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A brief History of (my) Time… as a guinea pig at Brookes - and how it got me a job

A brief History of (my) Time… as a guinea pig at Brookes - and how it got me a job. Julia Clark. Courtesy of Pinterest. What I will do. My rationale for doing the course Recent highlights How it got me a job What I do / will be doing. Why this course?.

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A brief History of (my) Time… as a guinea pig at Brookes - and how it got me a job

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  1. A brief History of (my) Time…as a guinea pig at Brookes -and how it got me a job Julia Clark

  2. Courtesy of Pinterest

  3. What I will do • My rationale for doing the course • Recent highlights • How it got me a job • What I do / will be doing

  4. Why this course? • I wanted to do a masters in Data Science (or similar) • Fairly local to where I live • Flexible – timescale • Flexible – location (can do remotely) • The content – very important • I wanted a route to becoming a Data Scientist

  5. Drew Conway

  6. Highlights of this year • From semesters 1 and 2 of 2018/19 • Semester 2 still ongoing

  7. Introduction to Machine Learning (semester 1)Advanced Machine Learning (semester 2) •  - I love machine learning •  - explained using maths • Reinforces, extends, and underpins • Pre-processing and dimensionality reduction • Supervised learning – classification • Also unsupervised learning / semi-supervised • Coursework - freedom to choose methods – or write your own algorithm

  8. Example: A Google Data Centre P08822: Lecture 1

  9. Introduction to Distributed Systems (semester 1) • “A collection of independent computers that appear to the users of the system as a single computer” • Transparency, reliability, scalability… • Hadoop, NoSQL • Map-Reduce • Service-oriented computing • JavaScript + possibly other languages • Mixture of group and solo working

  10. Time Series Analysis • Hooshang – always nice logical structure, good notes • Comfort zone • Trend • Seasonality • Random Walk

  11. How it got me a job • …I don’t think I’d have got the job without it • The fact that I was doing the course helped • Showing commitment to the subject • Being exposed to a lot of the essential ideas and technologies • But most of all…

  12. Regression, regression, regression • Regression Modelling (year 1, semester 1) • Advanced Statistical Modelling (year 1, semester 2) • The bedrock of everything else • Technical test at interview • Actual questions related to the subject in the interview

  13. I am now a Data Scientist

  14. What have I been doing • Can’t talk about the data • … and I only started in February • Working mainly in R  • SQL querying with Impala • Lots of data munging and exploring • I don’t have to use Excel - hooray

  15. Next few weeks • Meeting SMEs • Regression (probably logistic) • Machine learning • Topic modelling • Network graphs – possibly DAGs

  16. Finally • It has been hard work alongside working, running a house etc • But I have enjoyed the experience so far… • … there is the small matter of the dissertation • If you’re thinking about doing the course, I would say go for it.

  17. Any questions?

  18. Appendix • Anscombe’s quartet • some cartoons I didn’t use • Modules covered / to cover

  19. Visualization of the Datasets

  20. All cartoons from xkcd

  21. Modules completed 2017/18 • Data Science Foundations • Statistical Programming • Regression Modelling • Statistics in Government • Survey Fundamentals • Advanced Statistical Modelling

  22. Modules 2018/19 • Introduction to Machine Learning • Distributed Systems • Advanced Machine Learning • Time Series Analysis • Data Visualisation

  23. Still to do • Data Mining – I hope • Dissertation – worth 1/3 of the total marks

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