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.
See Business Analytics (Case Studies: Profit, Break-Even, Market-Entry, Diversification, Harvard Business Review)
SAS Data Science Resource Hub
Data Science using SAS (New Three-Day Content, Instructor based Online class with limited students)
UCLA Data Science Certificate Program
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
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
39. SAS Tutorial | 5 Steps to Your First Analytics Project Using SAS [Video]
40. A simple approach to text analysis using SAS functions, Wilson Suraweera, Jaya Weerasooriya, Neil Fernando
41. Hands-on Training for Machine Learning Programming, Kevin Lee
42. The Seven Most Popular Machine Learning Algorithms for Online Fraud Detection and Their Use in SAS, Patrick Maher
43. Machine Learning – Why we should know and How it works, Kevin Lee [Presentation]
44. An Overview about the PhUSE Machine Learning / Artificial Intelligence Team Project, Kevin Lee [Presentation]
45. Machine Learning SAS Global 2020 [Video]
46. SAS Techniques for Managing Large Datasets, Rucha Landge
47. SAS Macros for Large Scale Data Analysis and Quality Management of Corporate Actuarial Data Mart., Dennis Tang, Don Cooper
SAS Book Examples
The Next Step: Integrating the Software Life Cycle with SAS Programming
Machine Learning with SAS
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
How I became a Data Scientist SAS Blog
