Project Botticelli

New Sample ML Code & Large Data

New Sample ML Code, Larger Data, and Happy Holidays!

While delivering my Practical Data Science course over the last year-and-a-half, I have been updating and improving (I hope) some of the key demos that I use for teaching. As I have been often asked to share it with the attendees—and because I have promised it to many of you earlier in the year—I decided to make it available more broadly. In a way, this is our ML-themed end-of-year present, I suppose. It is not easy to get your hands on larger data sets that are easy to analyse, so I hope you will find it useful.

You can download four zip files for: performing classification diagnostics and plotting classifier performance using R, sample DMX that shows how to correctly query an association rules model to make cross-sell predictions with and without demographic (user-level) data, and an example in SQL Server R Services that shows the four different ways how to analyse a 10 million row data set, containing mortgage default risk information, using logistic regression. For your convenience, I have also included the 10 million rows data set as a SQL Server 2016 .BAK database backup file. All of this is free, but only available to registered members, including you, as a reader of this newsletter. Get these files from here:

  • Code and Data Samples (R, R Services, SSAS) (free, registration required)
  • and if you are looking for the HappyCars machine learning data set, it is also available from here (Full Access Members only)
  • If you are only looking for my R classifier performance code, you can also get it from my GitHub, which is the best place to go if you would like to modify it and share your edits with others.

Plans for 2017

I am still finalising the dates for my 2017 edition of the Practical Data Science course. It will be longer than in 2016, 5-days, and I will bring it around the world to:

  • 13–17 March 2017: Copenhagen
  • 24–28 April 2017: Oslo
  • 8–12 May 2017: Chicago
  • October TBD 2017: London
  • November TBD 2017: Dublin, Oslo, Stockholm

If you would like to find out more about the courses, have a look here, especially check out the detailed syllabus. This course has now been delivered 18 times, and I have already trained over 300 data scientists just in the last 18 months—many of them have started excellent, exciting, and profitable projects. Join us, this is the very best time to be a data scientist, data miner, or advanced analyst: same skills, different names.

Online Courses

Our online training will be refreshed in the first half of 2017. I have reserved the first two months of the year to work on it, and I hope to have more news soon. In the meantime, the front end of the web site has been updated to a fully responsive design. If you have any feedback, let me know.

Year-end 20% off

We reserve our best promotion, 20% off all memberships, for these last few days of December. It is once-a-year only, and I will not offer it again until the end of 2017. If you are thinking of renewing, or joining us as a Full Access Member, please take advantage of it before the year ends, as the code will expire on 1 January 2017. It is valid on all memberships, including group memberships. Use code

20BYEBYE2016

at checkout. If you have an existing membership, this will extend it by a full year. So, if you have a training budget that needs spending, now is the time... And thanks!

I wish you much success in data science and analytics. Thank you, very much, for following my updates. I appreciate your support and I hope to be of use to you in 2017. Happy holidays, I hope the next year is better for all of us on this planet!

Rafal

PS. You are receiving my newsletter because you have requested to be notified about new content when you registered on our site. Please share your feedback with me, pass it on, but if you are no longer interested in advanced analytics, BI, or data science, use the link below to unsubscribe. I respect your trust.

Rafal Lukawiecki, Data Scientist and Director, Project Botticelli Ltd

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