Data Science teams have different levels of maturity in terms of their ways of working. In the worst case, every team member works as an individual. Results are poorly explained and impossible to reproduce. In the best case, teams reach full scientific reproducibility with simple conventions and little overhead. This leads to efficiency and confidence in results and minimal friction in productionising models. It is important to be able to measure a team’s maturity so that you can improve your ways of working and so you can attract and retain great talent. This series of questions is a Joel Test of Data Science Maturity. As with Joel’s original test for software development, all questions are a simple Yes/No and a score below 10 is cause for concern. Depressingly, many teams seem to struggle around a 3.
Continue reading “13 Steps to Better Data Science: A Joel Test of Data Science Maturity”Tag: Data Science
Data Science jargon buster – for Data Scientists
Data Scientists need to communicate without jargon so customers understand, believe and care about their recommendations. Here is a Data Science jargon buster to help with communicating data science project results.
Bamboozled. That’s your customers’ reaction to the Data Scientists in your organisation. Data Scientists need to communicate without jargon so customers understand, believe and care about their recommendations. Here is a Data Science jargon buster to help with communicating data science project results.
Continue reading “Data Science jargon buster – for Data Scientists”Reproducible Data Science: faster iterations, reviews and production
Data Science involves applying the scientific method to the discovery of opportunities and efficiencies in business data. An essential part of the scientific method is reproducibility. Reproducible Data Science is essential for scientific credibility but also improves your Data Science efficiency in 3 keys ways – faster iterations, reviews and pushes to production.
Data Science involves applying the scientific method to the discovery of opportunities and efficiencies in business data. An essential part of the scientific method is reproducibility. Reproducible Data Science is essential for scientific credibility but also improves your Data Science efficiency in 3 keys ways – faster iterations, reviews and pushes to production.
If you start to apply the 7 Principles of Guerrilla Analytics your teams will quickly achieve reproducibility and benefit from these efficiencies.
To Become A Data Scientist, Focus On Competencies before Skills
Too often, the path to becoming a Data Scientist focuses on technology skills in vogue rather than more permanent competencies. Competencies are a more general combination of skills, behaviours and knowledge. You can have great Powerpoint skills creating beautiful slides but still be a terrible communicator. It is competencies that are most important when you build a Data Science career that is robust to changing trends in skills like languages and technology platforms. This post describes the most important competencies for being successful in data science.
Too often, the path to becoming a Data Scientist focuses on technology skills in vogue rather than more permanent competencies. Competencies are a more general combination of skills, behaviours and knowledge. You can have great Powerpoint skills creating beautiful slides but still be a terrible communicator. It is competencies that are most important when you build a Data Science career that is robust to changing trends in skills like languages and technology platforms. This post describes the most important competencies for being successful in data science.
Continue reading “To Become A Data Scientist, Focus On Competencies before Skills”The Rigour of Science is Essential for Successful Data Science in Business
The rigour of Science is essential for successful Data Science in business. The scientific method helps drive successful data science projects in business. This post will show you how.
The rigour of Science is essential for successful Data Science in business. The scientific method helps drive successful data science projects in business. This post will show you how.
Continue reading “The Rigour of Science is Essential for Successful Data Science in Business”Data Science – A Definition And How To Get Started
This is my keynote talk from the Polish Data Science with Business Conference.
This is my keynote talk from the Polish Data Science with Business Conference.
Continue reading “Data Science – A Definition And How To Get Started”10 Data Science Capabilities Your Team Needs (and the Tools to Support Them)
People want some guidance in what can be a very overwhelming and fast moving field. Managers want to know what to buy and where to invest training. Junior data scientists, students, dev ops engineers and system administrators want to know what to learn. I will focus on the important capabilities for a Data Science team and the tools I have found useful for enabling those capabilities.
I’ve recently had several people ask me about tools for Data Science both online and at conference talks. This post lists some of the tools I use and the capabilities they provide.
When writing Guerrilla Analytics: A Practical Approach to Working with Data I deliberately avoided mention of tools. People can be dogmatic about tools and I thought this would be a distraction from the book’s core message around principles for doing effective Data Science in dynamic real-world projects.
That said, people do want some guidance in what can be a very overwhelming and fast moving field. Managers want to know what to buy and where to invest training. Junior data scientists, students, dev ops engineers and system administrators want to know what to learn. I will focus on the important capabilities for a Data Science team and the tools I have found useful for enabling those capabilities.
