The Polyglot Data Scientist - Exploring R, Python, and SQL Server
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This document provides an overview of a presentation on being a polyglot data scientist using multiple languages and tools. It discusses using SQL, R, and Python together in data science work. The presentation covers the challenges of being a polyglot, how SQL Server with R or Python can help solve problems more easily, and examples of analyzing sensor data with these tools. It also discusses resources for learning more about R, Python, and machine learning services in SQL Server.
The Polyglot Data Scientist - Exploring R, Python, and SQL Server
- 1. The Polyglot Data Scientist Adventures with R, Python, and SQL
- 2. Audience Survey • How many here have used: – SQL? – Python? – R? • What job titles do people have?
- 3. What We Won’t Cover • Theories behind data science and machine learning • Deep dive into Python • Deep dive into R • Deep dive into SQL Server
- 4. There is a data science VM available on Azure. It won’t be covered in this presentation. See https://docs.microsoft.com/en- us/sql/advanced-analytics/getting-started- with-machine-learning-services for details. Azure Support
- 5. What We Will Cover • The Problem with Being a Polyglot • What SQL Server + R or SQL Server + Python Solves • A Glance at these in Action
- 6. Not a Microsoft sales person… • Microsoft MVP in Visual Studio • Been into exploring data most of my life • Been in tech over 20 years • Practitioner and hobbyist, not researcher
- 7. Sample Problem: Sensor Data • Domain: House of Sadukie • Problem: Temperature data is stored miserably • Goal: Display data in a visualization that makes sense
- 8. Current Outcome – via MySQL & R
- 9. Polyglot Knowing or using several languages
- 10. SQL Server
- 11. Data Scientist A person employed to analyze and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making
- 12. Multi-Faceted Data Science • Various categories: – Statistics – modeling, sampling, clustering, reduction – Mathematics – NSA, astronomers, military – Data engineering – database/memory/file optimization, Hadoop, data flows – Machine learning and algorithms – Business – ROI optimization, decision sciences – Software engineering – primarily polyglots in production code – Visualization – Spatial Source: https://www.datasciencecentral.com/profiles/blogs/six-categories-of- data-scientists
- 13. The Problem with Being a Polyglot • Understanding strengths and weaknesses of the languages • Knowing which language is appropriate for what situation
- 14. Multiple tools… multiple solutions… how many programs do I have to use?!? And wouldn’t it be awesome if I could use one tool to do most of the work?
- 15. What R and Python Have to Offer for SQL • Libraries specialized to handle data science domain problems including: – Visualization – Data exploration – Statistical and Mathematical Analysis – Trending – Regression • Libraries + Data right from the source = quicker exploratory analysis • Python and R are great working from one large table and branch for different directions – Which can inspire additional analyses
- 16. Sample Problem: Sensor Data • Number of rows: 400k+ • 1 Table • Questions to look into: – What are temperature trends over time? – When are sensors going offline? – What temperatures look spot on? – What sensors are wavering in reads and showing inconsistencies?
- 17. Bringing the Computation to the Data
- 18. Advanced Analytics in SQL Server 2016/2017 • SQL Server 2016 • SQL Server R Services / Machine Learning Services • SQL Server 2017 • SQL Server R Services / Machine Learning Services • Python Support
- 19. Sample Problem: Sensor Data • Possible Strategy: – Use SQL to gather the data into a dataset that has the most amount of data to observe. – Use Python or R to manipulate the data results and allow for easy analysis and substantial predictions based on observations.
- 20. Not Just Windows! R Server for Windows R Server for Linux - CentOS - RHEL - Ubuntu - SUSE R Server for Hadoop – cluster in the cloud R Server for Teradata – not as Machine Learning Server
- 21. SQL Server as our Base R and/or Python on Top Additional pieces provided by MachineML: Microsoft Machine Learning Services, RevoScaleR, RevoScalePy
- 23. Machine Learning Services in SQL Server • Allows integration of other languages in SQL Server – SQL Server 2016 can work with R – SQL Server 2017 introduces Python support • Scalable in that you can develop and test on a single machine and then deploy to distributed or parallel processing platforms. Platforms include: – SQL Server on Windows – Hadoop – Spark
- 24. SQL Server Machine Learning Services (In-Database) • SQL Server R Services (In-Database) started in SQL Server 2016 • With SQL Server 2017, SQL Server Machine Learning Services (In- Database) allows us to use R and Python within SQL Server • Do not need to open IDE and SQL tools to accomplish the work – no context switching needed! • Can call libraries from Python or R to process data right within SQL
- 25. Python vs R? • SQL Server 2016? R • SQL Server 2017? R and/or Python • What are you familiar with? • Look at tutorials – what makes sense? • What features do you need and how are they supported by Microsoft ML?
