Best Practices When Starting And Working On A Data Science Project

Several topical questions were recently asked on Data Science Central. This post addresses the question “What best practices do you recommend, when starting and working on enterprise analytics projects?” I have worked as a Data Scientist for 8 years now. This was after completing a PhD on “Design of Experiments for Tuning Optimisation Algorithms”. So I have a formal background in rigorous experiment design for Data Science and have also managed some pretty complex and fast paced projects in sectors including Financial Services, IT, Insurance, Government and Audit.

Best practice guidelines

Several interesting questions were asked recently on a Data Science social network. This post addresses the question

“What best practices do you recommend, when starting and working on enterprise analytics projects?”

I have worked as a Data Scientist for 8 years now. This was after completing a PhD on “Design of Experiments for Tuning Optimisation Algorithms”. So I have a formal background in rigorous experiment design for Data Science and have also managed some pretty complex and fast paced projects in sectors including Financial Services, IT, Insurance, Government and Audit.

This post summarises my thoughts on best practice that are heavily based on practical experience as described in my book “Guerrilla Analytics: A Practical Approach to Working with Data”. The book contains almost 100 best practice tips for doing Data Science in dynamic projects where reproducibillty, explainability and team efficiency are critical.

Here is a summary of the best practices for working on enterprise analytics projects.

  • Soft Skills Best Practice
    • Consult
      • Understand the business problem
      • Understand the stakeholders
      • Understand your STARS situation
    • Communicate
      • Explain what you are doing and why
      • Explain the caveats in interpreting what you are doing
      • Always focus on the business problem
      • Continuously validate the above
    • Budget and Plan
      • Clearly set out your approach, milestones and deliverables
      • Measure progress and adjust when going off track or moving in a new direction
  • Technical Skills Best Practice Using the 7 Guerrilla Analytics Principles
    • Operations
      1. Keep Everything (Principle 1: Space is cheap, confusion is expensive)
      2. Keep It Simple (Principle 2: Prefer simple, visual project structures and conventions)
      3. Automate (Principle 3: Prefer automation with program code )
      4. Maintain Data Provenance (Principle 4 Maintain a link between data on the file system, data in the analytics environment, and data in work products)
      5. Version Control Data and Code (Principle 5: Version control changes to data and analytics code)
      6. Consolidate (Principle 6: Consolidate team knowledge in version-controlled builds)
      7. Think like a developer (Principle 7: Prefer analytics code that runs from start to finish)
    • Testing
      • Test data with the 5 Cs of Guerrilla Analytics Data Quality
      • Test code. Take a risk based approach to small, medium and large tests to improve confidence in the correctness of data manipulations, data cleaning and the application of business rules.
      • Test models. Always reviews the standard tests that accompany a model or algorithm. Run models against new data to make sure they perform.

The rest of this post will look at these best practice guidelines in more detail.

Soft Skills Best Practice

Consult

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Whether your job title is ‘consultant’ or not, the fact is you are probably acting as as consultant to some degree. See Peter Block’s excellent book “Flawless Consulting: A Guide to Getting Your Expertise Used” on the Data Science Reading List for more information. Recognise that you are in a position where you need to influence stakeholders to use your insights and take action. That means you need to:

  • Understand the business problem. Ask questions, take notes, play back your understanding until you have the best understanding possible of what the real problem is, its drivers, its blockers and what a successful outcome looks like for all stakeholders. Too much data science fails because it produces the right answer to the wrong problem.
  • Understand the stakeholders. Who is asking you to solve this problem? Why? Who is sponsoring the project and what are their concerns, drivers, targets?
  • Understand your STARS situation. Are you in a situation that is a Start-up, Turnaround, Accelerated growth, Realignment, Sustaining success? Each of these requires a different approach from fast-action heroics at one end of the spectrum to carefully planned maintenance and improvement at the other. You can read more about these ideas in the excellent book “The First 90 Days: Critical Success Strategies for New Leaders at All Levels”.

Communicate

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For your work to be successful, you need to be able to communicate what it is you have done and why your audience should care. This applies regardless of the level you are operating at.

