Data Science Program


Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.  Same datasets, Data.GOV.

SAS Data Science Resource Hub

Data Science using SAS (New Three-Day Content, Instructor based Online class with limited students)

Sep 29 - Oct 28, Data Science using SAS, UCLA Extention  (Certificate Program)

Apr 2 - Jun 1, Data Science using SAS, UCSD Extention

Topics: 

1. SAS Enterprise Guide - Tool, Documentation

2. Proc SQL - Query for Business Questions and Joins for Data Preparation

3. Data Step and Merge - Programming Logic, SAS Functions/Formats

4. SAS Certification Exam - Base, Advanced and Clinical

5. Statistical Analysis - Data Mining/Intition, Exploratory Data Analysis (EDA)

6. Statistical Graphics - Visualization

7. SAS Debugging - Tools

See also:

1. SAS MindMaps – Navigation around SASSavvy.com

2. Project Management – Teams, Manage Resources and Timelines

3. External Files - Excel files

4. Clinical Data Management – Data Cleaning and Management

5. New Clinical Programmer – Clinical Trials and Statistics Basics

6. SAS Technical Interview – Sample Topics and Questions

7. SAS Technical Presentations – Communicate technical results and issues

8. Compare and Conquer – Master concepts and leverage SAS procedure features, Proc Compare

9. Business Intelligence ETL – Six Sigma, Extract, Transfer and Load data files

10. Metadata – Data-driven process for smarter data processing, Excel files

11. Macro Programming – Standardize and automate data processing

12. Program Efficiency – Leverage SAS system options for efficiency

13. SAS Institute – Resource for more information

14. Proc Transpose - Convert between rows (vertical) and columns (horizontal)

15. Dates - Dates and times

16. SAS Programming - Base and advanced SAS programming techniques

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1. What Every Data Scientist Needs to Know about SQL

2. 4 Must Have Skills Every Data Scientist Should Learn

3. A Brilliant Explanation of Decision Tree Algorithms

4. Data Science is Changing and Data Scientists will Need to Change Too – Here’s Why and How blog

5. Promoting precision medicine using data science of large datasets, Cytel

6. Data Science A-Z™: Real-Life Data Science Exercises Included

7. I ranked every Intro to Data Science course on the internet, based on thousands of data points

8. Hiring Data Scientists: What to Look for?

9. What Are Business Reports And Why They Are Important: Review And Examples

10. Sale Performance Dashboards

11. Creating a Successful Data Science Program – A Joint Academic and Industry Perspective, Krzysztof Dzieciolowski

12. Data Management Meets Machine Learning, Gregory Nelson

13. Demystifying Buzzwords: Using Data Science and Machine Learning on Unsupervised Big Data Ben Murphy

14. Take the Data Cleansing Challenge

15. Working with Big Data in SAS®, Mark Jordan

16. Data Mining and Statistics in a pharmaceutical environment, Franky De Cooman 

17. EXPLORATORY DATA ANALYSIS: GETTING TO KNOW YOUR DATA, Michael Walega

18. Exploring, Analyzing, and Summarizing Your Data: Choosing and Using the Right SAS Tool from a Rich Portfolio, Douglas Thompson

19. How to be a Data Scientist Using SAS, Charles Kincaid

20. SAS® Does Data Science: How to Succeed in a Data Science Competition, Patrick Hall

21. The Elusive Data Scientist: Real-world analytic competencies, Gregory Nelson, Monica Horvath

22. Data Science Rex: How data science is Evolving (Or Facing Extinction) Across the Academic Landscape, Jennifer Priestley

23. Star Wars and the Art of Data Science: An Analytical Approach to Understanding Large Amounts of Unstructured Data, Mary Osborne and Adam Maness

24. SAS-Enterprise Guide for Institutional Research and Other Data Scientists, Claudia McCann

25. Mine the GAP: How the Role of the Data Scientist Fills a Need in the Pharmaceutical Industry [Presentation]

26. Managing the Change – Evolving from Statistical Programmers to Clinical Data Scientists, Sascha Ahrweiler [Presentation]

27. Programming Techniques for Data Mining with SAS, Samuel Berestizhevsky, Tanya Kolosova

28. Get to the Meat on Machine Learning, Aadesh Shah

29. Mine the Gap: How the Role of Data Scientist Fills a Need in the Pharmaceutical Industry, Michael Rimler, Jorine Putter

30. EFFECTIVE USE OF A METADATA REPOSITORY ACROSS DATA OPERATIONS: THE NEED FOR A MACHINE READABLE FORM OF (PART OF) THE PROTOCOL [Presentation]

31. The Data Science Revolution in Pharma Industry, Linga Aenugu

32. Cows or Chickens: How You Can Make Your Models into Containers, Hongjie Xin, Jacky Jia, David Duling, Chris Toth

33. Creating a Data Quality Scorecard, Tom Purvis, Clive Pearso

34. Common Sense Tips and Clever Tricks for Programming with Extremely Large SAS® Data Sets, Kathy Fraeman

35. DATA Step in SAS Viya: Essential New Features, Jason Secosky

36. Coding in SAS Viya, Charu Shankar

37. Best Practices for Converting SAS® Code to Leverage SAS Cloud Analytic Services, Steven Sober, Brian Kinnebrew

38. How to refactor SAS code to leverage SAS Viya, SAS Blog

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SAS Book Examples

The Next Step: Integrating the Software Life Cycle with SAS Programming

 

References

Data Science Cheat Sheet blog

Data Science for Business Table of Contents

The Scientists Channel

SAS® Academy for Data Science

UCLA Data Science Program

Elements of Statistical Learning

THE CHALLENGES OF GROWING A BUSINESS - AND HOW TO MEET THEM

5 Questions To Prepare You For Your Next Data Science Interview

24 Ultimate Data Science Projects To Boost Your Knowledge and Skills

SAS Machine Learning

Understanding and interpreting your data set SAS Blog

Big Data, Data Mining, and Machine Learning SAS Class

Khan Academy - Statistics and probability

7-Step Guide to Making Your Data Science Resume Stand Out blog

5 data management best practices to help you do data right SAS Blog

Statistical Tests - When to use Which? blog

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