaluate responses to this challenge from people who we have requested to complete it. Of course, you are welcome to attempt it for your own enjoyment.

Way of working and expected structure of submission

Principles of reproducible analysis and code versioning are very important to our workflow. Structuring your work to aid in reproducibility and readability is important.

So, follow common conventions with respect to directory structure and names to make your work as easy to follow as possible.

What we're looking for

Expectation of Effort

We expect you to spend up to 48 calendar hours working on this assessment per position. If you are finding that you are spending significantly more time than this, then please contact whomever sent you the link to this assessment to let them know.

You should have received over 7 days warning that you would be undertaking this assessment. Please notify Delyno du Toit if this was not the case.

Things to focus on

Over and above the tasks specified below, there are particular aspects of each position that we would like you to pay attention to:

  • Data Scientist candidates - we're looking for both good, statistical insight into problems, as well as the ability to communicate complex topics. Please make special effort to highlight what you believe to be the crux of a particular problem, as well as how your work addresses it.
  • Data Engineer candidates - as the key enablers of our unit's work, we really want to see work done in a sustainable manner: writing for easy comprehension, testing, clean code, modularity all bring us joy.
  • Data Analyst candidates - we think our analysts have done a good job when they provide insights that inform actual decisions. Hence, we want evidence of both the ability to surface these insights from data, as well as the skill to convey those insights. Your audience is intelligent, but non-specialist.

Candidates where programming is required (Data Scientist; Engineers, Visualisation Engineer and Front End Developers)

Requirements and notes:

  • For Data Science and Data Engineering, our primary programming languages are python, R and SQL. We will accept code that is packaged in .py, .ipynb, .R and .Rmd files. Scripts in .sql may also be included where applicable.
  • Data Visualisation engineers and Front End Developers should have knowledge of either python or R, and relevant front-end programming languages (e.g. Javascript, HTML, CSS). We will accept code that is packaged in .py, .R and appropriate front-end programming language specific files, e.g. .js, .html etc. Furthermore, we greatly appreciate adherence to the principles and guidelines of Single Page Applications.
  • Bash or similar scripting language files are fine for glue. You may develop in any development environment you choose.
  • We expect to be able to clone your repo, immediately identify what script to execute from your README file, and execute it to completion with no human interaction. In order to ensure that our environment has the right libraries or packages, please follow standard python (PEP8) or R guidelines for structure in your code, i.e place import and library() commands at the top of your scripts.
  • If your repo does not clone and run, we will not attempt to fix it.
  • If your analysis makes use of any external data, the data must either be included in the repo, or be downloaded automatically during script execution.

Candidates where programming is not required (Data Analysts)

Note If you prefer, you may submit using the requirements described above.

You can use any tool to produce the output, e.g. Python, R, Excel, Power BI, Tableau, etc. The final deliverable needs to be a pdf report with your analysis.

How to submit

Candidates where programming is required (Data Scientist; Engineers, Visualisation Engineers and Front End Developers)

  1. Clone this repository and load it into your development environment.
  2. Work the challenge, committing regularly to document your progress. Try have structured, meaningful commits, where each one adds significant functionality in a coherent manner.
  3. Host your repository somewhere that is publicly accessible. If you're using GitHub, please use a fork of our original repository.
  4. Inform us via email that your challenge is complete, including a link to your repo. Be sure to make sure it is set to public.

Be sure to 'watch' this repo for changes - we may push bugfixes

NOTE: If you would like to improve the content of this repository, by fixing typos or perhaps enhancing the challenge, please do so by submitting a pull request.

Candidates where programming is not required (Data Analysts)

NB If you prefer, you may submit using the workflow described above.

  1. Download this repository using the Code -> Download ZIP option in the top right-hand corner.
  2. Add your work into this folder.
  3. Create a compressed archive file with all of your work in it.
  4. Send us an email, with your archived project attached. If it is larger than 10 MB, then share it via a cloud storage service such as DropBox, and include the link in your email.

Challenge

Follow the below steps, completing those indicated as relevant to the positions for which you are interviewing. If there are any steps that you can not complete after a reasonable amount of effort, rather move on to later steps, attempting everything relevant at least once.

For all roles, we expect the challenge response to include what you consider to be role-appropriate testing and validation. For example, a Data Scientist might want to include MAPE scores or confusion matrices. A Data Engineer may want to include logging and data quality validation tests, as well as unit and even integration tests. A Data Analyst might want to plot histograms of the data in question to ensure that outliers aren't overwhelming your analysis.