- 26. Python Support • CPython 3.5 • revoscalepy – Python equivalents of RevoScaleR • Remote compute contexts • Also supports familiar libraries such as: – scikit-learn – Tensorflow – Caffe – Theano/Keras
- 27. R Code in SQL DECLARE @rscript NVARCHAR(MAX); SET @rscript = N' SensorData <- SqlData; print(summary(SensorData))'; DECLARE @sqlscript NVARCHAR(MAX); SET @sqlscript = N' SELECT * FROM Sensors;'; EXEC sp_execute_external_script @language = N'R', @script = @rscript, @input_data_1 = @sqlscript, @input_data_1_name = N'SqlData', @output_data_1_name = N'SensorData';
- 28. Python Code in SQL execute sp_execute_external_script @language = N'Python', @script = N' summary = pandas.DataFrame.describe(InputDataSet) print(summary.transpose()) ', @input_data_1 = N'SELECT * FROM Sensors'; GO
- 30. What is RevoScaleR? • A library written in R that includes functions for importing, transforming, and analyzing data • Scalable, portable, and easily distributable • Things it can do include: – Descriptive statistics – Generalized linear models – Logistic Regression – Classification trees – Decision forest • Multithreaded and multinode
- 31. Running RevoScaleR • Part of the Machine Learning Server and Microsoft R products • Can use any R IDE to write scripts that use RevoScaleR • Needs to be run on a computer with the interpreter and libraries • Two modalities: – Locally – Remote compute context – Shift execution to the server – Windows server – Hadoop – Spark
- 32. Prediction • Linear models • Logistic regression models • Generalized linear models • Covariance and correlation • Decision forest • K-means clustering
- 34. Typical Workflow with RevoScaleRAnalyVVisuaMoveData Import / Export TidyData Clean Manipulate Transform PresentData Visualize MakeDecisions Analyze Learn Predict
- 35. Key Pieces for Analysis with RevoScaleR Data Source Compute Context Analytic Function
- 36. Data Sources • Comma-delimited text data • SAS • SPSS • XDF • ODBC • Teradata • SQL Server
- 37. Graphing with RevoScaleR • rxHistogram • rxLinePlot • rxLorenz • rxRocCurve
- 38. Descriptive Statistics • rxQuantile • rxSummary • rxCrossTabs • rxCube
- 39. Two Use Cases for Remote Computer Context • Running R in T-SQL scripts or stored procedures • Calling RevoScaleR in R from a SQL context
- 40. Visual Studio 2017: One IDE with Common Tools • Python Tools for Visual Studio • R Tools for Visual Studio • SQL Server capabilities within Visual Studio
- 42. Polyglot Data Scientist Presentation Resources • R Services in SQL Server 2016 (Channel 9) • Built-in machine learning in Microsoft SQL Server 2017 with Python (Build 2017) • MicrosoftML 1.3.0: What’s new for machine learning in Microsoft R Server (Channel 9) • Using Visual Studio for Machine Learning (Build 2017) • Performance patterns for machine learning services in SQL Server (Microsoft Ignite 2017)
- 43. Learn More
- 44. Resources • Kaggle: The Home of Data Science and Machine Learning • DataCamp: Learn R, Python, and Data Science Online • Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics – Vincent Granville • Python Tutorial from Mode Analytics • Coursera – Mastering Software Development in R Specialization – Data Science Specialization – Applied Data Science with Python Specialization – Executive Data Science Specialization
- 45. Contact Me • Twitter: @sadukie • Blog: http://codinggeekette.com • Email: [email protected] Sarah Dutkiewicz Cleveland Tech Consulting, LLC Owner