Successful Data Science Projects

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 1 day on-site course held in Central London near to London Bridge Station in a small group of circa 10 people mixing scenario solving, new tools and processes and an effective project planning approach that Ian has developed over several years of client-based team coaching. See <A HREF="https://ianozsvald.com/2019/03/04/second-successfully-delivering-data-science-projects-just-over/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">this write-up</A> for a previous iteration of the course. 
"One of the highlights from Ian’s [previous version] Successfully Delivering Data Science Projects course 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 
"After attending the course I can identify and communicate to the project team and client the uncertainties of the project efficiently. I am using the techniques covered on the course to write project initiation documents and put in place the necessary processes to reduce uncertainty. The course was very engaging and I was very happy to learn from Ian's experience to ensure a successful delivery on all future projects." - Dani Papamaximou, Data Scientist at Arcadia
"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>Has had a difficult project delivery and wants more confidence in their process</LI>
<LI>Believes that their team could work more efficiently - if only they knew what to change</LI>
<LI>Wants to know what a good Definition of Done looks like</LI>
<LI>Needs ideas on estimating Costs and Benefits to help plan a project and to sell the benefits (and risks) to colleagues</LI>
<LI>Wants to accelerate their own career and leadership potential</LI>
</UL>
During the course we'll:
<UL>
<LI>Work through scenarios building up some Project Plans (built out of Ian's experience) that define what we know, what we need, risks and milestones so that you could confidently start work on these projects</LI>
<LI>Review best practice and the approaches used by successful teams which you can take back to your team</LI>
<LI>Review data science tools that'll assist with Exploratory Data Analysis and project derisking that will have an up-front impact on your new projects</LI>
<LI>Review the tools and practices used by everyone in the room (you're free to observe if you don't want to participate) to share the good experiences and identify problems that can fixed</LI>
<LI>Discuss approaches to post-deployment monitoring and support</LI>
<LI>Work through any questions you've brought along from your team</LI>
<LI>Use a Slack channel to share results which you can continue to use after the course</LI>
</UL>
"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 Project Plan that you've built-up during the course to take back to apply to your own challenges</LI>
<LI>Have a Code Review guide which you can use with your team to rapidly improve the quality of everyone's code to keep team-velocity high</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 also see the discussions from past versions including the post-course tips that were shared by previous students and you can continue to talk with all students through the Slack </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 or a hands-on data science team lead. You might be a junior to senior data scientist who wants more confidence in delivering projects or a CTO who wants to understand how the data science team could work better with the rest of the organisation. 
This course is not aimed at first-time Python users who want to learn about 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. 
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:
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 (e.g. due to illness) 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.

Successful Data Science Projects

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