Your code should be well formatted according to generally accepted style guides and include whatever is necessary for a team-mate unfamiliar with it to maintain it.

0. Setup

Data

We have made the following datasets available (each filename is a link). These are all available in an AWS bucket cct-ds-code-challenge-input-data, in the af-south-1 region, with the object name being the filenames below):

  • sr.csv.gz contains 12 months of service request data, where each row is a service request. A service request is a request from one of the residents of the City of Cape Town to undertake significant work. This is an important source of information on service delivery, and our performance thereof. Note as indicated by the extension, this file is compressed.
  • sr_hex.csv.gz contains the same data as sr.csv as well as a column h3_level8_index, which contains the appropriate resolution level 8 H3 index for that request. If the request doesn't have a valid geolocation, the index value will be 0. Note as indicated by the extension, this file is compressed.
  • sr_hex_truncated.csv is a truncated version of sr_hex.csv, containing only 3 months of data.
  • city-hex-polygons-8.geojson contains the H3 spatial indexing system polygons and index values for the bounds of the City of Cape Town, at resolution level 8.
  • city-hex-polygons-8-10.geojson contains the H3 spatial indexing system polygons and index values for resolution levels 8, 9 and 10, for the City of Cape Town.

In some of the tasks below you will be creating datasets that are similar to these, feel free to use the provided files to validate your work.

Dummy AWS Credentials

We have made AWS credentials available in the following file, with the appropriate permissions set, here.

Note These creds don't have any special access, other than what is already set on these resources for anonymous access. These are more provided to make using the various AWS client libraries easier.

1. Data Extraction (if applying for a Data Engineering Position)

Use the AWS S3 SELECT command to read in the H3 resolution 8 data from city-hex-polygons-8-10.geojson. Use the city-hex-polygons-8.geojson file to validate your work.

Please log the time taken to perform the operations described, and within reason, try to optimise latency and computational resources used. Please also note the comments above about the nature of the code that we expect.

2. Initial Data Transformation (if applying for a Data Engineering, Visualisation Engineer, Front End Developer and/or Science Position)

Join the equivalent of the contents of the file city-hex-polygons-8.geojson to the service request dataset, such that each service request is assigned to a single H3 resolution level 8 hexagon. Use the sr_hex.csv.gz file to validate your work.

For any requests where the Latitude and Longitude fields are empty, set the index value to 0.

Include logging that lets the executor know how many of the records failed to join, and include a join error threshold above which the script will error out. Please motivate why you have selected the error threshold that you have. Please also log the time taken to perform the operations described, and within reason, try to optimise latency and computational resources used.

3. Descriptive Analytic Tasks (if applying for a Data Analyst Position)

Note: We are most interested in how you reason about the problem.

Please use the sr_hex_truncated.csv dataset to address the following.

Please provide the following:

  1. An answer to the question "In which 3 suburbs should the Urban Waste Management directorate concentrate their infrastructure improvement efforts?". Please motivate how you related the data provided to infrastructure issues.
  2. An answer to the questions:
    1. Focusing on the Urban Waste Management directorate - "What is the median & 80th percentile time to complete each service request across the City?" (each row represent a service request).
    2. Focusing on the Urban Waste Management directorate - "What is the median & 80th percentile time to complete each service request for the 3 suburbs identified in (1)?" (each row represent a service request).
    3. "Is there any significant differences in the median and 80th percentile completion times between the City as a whole and the 3 suburbs identified in(1)?". Please elaborate on the similarities or differences.
  3. Provide a visual mock of a dashboard for the purpose of monitoring progress in applying the insights developed in (1) & (2). It should focus the user on performance pain points. Add a note for each visual element, explaining how it helps fulfill this overall function. Please also provide a brief explanation as to how the data provided would be used to realise what is contained in your mock.
  4. Identify value-adding insights for the management of Urban Waste Management, from the dataset provided, in regard to waste collection within the City.

The final deliverable is a report (in PDF form) for the Executive Management team of the City. An Executive-level, non-specialist should be able to read the report and follow your analysis without guidance.

