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Ask a Recruiter: Managing Your Analytics & Data Science Career If you’ve ever wished you had a career advisor that specialized in the analytics and data science fields, now’s your chance! WCSUG is happy to have the Burtch Works’ data science and analytics recruiting team for a Lunch & Learn to hear about: • When to change jobs • Which skills are in demand • What companies look for, and how to choose which tools to learn • How to prep for interviews • Resume tips • How you can keep your skills sharp The WCSUG and Burtch Works' team will be sharing their knowledge from years of recruiting in this space, as well as answering questions to help set you up for long-term career success. We look forward to seeing you there! RSVP for this Free/Open Webinar About Burtch Works Burtch Works is a targeted recruiting firm whose quantitative specializations range from data science and predictive analytics to marketing research and data engineering, among many others. Over the past several years, Burtch Works has been repeatedly mentioned in the press, including The New York Times, The Wall Street Journal, CNBC, The Chicago Tribune, InformationWeek, Analytics Magazine, and many more. Burtch Works has also been recognized by Forbes as one of America’s Best Recruiting Firms. Burtch Works recruits for positions on both a contingency and retained basis with firms ranging from Fortune 50 corporations to growing startups and private equity firms. Additionally, Burtch Works recruiters maintain close relationships with quantitative professionals throughout their careers, working with early career through executive-level leadership professionals. Katie Ferguson Katie Ferguson, Partner & Executive Recruiter at Burtch Works, has over 15 years’ experience in recruiting, specializing in roles within quantitative marketing science and predictive analytics. Katie helped launch Burtch Works’ mid- and junior-level analytics practice, and her extensive knowledge of the quantitative space allows her to share copious hiring market insights on the Burtch Works blog, as well as speak with students about analytics careers. Flora Jiang Flora Jiang, an Executive Recruiter at Burtch Works, is well-versed in the analytics space, having been a quantitative market researcher at Nielsen for several years. She also spent three years doing competitive analysis, tracking studies, and qualitative interviews as a marketing consultant for a digital ad agency. Flora’s deep knowledge of the quantitative talent landscape allows her to share insights in numerous blogs and webinars, in addition to speaking to students at many universities on the intricacies of quantitative careers. Stefan Vallentine Stefan Vallentine spent several years recruiting for actuaries and predictive modelers in the insurance industry, and now partners with Katie Ferguson and Flora Jiang on the quantitative analytics team at Burtch Works. Stefan has enjoyed working in the quantitative space because of the fast pace of market activity and change in the space, and likes to focus on developing long-term relationships with both candidates and clients.
Lunch n Learn Webinar Presenter Kiran Venna SAS Data Engineer About our Speaker Kiran is SAS Certified Advance Programmer for SAS®9 and Teradata 12 Certified Technical Specialist with 8 years of experience. He is the author of 5 SAS papers and has presented at various SAS conferences. He was designated as PROC Star twice in SAS communities for answering and helping others SAS Users on SAS Communities. Presentation Title ETL with SAS - some Basics and Optimizations Details This event is a Webinar and Free to our members. RSVP to get the event link sent to you.
Lunch and Learn Presentation Title Life Expectancy Tables: Getting SAS® to Run the Hard Math Presenters Anna Vincent Program Specialist, Texas Department of State Health Services Presentation Description Life tables are statistical tools that are typically used to portray the expectation of life at various ages. Life expectancy at birth is the most frequently cited life table statistic. It also provides information about the numbers of individuals who survive to various ages, median age at death, age-specific death rates, and the probability of dying at certain ages. The Texas Department of State Health Service creates life expectancy tables for publication in the Vital Statistics Annual Report every year. The old method demands that data be run using a Python program, then using a Java script, and then back to Python, which is a tedious process. In this paper, we respond to such a challenge by creating a SAS® syntax that produces a life table in one syntax. The syntax provides a general framework for building life tables, which is a fundamental tool in survival analysis. Details This event is a Webinar and Free to our members. RSVP to get the event link sent to you.
Lunch and Learn Presentation Title Using SAS® to Analyze ICD-9 and ICD-10 Diagnosis Codes Found in Administrative Health-Care Data Presenters Kathy Fraeman Director, Data Analytics and Principal Data Analyst, Evidera Presentation Description Administrative health-care data – including insurance claims data, electronic medical records (EMR) data, and hospitalization data – contains standardized diagnosis codes to identify diseases and other medical conditions. These codes use the short-form name of ICD, which stands for International Classification of Diseases. Much of the currently available health-care data contains the ninth version of these codes, referred to as ICD-9. Although, the more recent 10th version, ICD-10, is becoming more common in health-care data. These diagnosis codes are typically saved as character variables, are often stored in arrays of multiple codes representing primary and secondary diagnoses, and can be associated with either outpatient medical visits or inpatient hospitalizations. SAS® text processing functions, array processing, and the SAS colon modifier can be used to analyze the text of these codes and to identify similar codes or ranges of ICD codes. In epidemiologic analyses, groups of multiple ICD diagnosis codes are typically used to define more general comorbidities or medical outcomes. These disease definitions based on multiple ICD diagnosis codes, also known as coding algorithms, can either be hardcoded in a SAS program or defined externally from the programming. When coding algorithm definitions based on ICD codes are stored externally, the definitions can be read into SAS, transformed to SAS format, and dynamically converted into SAS programming statements. Details This event is a Webinar and Free to our members. RSVP to get the event link sent to you.