This may be disappointing for a budding Data Scientist but your most sophisticated and clever work will only be appreciated in the context of your ability to consult as above. A ‘decrease in Type 1 error’, a ‘better Gini’, a responsive beautiful visualization are only of value when cast in terms that address the business problem in ways your stakeholders care about and can understand. Your manager who trusts your abilities may not need to know the minutiae of your workings but rather that you have taken a sensible approach and that you are clear on its limitations and any inherent risks/caveats. When communicating,

  • be able to summarise your key insights on a page
  • keep the technical details to an appendix. Your objective is rarely to impress with technicalities. Instead it is to deliver insight that leads to action.
  • be able to visualize your insights with a story that engages your audience

Budget and Plan

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Data Science projects are notoriously difficult to budget and plan because they are typically of an exploratory nature. Many can run indefinitely when poorly managed teams exhaustively mine data sets. There are almost unlimited ways to cut data and present it. This does not let you off the hook however. Your stakeholders and time with business SMEs is limited (they have day jobs). Your own time is limited. When running your project,

  • set goals with timelines
  • measure and track progress and adjust when necessary
  • avoid the temptation to do something because it’s cool or fun before validating it with your stakeholders

Win-Vector gives a really excellent post on how to set expectations in Data Science projects.

Technical Best Practice with Guerrilla Analytics

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Of course, Data Science is a technical discipline. There are 7 best practice guidelines called the Guerrilla Analytics Principles that will help keep everything running smoothly despite the very dynamic situations you are faced with. These principles apply across the entire analytics lifecycle from data extraction through to reporting.

Operations

Keep Everything (Principle 1: Space is cheap, confusion is expensive)

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Having a record of everything makes it easier when Data Science work needs to explain what was done, how understanding evolved, and why there may be errors or caveats around interpretation of results. Best practice tips include:

  • Keep all the data your receive, even older broken versions.
  • Keep all modifications to the data.
  • Keep all communications about the data (meetings, notes, dictionaries).
  • Keep all work products you create, even when they are superseded or replaced because they were wrong. This avoids confusion and conflicting results in what is a highly iterative environment.

Keep It Simple (Principle 2: Prefer simple, visual project structures and conventions)

Simple project and team conventions are easier to remember and therefore easier to follow. The more your team can look at a project structure and understand the purpose of the structure then the less time they waste and the lower the risk of inconsistencies. Some examples of best practice include:

  • Have one place for all data the team receives.
  • Take a consistent approach to loading data into the analytics environment.
  • Have one place for all work products the team produces.
  • Keep supporting materials (emails, documentation etc) near the data they support.

Automate (Principle 3: Prefer automation with program code)

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It’s inevitable that your project will involve multiple code files, perhaps from multiple tools, that must be repeatedly run in the correct order by multiple team members. Some of my projects have had several hundred SQL files for preparing data. When you develop a machine learning or statistical model, it is typically an iterative process that involves deriving new variables, re-running algorithms and creating outputs and visualizations to test the model performance.

  • Write code that is modular so parts of an analysis can be re-run and tested
  • Write code that can be run from the command line to facilitate simple automation.
  • Use a script for automating your code or use something a little more sophisticated like a build tool.

Maintain Data Provenance (Principle 4 Maintain a link between data on the file system, data in the analytics environment, and data in work products)

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Data Provenance simply means knowing where your data came from, how it was changed, who changed it, what was delivered and to whom it was delivered. If you can maintain this link from team inputs through to team outputs then your data science project will be much easier to manage, risks of inconsistency and incorrectness will be mitigated and team efficiency will be promoted. Some tips that help maintain data provenance include:

  • Give all received data its own unique identifier
  • Give all work products issued by the team their own unique identifier
  • Keep simple logs to record data receipt and work product creation

Version Control Data and Code (Principle 5: Version control changes to data and analytics code)

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With so much going on in the project and with the typically iterative nature of Data Science, it becomes imperative to use version control. You will need to go back to previous versions of code, branch a copy of current work to test a theory and undo mistakes. At a minimum you should:

  • version control data received
  • version control program code used to produce analytics
  • version control work products that are issued by the team

With this in place, any work product can be identified as say ‘version 3 of the work product, using version 3 of my code which draws on version 2 of the data received’.