4. Predictive Analytic Tasks (if applying for a Data Science Position)

Using the sr_hex.csv dataset, please chose two of the following:

  1. Time series challenge: Predict the weekly number of expected service requests per hex that will be created each week, for the next 4 weeks.
  2. Introspection challenge:
  3. Reshape the data into number of requests created, per type, per H3 level 8 hex in the last 12 months.
  4. Choose a type, and then develop a model that predicts the number of requests of that type per hex.
  5. Use the model developed in (2) to predict the number in (1).
  6. Based upon the model, and any other analysis, determine the drivers of requests of that particular type(s).
  7. Classification challenge: Classify a hex as sparsely or densely populated, solely based on the service request data. Provide an explanation as to how you're using the data to perform this classification. Using your classifier, please highlight any unexpected or unusual classifications, and comment on why that might be the case.
  8. Anomaly Detection challenge: Reshape the data into the number of requests created per department, per day. Please identify any days in the first 6 months of 2020 where an anomalous number of requests were created for a particular department. Please describe how you would motivate to the director of that department why they should investigate that anomaly. Your argument should rely upon the contents of the dataset and/or your anomaly detection model. 2 Feel free to use any other data you can find in the public domain, except for task (3).

The final output of the execution of your code should be a self-contained html file or executed ipynb file that is your report.

A statistically minded layperson should be able to read this report and follow your analysis without guidance.

Please log the time taken to perform the operations described, and within reason, try to optimise latency and computation resources used. Please also note the comments above with respect to the nature of work that we expect from data scientists.

5. Further Data Transformations (if applying for a Data Engineering Position)

  1. Create a subsample of the data by selecting all of the requests in sr_hex.csv.gz which are within 1 minute of the centroid of the BELLVILLE SOUTH official suburb. You may determine the centroid of the suburb by the method of your choice, but if any external data is used, your code should programmatically download and perform the centroid calculation. Please clearly document your method.

  2. Augment your filtered subsample of sr_hex.csv.gz from (1) with the appropriate wind direction and speed data for 2020 from the Bellville South Air Quality Measurement site, from when the notification was created. All of the steps for downloading and preparing the wind data, as well as the join should be performed programmatically within your script.

  3. Write a script which anonymises your augmented subsample from (2), but preserves the following precisions (You may use H3 indice or lat/lon coordinates for your spatial data):

    • location accuracy to within approximately 500m
    • temporal accuracy to within 6 hours Please also remove any columns which you believe could lead to the resident who made the request being identified. We expect in the accompanying report that you will justify as to why this data is now anonymised. Please limit this commentary to less than 500 words. If your code is written in a code notebook such as Jupyter notebook or Rmarkdown, you can include this commentary in your notebook.

6. Data Visualisation Task (if applying for a Data Visualisation Engineering or Front End Developer Position)

Using the sr_hex.csv.gz dataset and open source front-end web technologies (html, css, javascript, etc), develop a data visualisation / dashboard that help to answer the question:

"In which suburbs should the Water and Sanitation directorate concentrate their infrastructure improvement efforts?".

The data visualisation / dashboard must include the following:

  1. A chart (plot) or charts (plots) that helps to answer the above question.
  2. A minimalist cartographic map with identifiable landmark features (e.g. major roads, railways, etc.) and some representation of the data.
  3. Make (1) and (2) interactive in some manner, so as to allow users to explore the data and uncover insights. The following example Map with "range" sliders demostrates an interactive map. However, you're not limited to this example – feel free to explore other interactive approaches.
  4. Data Storytelling: in a separate markdown document, titled data-driven-storytelling.md, provide a brief, step-by-step, point form description of how your visualisations (and information from the dataset) outline a data-driven story that answers the above question.
  5. Design Principles: In a separate markdown document, titled visualisation-design-choices.md, please provide a brief, point form explanation for why you have chosen certain colours (e.g. for legends), fonts, the layout or anything else that will help us understand your thinking in designing the data visualisation / dashboard to answer the question.
  6. Publish your work using an online service such as https://pages.github.com/ or any other means you are familiar with. Anyone with an Internet connection and a modern browser such as Google Chrome, Mozilla Firefox or Microsoft Edge, should be able to see the end product and interact with it. Please reference the published link to your visualisation tool in the README.md of your repository.

Please also note the comments above about the nature of the code that we expect.

Contact

You can contact gordon.inggs, muhammed.ockards and/or colinscott.anthony @ capetown.gov.za for any questions on the above.