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Demystifying Data Science with an introduction to Machine Learning
The document provides an introduction to the field of data science, including definitions of data science and machine learning. It discusses the growing demand for data science skills and jobs. It also summarizes several key concepts in data science including the data science pipeline, common machine learning algorithms and techniques, examples of machine learning applications, and how to get started in data science through online courses and open-source tools.
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Demystifying Data Science with an introduction to Machine Learning
- 1. Demys&fying Data Science with and Intro to Machine Learning
- 2. Data science is everywhere
- 3. Sexiest job in 21st century* McKinsey Global Ins&tute report es&mates that by 2018, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analy&cal skills as well as 1.5 million managers and analysts with the know-‐how to use the analysis of big data to make effec&ve decisions” Source: Harvard business Review Oct’ 2012
- 4. So what is Data Science?
- 5. Source: Hilary Mason ex-‐Chief data science bit.ly
- 6. Who are these unicorns?
- 7. Bit about me @brightsparc
- 8. I thought it was all about stats?
- 9. It’s a broader skillset Source: h[p://blogs.wsj.com/cio/2014/02/14/it-‐takes-‐teams-‐to-‐solve-‐the-‐data-‐scien&st-‐shortage/
- 10. Data science pipeline Source: h[p://cacm.acm.org/blogs/blog-‐cacm/169199-‐data-‐science-‐workflow-‐overview-‐and-‐challenges/fulltext
- 11. Where does Kaggle fit it? Degree breakdown in top 100 Areas of study
- 12. What’s the deal with big data?
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- 14. It’s like Map Reduce you know
- 15. So what about machine learning? Pioneer in machine learning, created a checkers game that played itself “Give machines the ability to learn without explicitly programming them.” Arthur L. Samuel (1959)
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- 19. Build a model Underfit Overfit Linear Regression Solve for values of θ in the Hypothesis func&on hθ(x)
- 20. Gradient descent algorithm Minimize cost func&on which is ½ of average square error of predic&on vs. the training data.
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- 22. Cross valida&on – split training/test
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- 24. Recommender systems Collabora&ve filtering – predict ra&ngs for similar items given other users behavior
- 25. Collabora&ve filtering method Source: h[p://cran.r-‐project.org/web/packages/recommenderlab/vigne[es/recommenderlab.pdf
- 26. Similar users based on distance Manha[an distance Euclidian distance
- 27. Demo: Music recommender system Pearson Correla&on Coefficient
- 28. Visualiza&on frameworks Tableau D3.js Processing Raphaël.js
- 29. What about online experimenta&on?
- 30. What will the future look like • Online collabora&on • Open Data
- 31. Next gen distributed compu&ng 100x faster in memory, and 10x faster even when running on disk.
- 32. Deep learning, a new fron&er? Geoffrey Hinton @Google
- 33. How can I get started? • MOOCs – Coursera Machine Learning (Andrew Ng -‐ Stanford) – Learning from Data (Abu-‐Mostafa -‐ Caltech) • Other references – Collec&ve Intelligence – Mining of massive data sets – Open-‐Source Data Science Masters • Frameworks – Python – Scikit learn – Java – WEKA and Cascading
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