Consolidate (Principle 6: Consolidate team knowledge in version-controlled builds)

It is inevitable that data cleaning rules, business rules and lessons learned will emerge over the course of a project. If each team member is applying these individually then the team is not performing efficiently and there is a risk of inconsistency. Take even a simple ranking operation. Do we mean a dense rank (1223) or something else (1224, 1334 etc)?

  • Identify the latest true version of data received and publish that centrally to the team
  • Identify common data manipulations, data cleaning and business rules. Implement them centrally and publish them to the team

Think like a developer (Principle 7: Prefer analytics code that runs from start to finish)

Data Science is not software development although both use program code. Data Science typically involves a lot of profiling of data to understand its properties. Models and rules are trialled to see if they perform well on the data. Cleaning rules are continuously discovered as understanding of the data grows.

This inevitably leads to many ‘code snippets’ that are necessary to developing an understanding of the data but not required for the final work product. These code snippets usually break over the course of the project, clog up work product code and confuse team members and reviewers when it comes to reproducing a result.

Writing analytics code that executes end-to-end eliminates these types of bugs and ‘code noise’. Team efficiency is improved, reproducibility of results is guaranteed and risk of data loss is eliminated since deleted data sets can easily be restored with a quick code execution.

Testing

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So much of testing is overlooked in Data Science. Its importance lead me to devote 4 chapters to Data Science Testing in “Guerrilla Analytics: A Practical Approach to Working with Data”.

At a minimum, high performing teams are doing some amount of testing. The testing falls into three areas. But before discussing those, pay attention to the overarching testing best practices.

  • Take a risk-based approach. There is not time to test everything exhaustively. Make sure that the most critical aspects of the data science work are being tested first.
  • Test early and often. Do not be tempted to put off testing until later in your project. Many of the models you build and data transformations you code may have to change if flaws are discovered in the data or in code.
  • Automate testing. To facilitate testing often, it helps to have some form of automation of test scripts so that tests can be easily repeated and you immediately know when something has gone wrong.

Test Data with the 5 Cs of Data Quality

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The excellent Bad Data Handbook lists 4 data test categories which I extend to 5 for “Guerrilla Analytics: A Practical Approach to Working with Data”. The 5 Cs of Guerrilla Analytics data quality are:

  1. Completeness: Do you actually have all the data you expect to have?
  2. Correctness: Does the data actually reflect the business rules and domain knowledge you expect it to reflect?
  3. Consistency: Are refreshes of the data consistent and is the data consistent when it is viewed over some time period?
  4. Coherence: Does the data “fit together” in terms of its expected relationships?
  5. aCcountability: Can you trace the data to tell where the data came from, who delivered it, where it is stored in the DME, and other information useful for its traceability?

Test Code

Testing of analytics code typically covers the data preparation and manipulation code that leads up to a visualization or application of an algorithm. Testing is a well established field in software engineering but not so in Data Science. “How Google Tests Software” is a great introduction and identifies 3 types of tests. In the context of data science, these are

  • Small: tests that wrap around small units of code, usually units that contain some particularly complex data manipulation or cleaning rules. Some example include application of regular expressions, calculations of running totals, identification of duplicates.
  • Medium: tests that wrap around a ‘component’ of multiple units. This might include the joining up and end-to-end cleaning of a particular data segment such as ‘customer’ or ‘products’.
  • Large: tests that wrap around the entire end-to-end project. For example, does the output of the machine learning model still contain the expected number of customers or where customers accidentally dropped somewhere between raw data and algorithm output?

In addition, some best practice tips for facilitating testing include:

  • Write modular code, typically having one code file per data step. This makes it easier to perform small and medium tests and makes it easier for several team members to work simultaneously in the code base without blocking one another.
  • Use a common structure for test code. All test code should have a setup, test and tear-down structure. This makes it easier to automate and debug tests.

Test Models

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When testing statistical models and machine learning outputs, things understandably a little more domain specific. At a minimum, best practice involves:

  • Running model tests. For example, tests of regression include heteroscedasticity, normality, correlation, and leverage.
  • Using cross-validation. Build and test models on a variety of partitions of the data to help minimise overfitting
  • Testing model predictions. See how the model performs on data it has not encountered before.

