The Developer's Guide to Data Science by Helge Reikeras

The myth of data science holds that you need an army of machine learning PHDs to be able to implement anything impactful with data science. In this talk I will attempt to dispel this myth and show how software developers can skip getting the machine learning PHD and start building awesome software with Data Science and Machine Learning today.

I believe developers are well situated to implement data science projects as they possess the understanding of how the product works, how users like to interact with the product and their opinions are already valued within the business.

To help developers level up their data science skills, I’ll discuss the core concepts behind the most prevalent methods in data science, how the data science process works, how to think like a data scientist, which frameworks and programming languages to choose (surprise Python!) and how to measure and communicate the value-add of data science projects to the business.

About the speaker: Helge Reikeras is a Data Scientist at Offerzen with over 6 years experience in practical data science and has also worked as a software developer at various points in his career.

Transcript

1. The Developers Guide to Data Science
2. Why Offerzen?
3. Source
4. Model V2 Make Interested in Make? https://make.offerzen.com helge@offerzen.com
5. Why this talk? ✓ ✓ ✓
6. 6 Named Entity Recognition
7. Named Entity Recognition
8. Tickets for PyConZA 2018 (rigid designator), the best conference ever (flaccid designator), will be on sale at Quicket and at the entrance. Model V2 Named Entity Recognition
9. Xbox is a video gaming brand created and owned by Microsoft. Model V2 Named Entity Recognition
10. Pirates of the Caribbean is a 2003 American fantasy swashbuckler directed by Gore Verbinski and the first film in the Pirates of the Caribbean film series. Model V2 Named Entity Recognition
11. Donal J. Trump (rigid designator) is the 45th and current President of the United States (flaccid designator). In his past time he enjoys eating meat loaf and listening to the rock artist meat Loaf. Model V2 Named Entity Recognition
12. Apple released the iPhone in 2007combining three products—a revolutionary mobile phone, a widescreen iPod with touch controls, and a breakthrough Internet communications device with desktop-class email, web browsing, searching and maps—into one small and lightweight handheld device. Model V2 Named Entity Recognition Apple Reinvents the Phone with iPhone.
13. What is Machine Learning? ✘
14. What is Machine Learning? ✓
15. n-Grams Words Characters
16. POS tagging
17. Feature Encoding
18. Linear models
19. Non-linear models
20. Non-linear models
21. Deep Learning Models AlexNet (2012)
22. 22 Feedforward Neural Network
23. 23 Word Embeddings
24. Vector Space Model
25. Word Embedding world
26. 26 Recurrent Neural Networks
27. 27 Recurrent Neural Networks
28. 28 Recurrent Neural Networks
29. 29 Recurrent Neural Networks
30. 30 Recurrent Neural Networks
31. 31 Recurrent Neural Networks
32. 32 Recurrent Neural Networks
33. Bidirectional LSTM + CNN
34. Evaluating performance
35. Questions? Model V2 Q&A