Introduction to Data Science
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Anastasiia Kornilova has over 3 years of experience in data science. She has an MS in Applied Mathematics and runs two blogs. Her interests include recommendation systems, natural language processing, and scalable data solutions. The agenda of her presentation includes defining data science, who data scientists are and what they do, and how to start a career in data science. She discusses the wide availability of data, how data science makes sense of and provides feedback on data, common data science applications, and who employs data scientists. The presentation outlines the typical data science workflow and skills required, including domain knowledge, math/statistics, programming, communication/visualization, and how these skills can be obtained. It provides examples of data science
Introduction to Data Science
- 2. WHO AM I? 3+ years in Data Science MS in Applied Mathematics Professional interests: recommendations systems, natural language processing, scalable data science solutions Authors of two blogs: energy鍖refox.blogspot.com, datascientistdiary.blogspot.com Fan of online education (20+ 鍖nished MOOCs)
- 3. What is Data Science and why do we need it? Data Scientists.Who they are and what do they do? How to start? Practical case AGENDA 3
- 7. MAKING SENSE OF DATA
- 9. DATA ISTHE NEW OIL
- 10. WHAT IS DATA SCIENCE? Andrew Conway 10
- 11. FRAUD DETECTION
- 12. RECOMMENDATIONS
- 13. OPTIMISATION
- 17. FIND PATTERNS IS USER BEHAVIOUR/ACTIONS
- 18. WHO USES DATA SCIENCE? 18
- 20. WHO DATA SCIENTISTS ARE AND WHAT DOTHEY DO? 20
- 21. TYPES OF DATA SCIENTISTS A - Analysis B - Building Robert Chang
- 22. DSTYPE A - ANALYSIS making sense of data or working with it in a fairly static way. very similar to a statistician (and may be one) knows all the practical details of working with data that arent taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data
- 23. share some statistical background withType A very strong coders and may be trained software engineers mainly interested in using data in production. build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results). DSTYPE B - BUILDING
- 24. WHAT DOTHEY DO?
- 25. Understand Collect Data exploration Clean and transform Model Validate Communicating results Deploy WHAT DOTHEY DO?
- 26. TYPICAL DATA SCIENCE WORKFLOW Preparing to run a model (Gathering, cleaning, transformation) Running the model Interpreting the results 80% of work - Aaron Kimball Other 80% of the work 26
- 28. DOMAIN KNOWLEDGE AND SOFT SKILLS Passionate about the business Curios about data In鍖uence without authority Hacker mindset Problem solver Strategic, proactive, creative, innovative and collaborative 28
- 29. MATH AND STATISTICS Machine learning Statistical modelling Experiment design Supervised learning Unsupervised learning Optimisation 29
- 30. PROGRAMMING AND DATABASES Computer science fundamentals Scripting language Statistical computing language Databases Relational algebra Distributed computations 30
- 31. COMMUNICATION AND VISUALIZATION Ability to engage with senior management Storytelling skills Visual art design Knowledge of a vizualisation tool Translate data-driven insights into decisions and actions 31
- 33. HARD WAY 33
- 34. TRADITIONAL WAY LITS - Machine Learning UCU - CS Master Degree Data Science Degree Kyivstar - Big Data University 34
- 35. EASY WAY 35
- 38. AND NOW WHAT? 38
- 41. AREYOU GOOD ENOUGH? AREYOU GOOD ENOUGH?41
- 42. KAGGLE STORY 42
- 44. WHAT CAN YOU FIND ON KAGGLE? Knowledge Money Job Reputation 44
- 45. 1. Understand 2. Collect 3. Data exploration 4. Clean and transform 5. Model 6. Validate 7. Communicating results Deploy 45
- 47. TIME FOR FUN San Francisco crimes analysis
- 48. THANK YOU!
- 49. LINKS https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6#.49jdojamn https://blog.kissmetrics.com/how-net鍖ix-uses-analytics/ http://recode.net/2015/10/07/jawbone-isnt-a-hardware-company-anymore-says-ceo-hosain-rahman/ https://jawbone.com/blog/napa-earthquake-effect-on-sleep/ http://cs.ucu.edu.ua/ http://lits.com.ua/course/machine-learning/ http://bigdata.kyivstar.ua/ https://www.kaggle.com/ http://inversquare.github.io/moon/mooncrime.html#part-2-crimes-of-passion http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0059030#abstract0 http://blog.babajob.com/wp-content/uploads/2015/08/manyhands-300x161.jpg http://www.criminalelement.com/images/stories/-2015-Jul-Sep/sherlock-holmes-benedict-cumberbatch.jpg