Software Engineering for Data Scientists

Ian is a Chief Data Scientist and Coach, he co-organises the annual PyDataLondon conference with 700+ attendees and the associated 10,000+ member monthly meetup. He runs the established Mor Consulting Data Science consultancy as Chief Data Scientist in London, gives conference talks internationally often as keynote speaker and is the author of the bestselling O'Reilly book High Performance Python (with the second edition due in 2020).He has 16 years of experience as a senior technical leader, data scientist and coach. For fun he's walked by his high-energy Springer Spaniel, surfs the Cornish coast and drinks fine coffee.

Ian Ozsvald

This is a 2 day on-site course held in Central London near to London Bridge Station in a small group of circa 10 people. We'll focus on developing a standard process from R&D through to production backed by code reviews, documentation, refactoring, unit tests and a Notebook based git process. This course is based on hard-won experience by Ian from client engagements aimed at getting you more reliably over the line with successful working code. 
The photo above is from a sister course - this gives you an idea of the room layout and what students look like after a full day's learning with me.
"Ian's expertise and experience were essential to the development of our fledgling data science team. He played a fundamental role in defining our ways of working and mentoring junior members, as well as leading multiple projects with internal clients. I can't recommend working with him highly enough!" - Cyprian Rangel, Data Science Product Manager at QBE Insurance
This course is aimed at any Pythonic data scientist who: 
<UL>
<LI>Wants more confidence that their code runs correctly during deployment to avoid downtime and friction with business colleagues </LI>
<LI>Needs to learn more about testing and debugging</LI>
<LI>Wants ideas on routes to deployment and solutions to post-deployment changing data </LI>
<LI>Wants to collaborate with confidence with other technical team members to raise the team's long-term velocity </LI>
</UL>
<UL></UL>
During the course we'll:
<UL>
<LI>Develop defensive coding practices in our Notebook which double up as documentation</LI>
<LI>Refactor Notebook code into modules for reuse and increased trust</LI>
<LI>Add unit-tests to test our modules for trust and integration with a Continuous Integration pipeline</LI>
<LI>Review a git process that uses nbdime for collaboration on Jupyter Notebooks</LI>
<LI>Practice code reviews backed by a documented process you can take back to your team</LI>
<LI>Review a standard research-to-deployment process using cookiecutter </LI>
<LI>Discuss how to sell these new techniques to team members and senior staff to get critical buy-in to see change occur after the course</LI>
<LI>Look at how "traditional" software engineering and "data science engineering" differ to highlight process differences that your software engineering colleagues probably haven't seen</LI>
<LI>Write useful documentation in our code to improve future support</LI>
</UL>
"One of the highlights from Ian’s Successfully Delivering Data Science Projects course [note - this is the sister course to this one] was being introduced to the concept of a specialised project specification document. This provides a systematic framework to directly tackle numerous problems I have experienced when trying to move a project beyond an initial prototyping stage. I have now applied my own tailored specification document at my organisation and it immediately surfaced critical questions and issues that otherwise would not have been realised for months." - Thomas Brown, Data Scientist at aire.io 
"Ian coached our team when we needed some extra technical firepower, and provided that in spades. He slipped into a role providing technical leadership to a new bunch of people, and energised every project to which he contributed. He also straightened our path towards best practice, with a combination of good sense and business experience, for which generations of my team will be grateful" - Alice Jacques, Senior Data Scientist at Channel 4 
After the course you'll: 
<UL>
<LI>Have a working cookiecutter layout to demonstrate all of the processes to your team</LI>
<LI>Take home a practical guide for code reviews to significantly improve your team's code quality and overall velocity </LI>
<LI>Have gained answers to the questions you arrived with, so your personal blockers will be resolved</LI>
<LI>Have a plan for new tools and processes to introduce at work to make your team more efficient</LI>
<LI>Have access to our Slack channel to continue the conversation with class mates and to download any shared material, you'll be able to see conversations from previous courses and you'll be able to collaborate with past and current students </LI>
<LI>Receive a Certificate of Professional Development</LI>
</UL>
You have some prior experience with Python and data science tools like Jupyter Notebooks and Pandas. You might be a data scientist of any level or a hands-on data science team lead. You might be a junior software engineer in a data science team who needs to understand data science processes. 
This course is not aimed at first-time Python users who want to learn about software engineering and data science. This is aimed at practitioners who have worked on a data science project and who want to get better results, faster and with more confidence. This course is not aimed at non-technical project managers, it is very hands-on. 
You'll need your laptop with Anaconda installed with Python 3.7+. I'll check-in with you before the course to make sure you have everything you need installed. You can also install before the class by arriving earlier.
If you have questions please contact Ian@MorConsulting.com. <A HREF="http://ianozsvald.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Ian's blog</A> has a long list of <A HREF="http://ianozsvald.com/about-me/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">past public talks and videos</A>. Ian also has a <A HREF="https://ianozsvald.com/training/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">training email announce list</A> if you'd like advance notice of future events. Read <A HREF="https://www.linkedin.com/in/ianozsvald" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">many glowing testimonials</A> about Ian Ozsvald's work with past clients on LinkedIn. 
Location:
Prospero House is a dedicated training facility 5 minutes walk south of London Bridge station, opposite Borough tube station (on the Northern Line). It is easily reached from Kings Cross, Paddington and Victoria. The venue has tea and coffee and snacks all day, light continental breakfast for early arrivals, natural daylight and air conditioning.
Lunch is included in the price, options include vegetarian/meat/fish and other diets may be accommodated (please contact me if you have dietary requirements). I've visited the venue and eaten their lunch - this location is ideal for a great day's training.
Refund and change policy:
Please be sensible when booking any accommodation or travel and get refundable tickets. In the unlikely event that we have to cancel this event we are not responsible for any expenses you've incurred. Make sure your hotel or travel is refundable, just in case.
Eventbrite offers you a 100% refunded cancellation up to 7 days before the event (I'd happily talk to you about why you need to cancel to see if we can figure something out). I can move a ticket to another colleague, just get in touch. In the extremely unlikely event that we have to cancel the course then you will be offered either a 100% refund or the option of moving attendance to another workshop. Mor Consulting Ltd and Ian Ozsvald are not liable for any other costs incurred including travel and accommodation.

Prospero House

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Software Engineering for Data Scientists

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