Python for Data Science - TDC 2015
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In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
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Python for Data Science - TDC 2015
- 1. PYTHON FOR DATA SCIENCE Gabriel Moreira Machine Learning Engineer @gspmoreira 2015
- 2. Why so much buzz?
- 4. Big Data
- 6. WHAT IS DATA SCIENCE http://drewconway.com
- 7. WHAT IS DATA SCIENTIST A Data Scientist is someone with deliberate dual personality who can first build a curious business case defined with a telescopic vision and can then dive deep with microscopic lens to sift through DATA to reach the goal while defining and executing all the intermittent tasks. http://www.datasciencecentral.com/profiles/blogs/are-you-a-data-scientist
- 10. 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/
- 11. [Hillary Mason, Data Scientist] Inquire( Obtain( Scrub( Explore( Model( iNterpret( DATA SCIENCE IS IOSEMN
- 13. ANALYTICS CASE CORPORATE SOCIAL NETWORKS
- 14. Full Data Analysis demo available in IPython Notebook bit.ly/python4ds_nb
- 17. INQUIRE 1.Which communities are more popular? 2.Is the user engagement increasing? 3.What is the distribution of publishing time? 4.What is the distribution of user interactions? 5.Is there a relationship between publishing hour and number of interactions?
- 19. 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)
- 20. READING INTERACTIONS FROM CVS
- 21. READING POSTS FROM JSON LINES
- 23. SCRUB
- 24. SCRUB
- 25. SCRUB
- 27. SCRUB
- 29. 1 - WHICH COMMUNITIES ARE MORE POPULAR?
- 30. 1 - WHICH COMMUNITIES ARE MORE POPULAR?
- 31. 2 - IS USER ENGAGEMENT INCREASING?
- 32. 2 - IS USER ENGAGEMENT INCREASING?
- 33. 3 - WHAT ISTHE DISTRIBUTION OF PUBLISHINGTIME?
- 34. 4 - HOW ISTHE DISTRIBUTION OF USER INTERACTIONS?
- 35. 4 - HOW ISTHE DISTRIBUTION OF USER INTERACTIONS?
- 36. 4 - HOW ISTHE DISTRIBUTION OF USER INTERACTIONS?
- 37. 5 - RELATIONSHIP BETWEEN PUBLISHINGTIME AND NUMBER OF INTERACTIONS?
- 38. 5 - RELATIONSHIP BETWEEN PUBLISHINGTIME AND NUMBER OF INTERACTIONS?
- 39. 5 - RELATIONSHIP BETWEEN PUBLISHINGTIME AND NUMBER OF INTERACTIONS?
- 40. 5 - RELATIONSHIP BETWEEN PUBLISHINGTIME AND NUMBER OF INTERACTIONS? http://viverdeblog.com/melhoresahorarios-para-postar-nas-redes-sociais/
- 43. 1. Discover the most relevant words in the posts 2. Find related posts, with similar content Operational AnalyticsTasks example Find Related Posts
- 44. 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
- 45. 1 - RELEVANT WORDS IN A POST
- 46. 1 - RELEVANT WORDS IN A POST
- 47. 1 - RELEVANT WORDS IN A POST
- 48. BONUS - GLOBAL RELEVANTTERMS [ALL POSTS]
- 49. 2 - SIMILAR POSTS Cosine Similarity Measure of similarity between two vectors being the cosine of the angle between them.
- 50. 2 - SIMILAR POSTS
- 51. 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
- 52. SIMILAR PEOPLE!
- 54. INTERPRET •Drawing conclusions from your data •Evaluating what your results mean •Communicating your result
- 55. DATA PRODUCTS “If information has context and the context is interactive, insights are not predictable." [Agile Data Science, O’Reilly, 2014]
- 59. What about Python for Big Data?
- 60. PYTHON ON HADOOP Streaming HADOOPY Pig UDFs in Jython
- 61. HADOOP STREAMING Hadoop Streaming - Allows MapReduce jobs from any executable script - including Python
- 62. HADOOP STREAMING http://workingsweng.com.br/2014/04/clusterizando-raios-com-hadoop-e-k-means-em-map-reduce/ K-Means with Python on MapReduce 140.000 lightnings em 28/02/2014 in 137 data files Running on Amazon Elastic Map Reduce •Instances: 10 m1.small •Time (k=10): 10 iterations => 32 minutes •Time (k=50): 50 iterations => 164 minutes
- 64. IS DATA SCIENTISTTHE NEW WEBMASTER?
- 65. [Doing Data Science, O’Reilly, 2014]
- 66. 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/
- 67. BOOKS
- 69. Gabriel Moreira @gspmoreira http://about.me/gspmoreira Thank you! 2015 PYTHON FOR DATA SCIENCE Slides: http://bit.ly/python4ds_tdc