Intro to Data Science by DatalentTeam at Data Science Clinic#11
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This document provides an overview of data science including: - Definitions of data science and the motivations for its increasing importance due to factors like big data, cloud computing, and the internet of things. - The key skills required of data scientists and an overview of the data science process. - Descriptions of different types of databases like relational, NoSQL, and data warehouses versus data lakes. - An introduction to machine learning, data mining, and data visualization. - Details on courses for learning data science.
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Intro to Data Science by DatalentTeam at Data Science Clinic#11
- 1. Introduction to Data Science @Data Science Clinic #11 6-Jun-2017 All Season Place Dr. Sotarat Thammaboosadee @DatalentTeam
- 2. Agenda • What is Data Science? • Motivation • Data Scientist’s Skill • Data Science Process • Relational Database vs NoSQL Database • Data Warehouse vs Data Lake • AI / Machine Learning / Data Mining • Data Visualization • Courses
- 7. Datalent Team Member • Wichian Boonyaprapa (Aod) – Business Analyst and Intelligence • Siriraj Hospital • Chanwit Onsumran (Earth) – Financial Analyst • Mahidol University
- 8. Datalent Team Member • Teerapat Kansadub (Champ) – Data Engineer • Faculty of Physical Therapy, Mahidol university • Chanon Srisuwan (Fern) – Process Engineer • Ramathibodi Hospital
- 9. Datalent Team: Data Talent Development Research Group • FB: Datalent Team
- 10. Datalent Team: Data Talent Development Research Group • Website: http://www.datalentteam.com
- 11. What is Data Science? • Data Science – is the study of the generalizable extraction of knowledge from data (Wikipedia) – is getting predictive and/or actionable insight from data (Neil Raden) – Involves extracting, creating, and processing data to run it into business value (Vincent Granville)
- 12. What’s new? • Data science is not new, Data science is just modernizing existing reporting solution, analytics solution, data warehousing solution, business intelligence solution, and even data management solution. (Jothi Periasamy) • So, Data science is… – New thinking – New ideas – New data source – New data structure – New data architecture – New data processing mechanism – New innovation on data – New way of solving problems
- 14. Motivation: Why data science now? http://datascienceth.com/why-data-science-now/
- 18. Internet of Things http://www.cbronline.com/what-is/what-is-consumer-internet-of-things-4926794/
- 20. Data Scientist’s Skill http://www.kdnuggets.com/2014/06/data-science-skills-business-problems.html
- 24. Data Scientist
- 25. Data Analyst
- 27. Data Consultant
- 42. Data Science Life Cycle http://blog.revolutionanalytics.com/2016/10/the-team-data-science-process.html
- 45. Six Types of Databases 45 Relational Analytical (OLAP) Key-Value Column-Family key value key value key value key value DocumentGraph https://www.slideshare.net/Couchbase/webinar-making-sense-of-nosql-applying- nonrelational-databases-to-business-needs
- 46. Relational • Data is usually stored in row by row manner (row store) • Standardized query language (SQL) • Data model defined before you add data • Joins merge data from multiple tables • Results are tables • Pros: mature ACID transactions with fine-grain security controls • Cons: Requires up front data modeling, does not scale well 46https://www.slideshare.net/Couchbase/webinar-making-sense-of-nosql-applying- nonrelational-databases-to-business-needs
- 47. Analytical (OLAP) • Based on "Star" schema with central fact table for each event • Optimized for analysis of read- analysis of historical data • Use of MDX language to count query "measures" for "categories" of data • Pros: fast queries for large data • Cons: not optimized for transactions and updates 47https://www.slideshare.net/Couchbase/webinar-making-sense-of-nosql-applying- nonrelational-databases-to-business-needs
- 48. Key-Value Stores • Keys used to access opaque blobs of data • Values can contain any type of data (images, video) Pros: scalable, simple API (put, get, delete) Cons: no way to query based on the content of the value 48 key value key value key value key value https://www.slideshare.net/Couchbase/webinar-making-sense-of-nosql-applying- nonrelational-databases-to-business-needs
- 49. Column-Family • Key includes a row, column family and column name • Store versioned blobs in one large table • Queries can be done on rows, column families and column names • Pros: Good scale out • Cons: Can not query blob content, row and column designs are critical 49 Examples: HBase, Cassandra https://www.slideshare.net/Couchbase/webinar-making-sense-of-nosql-applying- nonrelational-databases-to-business-needs
- 50. Graph Store • Data is stored in a series of nodes and properties • Queries are really graph traversals • Ideal when relationships between data is key: – e.g. social networks • Pros: fast network search, works with public linked data sets • Cons: Poor scalability when graphs don't fit into RAM, specialized query language 50 Examples: Neo4j, AllegroGraph https://www.slideshare.net/Couchbase/webinar-making-sense-of-nosql-applying- nonrelational-databases-to-business-needs
- 51. Document Store • Data stored in nested hierarchies • Logical data remains stored together as a unit • Any item in the document can be queried • Pros: No object-relational mapping layer, ideal for search • Cons: Complex to implement, incompatible with SQL 51 Examples: MongoDB, Couchbase https://www.slideshare.net/Couchbase/webinar-making-sense-of-nosql-applying- nonrelational-databases-to-business-needs
- 53. Data Warehouse vs Data Lake http://datawarehouse4u.info/
- 56. What is data mining?
- 57. AI / Machine Learning / Data Mining http://blogs.sas.com/content/subconsciousmusings/2014/08/22/looking- backwards-looking-forwards-sas-data-mining-and-machine-learning/
- 63. CRISP-DM CRoss-Industry Standard Process for Data Mining
- 65. CRISP-DM: Phases • Business Understanding Project objectives and requirements understanding, Data mining problem definition • Data Understanding Initial data collection and familiarization, Data quality problems identification • Data Preparation Table, record and attribute selection, Data transformation and cleaning • Modeling Modeling techniques selection and application, Parameters calibration • Evaluation Business objectives & issues achievement evaluation • Deployment Result model deployment, Repeatable data mining process implementation
- 66. Phases and Tasks Business Understanding Data Understanding Data Preparation Modeling DeploymentEvaluation Format Data Integrate Data Construct Data Clean Data Select Data Determine Business Objectives Review Project Produce Final Report Plan Monitering & Maintenance Plan Deployment Determine Next Steps Review Process Evaluate Results Assess Model Build Model Generate Test Design Select Modeling Technique Assess Situation Explore Data Describe Data Collect Initial Data Determine Data Mining Goals Verify Data Quality Produce Project Plan
- 74. Courses • [Microsoft] https://academy.microsoft.com/en- us/professional-program/ • [IBM] https://cognitiveclass.ai/ • [Datacamp] https://www.datacamp.com • DataScience Thailand • Evenmore…
- 83. ดร. โษฑศ์รัตต ธรรมบุษดี (โอม) zotarat@gmail.com Line: @zotarutto FB: Datalent Team http://www.datalentteam.com