1. Version control with Git and git-flow
Capability: Typically you will go through many iterations of your code and the work products your code produces. It quickly becomes impossible to track changes and reproduce earlier work without some code version control tool. This is only exacerbated when your team size is >1.
Tool: Git is a great version control system and the effort to learn its command line interface is a very worthwhile investment.
Git is incredibly flexible. However this can lead to confusion and inconsistency in how it is applied. Git-flow is a set of scripts that automate much of what you will need to do in Git subject to a particular convention that happens to be very helpful for Data Science.
2. Wrangling and persisting data with PostgreSQL
Capability: Even if your data is small enough to fit in memory, reproducing work will involve running all those scripts into memory before you can pick up where you left off. Other team members have to do the same. This is painful and inefficient. You therefore need to persist your work (raw data, intermediate datasets and work products).
Tool: A database gives you a way to persist your workings and intermediate datasets as well as share with team members. Pick a database the is performant and flexible. I use PostgreSQL. It has an amazing set of features and this flexibility is what you want when doing Data Science.
3. Wrangling and visualizing data with Pandas, Matplotlib and Seaborn
Capability: Getting your head around your data and preparing it for a variety of algorithms is probably the most time-consuming and important part of the Data Science life cycle. Some preparations are easier done outside of many databases e.g. some natural language processing. Visualizing the data is really important here too.
Tool: Pick a programming language that has great data reshaping and visualization capabilities. If you work in Python, Pandas is a powerful set of data structures and algorithms for wrangling. Seaborn and Matplotlib are good places to start for visualization. And don’t waste time trying to get all these things to work together. Just use Continuum’s excellent distribution Anaconda.
Read: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
4. Documentation with Markdown
Capability: Data Science is useless without communication (to your customer and within your team). You could just write a report as a Word document. There’s nothing wrong with that and it’s a format your business customers will expect. However, it would be great to have a documentation that is easy to version control and can be kept close to your project code.
Tool: Markdown is a nice platform-neutral way to document your project. Because it’s plain text it’s easy to version control (see above). And if your report isn’t too complicated you can convert it to Word from Markdown. Win.
5. Fast file manipulation, cleaning, and summarising at the command line
Capability: You get hundreds of data files. You get huge files in strange formats with broken delimiters. You want to chop these up, patch them together, change their encodings, unravel XML etc etc. No, trying to open the file in a text editor or spreadsheet is not the answer.
Tool: This is best done at a powerful command line. Linux is worth learning.
Read: Data Science at the Command Line: Facing the Future with Time-Tested Tools
6. Story telling with Jupyter Notebooks
Capability: Data Science is difficult to communicate. It’s often a slightly meandering journey with dead ends, back-tracking, unexpected insights leading to new research avenues etc. When updating your customer, you need to walk them through some of this journey using narratives interleaved with graphics and tabular data. Code files won’t do. Duplicating into Powerpoint is a lot of extra work for a quick interim update.
Tool: Jupyter allows all of the above in presentation quality. The close interleaving of analysis and documentation helps other team members join a project. And it reduces duplication when you decide it’s time to stop coding and start updating your customer.
7. Build automation with Luigi
Capability: eventually, your understanding and your code start to consolidate. There are some core datasets. They go through some agreed preparatory steps. There are some reports and algorithm datasets that you want to lock down and reproduce several times during the project. Manually running all those code files is a pain.
Tool: build automation tools allow you to automate tasks such as executing code files, creating documentation, importing and exporting data etc etc. I’ve used command line scripts (see above) and software build tools like Ant for this automation. More sophisticated tools like Luigi are now reaching a level of maturity where you could consider them for your team too.
8. Workflow tracking with JIRA
Capability: what the hell is everybody doing? Where did that data come from? Where is the conversation with the system SME that led to that business rule? Where is the deliverable from 2 weeks ago and who sent it to which customer?
Tool: workflow tracking tools like JIRA help answer all the above questions. Look for a tool that is customizable as Data Science doesn’t need all the detail of a large scale software development project. Do make sure you track where your data is coming from and what deliverables are going out the door (see Guerrilla Analytics).
9. Packaging it all up with Vagrant
Capability: the diverse nature of Data Science activities leads to a correspondingly diverse set of tools as you’ve seen above. When you get things working, you would rather not break them and you would rather not force every team member to go through the same painful installations and configurations and risk inconsistency.
