This is a 3 morning virtual course held on-line via Zoom for 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.

You'll learn to develop faster, increase the trust in your code, refactor faster to cleaner and more reusable code and as a result you'll deliver higher quality solutions with less effort.

The photo above is from another of my online courses - read the students' happy expressions to see what learning with me is like.

"Ian's course Software engineering for Data Scientist was really useful to me. I learned more about refactoring and testing which I implemented at work in my current project the week after the training. There are other good practices (including the use of libraries I didn't know) that I am willing to put in place in the future." - Sandrine Pataut (QBE)

"Ian's Software Engineering for Data Scientists course provides an excellent overview of best practices with focus on testing, debugging and general code maintenance. Ian has a wealth of experience and also makes sure to keep on top of the latest tools and libraries in the Data Science world. I would especially recommend the course to Data Science practitioners coming from an academic rather than software engineering background." - Mirka (LibertyGlobal)

This course is aimed at any Pythonic data scientist who:

Wants more confidence that their code runs correctly during deployment to avoid downtime and friction with business colleagues
Needs to learn more about testing, test driven development and debugging
Wants ideas on routes to deployment and solutions to post-deployment changing data
Wants to collaborate with confidence with other technical team members to raise the team's long-term velocity

During the course we'll:

Develop defensive coding practices in our Notebook which double up as documentation
Refactor Notebook code into modules for reuse and increased trust
Add unit-tests to test our modules for trust and potential integration with a Continuous Integration pipeline
Review a git process that uses nbdime for collaboration on Jupyter Notebooks
Practice code reviews backed by a documented process you can take back to your team
Review a standard research-to-deployment process using cookiecutter
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
Look at how "traditional" software engineering and "data science engineering" differ to highlight process differences that your software engineering colleagues probably haven't seen
Write useful documentation in our code to improve future support

After the course you'll:

Have a working cookiecutter layout to demonstrate all of the processes to your team
Have a cheatsheet with a summary of the main points from the course so you can easily put them into practice back in the office
Take home a practical guide for code reviews to significantly improve your team's code quality and overall velocity
Have gained answers to the questions you arrived with, so your personal blockers will be resolved
Have a plan for new tools and processes to introduce at work to make your team more efficient
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
Receive a Certificate of Professional Development

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 3.9+. I'll check-in with you before the course to make sure you have everything you need installed.

If you have questions please contact Ian@MorConsulting.com. Ian's blog has a long list of past public talks and videos. Ian also has a training email announce list if you'd like advance notice of future events. Read many glowing testimonials about Ian Ozsvald's work with past clients on LinkedIn.

Attendance:

We'll meet online via Zoom over 3 mornings with breaks every hour for refreshments. I'll provide some light optional homework for the afternoons which you can undertake to get more value out of the course.

Two weeks after the course you can dial-in to a free chat I'll setup where we can review what was covered and address any remaining questions you have once you've had a chance to use the new tools back in your office.

Refund and change policy:

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.

Coronavirus note - If the course leader (Ian) has to cancel due to illness then you'll be offered a refund or a subsequent date. If you get ill I'm happy to discuss delaying your attendance to a later event. Coronavirus will be very disruptive, let's try to make disruption less painful. I will be running more of these events so please don't stress about whether you'll be available.

Software Engineering for Data Scientists (3 morning virtual UK course)

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