Summary

There is a lot to be mindful of in terms of best practices when doing Data Science. The key insight from “Guerrilla Analytics: A Practical Approach to Working with Data” is that 7 Guerrilla Analytics Principles can mitigate the operational risks inherent in Data Science projects. You can read more about these risks, the Guerrilla Analytics Principles and see almost 100 examples of their practical application in the book “Guerrilla Analytics: A Practical Approach to Working with Data”.

Data Scientists Need a Better Operating Model

In many cases, where Data Scientists struggle on projects has nothing to do with the technical complexity of problems or any lack of Data Science skills – they have all of that from their study and training and are quite motivated people who are passionate about their field. In fact, what makes Data Science difficult for many is the complexity of operating in a Data Science project environment.

A Data Science Operating Model

Motivation

In my job, I interview many potential Data Scientists and Data Analysts. I have also managed people with a wide range of experience from interns to seasoned PhDs with degrees in fields including Computer Science, Chemistry, Physics, Mathematics, Engineering and the Humanities. Just last week I had several conversations with prospective Data Scientists who are early in their careers and wondering what projects they should try to get on, what technologies they should learn and what additional courses they should study.

In many cases, where Data Scientists struggle on projects has nothing to do with the technical complexity of problems or any lack of Data Science skills – they have all of that from their study and training and are quite motivated people who are passionate about their field.

In fact, what makes Data Science difficult for many is the complexity of operating in a Data Science project environment. Specifically, a Data Scientist has to operate in an environment that looks like the following.

  • Dynamics of data: data will change over the course of most projects. It will be refreshed, added to, replaced and repaired. Manual data sources are a common way of interfacing with other team members outside the Data Science team. Since much Data Science involves bringing together disparate data sources in novel ways, it is rare for all of this data to arrive at the same time and to schedule. So Data Scientists have to cope with trying to design and implement their work on top of a base of data that is always in flux.
  • Dynamics of requirements: Data Science is exploratory. You really don’t know what’s in the data until you have worked with it. Typically several algorithms and analyses have to be tried out. The insights from these activities often lead to the project taking a new direction and new analyses being framed for these new requirements.
  • Dynamics of people: it is rare to work in isolation. A Data Scientist will typically interact with IT, warehousing, developers, business SMEs, third party data providers and, of course, their team mates and their customer. This means that other people are providing inputs to their data, other people are writing code and creating data sets they depend on and other people are presenting results they may have contributed to. When other team members leave or take vacation, they may be expected to take over work.
  • Constraints on time and resources: despite the dynamics above, the Data Scientist will be expected to add value and deliver successfully in limited time and with limited resources. You don’t always get the ideal technology stack or one that you are familiar with. You don’t always get all the skill sets you need on a project. And you don’t always get all the data for a perfect analysis.

If a Data Scientist does not have methods for coping with these dynamics and constraints then they will struggle to perform. Ultimately, they will rarely see the more advanced analytics where they can really add value.

  • They become mired in forensics of their own work and their team’s work
  • Time is wasted investigation and explaining inconsistencies
  • Deliverables must be rewritten because the original cannot be reproduced or cannot be explained
  • A team descends into reactively producing analyses rather than leading the project from their data and their deliverables
  • Results are plain wrong because of the chaos that arises from project dynamics and constraints

A Data Science Operating Model with Guerrilla Analytics

Guerrilla Analytics and its 7 Principles provide a tried and tested operating model for Data Scientists. It has been used in many high pressure, dynamic and constrained project environments to deliver analyses that are reproducible, auditable and explainable.

This Guerrilla Analytics operating model breaks Data Science activities into the following components, highlighting the challenges faced in each component and offering guidelines on how to overcome these challenges.