Tool: Vagrant and other ‘dev ops’ tools allow you to define your tech setups and their configuration in program code. What does that mean? It means that you can build your entire technology stack and configure it by running some code. It also means that the installation of all your tools and their configuration can be version controlled. As your technology stack evolves, update your code and issue a new release to your team. If you trash your technology or need to move to other servers, everything you need to reproduce your environment has been captured and you should be back up and running in minutes.
Read: Vagrant: Up and Running
10. Putting it all together – Operations with Guerrilla Analytics
I’ve covered a lot here. How do you put this all together without choking a team in conventions, rules, tools etc? How do you reduce Data Science chaos and continue to deliver iteratively and at pace? That’s where Guerrilla Analytics can help.
Have a read around this blog, check out the book and get in touch with any questions!
10 Data Science Capabilities (and supporting tools)
People want some guidance in what can be a very overwhelming and fast moving field. Managers want to know what to buy and where to invest training. Junior data scientists, students, dev ops engineers and system administrators want to know what to learn. I will focus on the important capabilities for a Data Science team and the tools I have found useful for enabling those capabilities.
Data Scientists and data science managers want some guidance on supporting tools choice for their data science capabilities. Managers want to know what to buy and where to invest training. Junior data scientists, students, dev ops engineers and system administrators want to know what to learn. I will focus on the important capabilities for a Data Science team and the tools I have found useful for enabling those capabilities.
Continue reading “10 Data Science Capabilities (and supporting tools)”Irish Language Data Science lecture at Engineers Ireland
I gave the following lecture to Engineers Ireland which is the professional body for Engineers. It’s about “Data Science and the benefits for engineering” and is entirely in Irish.
It was an interesting exercise to brush up on my Gaelic and also to see the wealth or resources that now exist for using Irish with modern technical vocabulary. If you are curious or are trying to get your Gaelic up to scratch then please get in touch!
I may be the first person to coin Data Science in Gaeilge!
I gave the following lecture to Engineers Ireland which is the Irish professional body for Engineers. The lecture is about “Data Science and the benefits for engineering” and is entirely in Irish.
It was an interesting exercise to brush up on my Gaelic and also to see the wealth of resources that now exist for using Irish with modern technical vocabulary. If you are curious or are trying to get your Gaelic up to scratch then please get in touch!
In terms of content, it covers what data and data science look like and how traditional engineering problems might benefit from the application of data science.
The full video is linked below.
And here are the slides 2016-04 Engineering Ireland_04_Gaeilge.
Data Science Patterns: Preparing Data for Agile Data Science
Are you a data scientist working on a project with constantly changing requirements, flawed changing data and other disruptions? Guerrilla Analytics can help.
The key to a high performing Guerrilla Analytics team is its ability to recognise common data preparation patterns and quickly implement them in flexible, defensive data sets.
After this webinar, you’ll be able to get your team off the ground fast and begin demonstrating value to your stakeholders.
You will learn about:
* Guerrilla Analytics: a brief introduction to what it is and why you need it for your agile data science ambitions
* Data Science Patterns: what they are and how they enable agile data science
* Case study: a walk through of some common patterns in use inreal projects
I recently gave a webinar on Data Science Patterns. The slides are here.
Data Science Patterns, as with Software Engineering Patterns, are ‘common solutions to recurring problems’. I was inspired to put this webinar together based on a few things.
- I build Data Science teams. Repeatedly, you find teams working inconsistently in terms of the data preparation approaches, structures and conventions they use. Patterns help resolve this problem. Without patterns, you end up with code maintenance challenges, difficulty in supporting junior team members and all round team inefficiency due to having a completely ad-hoc approach to data preparation.
- I read a recent paper ‘Tidy Data’ by Hadley Wickham in the Journal of Statistical Software http://vita.had.co.nz/papers/tidy-data.pdf. This paper gives an excellent clear description of what ‘tidy data’ is – the data format used by most Data Science algorithms and visualizations. While there isn’t anything new here if you have a computer science background, Wickham’s paper is an easy read and has some really clear worked examples.
- My book, Guerrilla Analytics (here for USA or here for UK), has an entire appendix on data manipulation patterns and I wanted to share some of that thinking with the Data Science community.
I hope you enjoy the webinar and find it useful. You can hear the recording here. Do get in touch with your thoughts and comments as I think Data Science patterns is a huge area of potential improvement in the maturity of Data Science as a field.
Guerrilla Analytics: Book, Speaking and Training 
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