Python for Data Science - Python Brasil 11 (2015)

This talk, presented at Python Brasil 11 (2015), demonstrates a complete Data Science process, involving Obtaining, Scrubbing, Exploring, Modeling and Interpreting data using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn.

Transcript

1. PYTHON FOR
   DATA SCIENCE Gabriel Moreira Machine Learning Engineer @gspmoreira PythonBrasil 2015
2. Why so much buzz?
3. https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
4. WHAT IS DATA SCIENCE http://drewconway.com
5. TYPES OF ANALYTICS Investigative Analytics Operational Analytics Consumers: Humans Consumers: Machines http://blog.cloudera.com/blog/2014/03/why-apache-spark-is-a-crossover-hit-for-data-scientists/ https://hbr.org/2014/08/the-question-to-ask-before-hiring-a-data-scientist/
6. [Hillary Mason, Data Scientist] Inquire( Obtain( Scrub( Explore( Model( iNterpret( DATA SCIENCE IS IOSEMN
7. Inquire( Obtain( Scrub( Explore( Model( iNterpret( PYTHON IS IOSEMN js Outsider
8. ANALYTICS CASE
 CORPORATE SOCIAL NETWORKS
9. Full Data Analysis demo available in IPython Notebook bit.ly/python4ds_nb
10. Investigative Analytics Consumers: Humans
11. Inquire( Obtain( Scrub( Explore( Model( iNterpret(
12. INQUIRE 1. Which communities are more popular? 2. Is the user engagement increasing? 3. What is the distribution of user interactions? 4. Is there a relationship between publishing hour and number of interactions?
13. Inquire( Obtain( Scrub( Explore( Model( iNterpret(
14. OBTAIN •Download data from another location (e.g., a web page or server) •Query data from a database (e.g., MySQL or Oracle) •Extract data from an API (e.g., Twitter, Facebook) •Extract data from another file (e.g., an HTML file or spreadsheet) •Generate data yourself (e.g., reading sensors or taking surveys)
15. READING INTERACTIONS FROM CVS
16. READING POSTS FROM JSON LINES
17. Inquire( Obtain( Scrub( Explore( Model( iNterpret(
18. SCRUB
19. SCRUB
20. SCRUB
21. SCRUB Dealing with nulls
22. SCRUB
23. Inquire( Obtain( Scrub( Explore( Model( iNterpret(
24. 1 - WHICH COMMUNITIES ARE MORE POPULAR?
25. 1 - WHICH COMMUNITIES ARE MORE POPULAR?
26. 2 - IS USER ENGAGEMENT INCREASING?
27. 2 - IS USER ENGAGEMENT INCREASING?
28. 3 - HOW IS THE DISTRIBUTION OF USER INTERACTIONS?
29. 3 - HOW IS THE DISTRIBUTION OF USER INTERACTIONS?
30. 3 - HOW IS THE DISTRIBUTION OF USER INTERACTIONS?
31. 4 - RELATIONSHIP BETWEEN PUBLISHING TIME AND NUMBER OF INTERACTIONS?
32. 4 - RELATIONSHIP BETWEEN PUBLISHING TIME AND NUMBER OF INTERACTIONS?
33. 4 - RELATIONSHIP BETWEEN PUBLISHING TIME AND NUMBER OF INTERACTIONS?
34. 4 - RELATIONSHIP BETWEEN PUBLISHING TIME AND NUMBER OF INTERACTIONS? http://viverdeblog.com/melhoresahorarios-para-postar-nas-redes-sociais/
35. Operational Analytics Consumers: Machines
36. Inquire( Obtain( Scrub( Explore( Model( iNterpret(
37. 1. Discover the most relevant words in the posts 2. Find related posts, with similar content Operational Analytics Tasks example Find Related Posts
38. 1 - RELEVANT WORDS IN A POST TF-IDF - More “relevant" terms in a document are frequent terms in the document and rare in other documents
39. 1 - RELEVANT WORDS IN A POST
40. 1 - RELEVANT WORDS IN A POST
41. 1 - RELEVANT WORDS IN A POST
42. BONUS - GLOBAL RELEVANT TERMS [ALL POSTS]
43. 2 - SIMILAR POSTS Cosine Similarity
 Measure of similarity between two vectors 
 being the cosine of the angle between them.
44. 2 - SIMILAR POSTS
45. 2 - SIMILAR POSTS Original Post Did you ever wonder how great it would be if you could write your jmeter tests in ruby ? This projects aims to do so. If you use it on your project just let me now. On the Architecture Academy you can read how jmeter can be used to validate your Architecture. modulo 13 arch definition architecture validation | academia de arquitetura
 
 Most similar post (cosine similarity = 0.30)
 Foram disponibilizados no site Enterprise Architecture, na parte de Knowledge Base de performance, alguns how-tos relacionados a testes de performance.Entre eles, como definir os requisitos (throughput, cálculo de threads para o JMeter etc.), utilização do JMeter, geração de massa de dados e monitoramento. planning and executing performance testing | enterprise architecture - how to identify performance acceptance criteria | enterprise architecture - how to geracao de massa de dados | enterprise architecture - how to jmeter | enterprise architecture - how to monitoramento | enterprise architecture
46. SIMILAR PEOPLE!
47. Inquire( Obtain( Scrub( Explore( Model( iNterpret(
48. INTERPRET •Drawing conclusions from your data •Evaluating what your results mean •Communicating your result
49. DATA PRODUCTS “If information has context and the context is interactive, insights are not predictable." [Agile Data Science, O’Reilly, 2014]
50. SENTIMENT ANALYSIS bit.ly/eleicoes2014debatesbt Analytical Dashboard
51. SENTIMENT ANALYSIS Analytical Dashboard bit.ly/eleicoes2014debatesbt
52. NETWORK ANALYSIS https://linkedjazz.org/network/ js
53. What about 
 Python for Big Data?
54. PYTHON FOR BIG DATA Streaming HADOOPY Pig UDFs 
 in Jython
55. DATA SCIENCE COURSES • Introduction to Data Science (Univ. of Washington) • Data Science specialization (Johns Hopkins) • Intro to Hadoop and MapReduce (Cloudera) • Machine Learning (Stanford) • Statistical Learning (Stanford) • Mining Massive Datasets (Stanford) • Scalable Machine Learning (Berkeley) http://workingsweng.com.br/2014/04/cursos-mooc-e-especializacoes-em-data-science/
56. BOOKS
57. Happy data geeking!
58. Gabriel Moreira @gspmoreira http://about.me/gspmoreira Thank you! PYTHON FOR DATA SCIENCE Slides: http://bit.ly/python4ds_pybr11 PythonBrasil 2015