Building a Data Science Capability: Inspirational Keynote at Data Leaders Summit Europe

I’ve just delivered the inspirational keynote at Data Leaders Summit Europe, 2018. Lots of great engagement and feedback. In particular, it seems people liked a clear definition of what data science actually is and the practical steps (miss-steps) I took in building a capability at Sainsbury’s.

Continue reading “Building a Data Science Capability: Inspirational Keynote at Data Leaders Summit Europe”

13 Steps to Better Data Science: A Joel Test of Data Science Maturity

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”

Data Science – A Definition And How To Get Started

Confusion, hype, failure to start. Data Science has huge potential to change an organisation. But many organisations become mired in the associated cultural, technological and people change. Data Science is delivered as an interesting report rather than a driver of change. Data Science identifies algorithms that are run in the safety of a lab but never make it into production.

This is my keynote talk from the Polish Data Science with Business Conference.

Confusion, hype, failure to start. Data Science has huge potential to change an organisation. But many organisations become mired in the associated cultural, technological and people change. Data Science is delivered as an interesting report rather than a driver of change. Data Science identifies algorithms that are run in the safety of a lab but never make it into production.

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 PandasMatplotlib 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)”

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.

Building Guerrilla Analytics Teams

I recently had the opportunity to present a webinar on ‘Building Guerrilla Analytics Teams’ as part of the BrightTalk ‘Business Intelligence and Analytics’ series. You can access the full recorded webinar and slides here and the slides are embedded below.

Some really interesting questions came up at the end of the session. I’ve listed them here and will pick them up in subsequent blog posts.

  • How do you build a business case to resource and set up a data science team?
  • What is the number one tip for someone putting together a completely new data science team?
  • What role is most important when setting up a data science team?
  • What are the typical challenges faced when setting up a Guerrilla Analytics team?

You can learn more about building a Guerrilla Analytics capability in my book Guerrilla Analytics: A Practical Approach to Working with Data which has chapters devoted to getting the right people in place, giving them the right technology and controlling everything with a minimal lightweight process.