I got two more courses from the Data Science Specialisation ….
out of the way;Practical Machine Learning and Developing Data Products.
Practical Machine Learning: covered the basic components of building and applying prediction functions with an emphasis on practical applications.
It also covered concepts such as training and tests sets, over fitting, and error rates.
It introduced a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests.
The content for this course was a lot.
I will publish my notes to rpubs and embed it in a separate story.
Developing Data Products: This was the very fun bit about putting all of your insightful findings and smart model implementations together in a pretty format that anyone can understand.
For my final project, I made a little app that analyses data from kaggle’s 2018 survey to identify industry trends.
The app is still in development but you can see the most current version hereI will do a story on each of the courses soon .
I am now concentrating on completing the capstone for the specialization(a very interesting project on text prediction – watch out for a story on this too!).
I do have plans to be at a few data science meetups in London soon.
I will be sure to post about those and if you do attend any feel free to say hello.
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