  • Data Extraction: how data is extracted and transported by a team in a traceable manner
  • Data Receipt: how data should be received and logged by a team
  • Data Load: how to load multiple versions of data into an analytics environment without breaking data provenance
  • Coding: how data should be manipulated in ways that promote flexibility, testability, audit and agility. How to structure code and how to mix multiple tools and programming languages without being overwhelmed.
  • Work products and Reports: how to produce multiple versions of agile work products and project milestone reports so they can be tracked easily with a customer or fellow team members
  • Building consolidated analytics: how to identify and control consolidated understanding, business rules and data sets that emerge over the course of a project to promote efficiency and consistency and to avoid re-inventing the wheel
  • Testing: how to test analytics code and data sets in a fast paced environment
  • Workflows: simple workflows for peer review and quality control

Operating models may not produce beautiful visualizations or involve high end statistics and machine learning. However they do allow Data Scientists to hit the ground running. They provide Data Scientists with the tools they need to survive real world project environments. This is turn improves the Data Scientist’s coordination with team members, their efficiency, their credibility, and ultimately increases the opportunities to add value.

We expect methodology from traditional laboratory scientists. Let’s expect the same from Data Scientists.

The Guerrilla Analytics Principles

I designed the principles to help avoid the chaos introduced by the dynamics, complexity and constraints of data projects. You will find the principles helpful if you work in Data Science, Data Mining, Statistical Analysis, Machine Learning or any field that uses these techniques.

The Guerrilla Analytics Principles have been applied successfully to many high profile and high pressure projects in domains including Financial Services, Identity and Access Management, Audit, Fraud, Customer Analytics and Forensics.

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There is now a page on guerrilla-analytics.net giving an overview of the 7 Guerrilla Analytics Principles.

I designed the principles to help avoid the chaos introduced by the dynamics, complexity and constraints of data projects. You will find the principles helpful if you work in Data Science, Data Mining, Statistical Analysis, Machine Learning or any field that uses these techniques.

The Guerrilla Analytics Principles have been applied successfully to many high profile and high pressure projects in domains including Financial Services, Identity and Access Management, Audit, Fraud, Customer Analytics and Forensics.

You can read more about the Guerrilla Analytics Principles in my book Guerrilla Analytics: A Practical Approach to Working with Data. Here you will find almost 100 practice tips from across the Data Science life cycle showing you how to implement these principles in real-world situations.

Do you have your own data science experiences and principles? Let me know by getting in touch!

Guerrilla Analytics: Tactics for Coping with Data Science Reality

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Here are the slides from a talk I gave today to the Information Technology Department at the National University of Ireland, Galway. Thanks to Michael Madden for the opportunity to speak.

The talk was about how Guerrilla Analytics principles and practice tips help you do Data Science in circumstances that are very dynamic, constrained and yet required traceability of what you do.

There were plenty of questions afterwards which is always encouraging. I’ll try to address these questions in subsequent blog posts so please do follow me @enda_ridge for all the latest posts.

Here are some of the questions from today.

  • what are the key skills to focus on if you want to work in data analytics / data science?
  • is programming ability a pre-requisite for doing data science? This question came up before at Newcastle University.
  • do the guerrilla analytics principles map to research projects?
  • do the guerrilla analytics principles map to ‘big data’ projects?

Since NUI Galway is a bi-lingual university, you can find my broken Gaelic version below!

As Gaeilge

Seo h-iad na sleamhnáin ó léacht a bhí agam inniú sa Roinn Teicneolaíocht Fáisnéise in Ollscoil na h-Éireann, Gaillimh. Buíochas le Michael Madden as an deis labhairt.

Bhain an léacht le cén chaoi is féidir leis na  prionsabail agus noda Guerrilla Analytics cabhair leat agus tú ag déanamh Data Science i ndálaí atá dinimic, srianta ach fós tá sé riachtanach go bhfuil inrianaitheacht ann.

Bhí mórán ceisteanna tar éis an léacht agus is maith an rud é. Freagróidh mé iad i mblag eile agus bígí cinnte mé a leanacht ag @enda_ridge don scéal is déanaí.

Seo h-iad roinnt de na ceisteanna.

  • céard iad na scilleanna is tábhachtaí agus tú ag iarraidh obair mar data scientist?
  • an gá duit bheith in ann ríomhchlárú le h-aghaidh obair mar data scientist?
  • an bhfuil baint ann idir na prionsabail agus tionscadail taighde?
  • an bhfuil baint ann idir na prionsabail agus ‘Big Data’?