Richelle Serrano Clindata Insight September 4-8, 2018

1. Innovation and Your Career in Rapidly Changing World of Job Titles:How Different is Data Scientist from What I do? Richelle Serrano Clindata Insight September 4-8, 2018

2. Agenda • Explosion of the Term “Data Scientist” • Short History of Data Science – Highlights • Job Market : Data Scientist and Statistical Programmer • Job Differences and Similarities • Comparisons Data Scientist vs Statistical Programmer • Pharma Domain Expertise • Data Science Initiatives • How to Stay Current in Today’s Job Market WUSS 2018

3. Explosion of the term “Data Science” Why? • The amount of data has skyrocketed • Data-driven decisions are more profitable • Machine learning has changed how business is conducted WUSS 2018

4. Short History of Data Science Articles Abound & Continue… JISC Publishes Harvard Business Review Publishes Dataology & Data Science Research Center Established final report, “The Skills, Role & Career Structure of Data Scientists & Curators: Assessment of Current Practice & Future Needs. Members of the International Federation of Classification Societies (IFCS) include the term data science in conference title Numerous Data Science Articles Why the Term ‘Data Science’ is Flawed but Useful - Pete Warden ‘Data Science’: What's in a name? - David Smith Data Science, Moore’s Law, and Moneyball - Harlan Harris International Association for Statistical Computing (IASC) is Formed Peter Naur’sConcise Survey of Computer Methods is Published Explosion of Data Science Talk Tom Davenport and D.J. Patil’s “Data Scientist: The Sexiest Job of the 21st Century” National Science Board publishes What is Data Science? A Taxonomy of Data Science The Data Science Venn Diagram “Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century.” 2001 2012 2009 2007 2010 1974 1977 1996 William S. Cleveland’s Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics 1970’s 1990’s 2000 1960’s 1980’s 2010 65 90 18 75 80 85 70 00 10 05 15 95 Today WUSS 2018 2011 1962 2008 2005 2003 1997 1977 1989 John W Tukey’s Exploratory Data Analysis is Published Prof. C. F. Jeff Wu Calls for statistics to be renamed data science and statisticians to be renamed data scientists Launch of Data Science Journals First Knowledge Discovery in Databases (KDD) Workshop hosted The Future of Data Analysis written by John Tukey, published in The Annals of Mathematical Statistics WUSS 2018

5. Job ComparisonData Scientist vs Statistical Programmer Statistical Programmer vs Data Scientist All Industries: 1000:16,000 WUSS 2018

6. Pharmaceutical Programmers have industry specific knowledge and domain expertise • CDISC Standards • Pharma programmer needs ability to implement CDISC SDTM and ADaM data standards. • Statistical Methods For Clinical Trials • Therapeutic Area Knowledge • Regulated Environment • Pharma Programmer understands a regulated environment, knows and follows GCP and ICH guidelines. Data scientist in other disciplines may face much less regulation. Pharma Programmer must apply the right statistical models for clinical trial data to study design, data collection, and analysis. • Data Scientist is tasked with building predictive models and developing machine learning capabilities to analyze data. Vs. WUSS 2018

7. Comparison of Job Descriptions Statistical Programmer Data Scientist Designs, develops, modifies programs to analyze & evaluate clinical trial data. Create study specific and ad hoc listings summary tables figures. Select and apply a computational approach to advance the product development. Identify data that supports multi-functional groups progress and objectives. Summary • Statistical programming expertise (SAS, R) • CDISC hands on experience • Study lifecycle from Protocol to TFL • Therapeutic area expertise • Regulatory support, ISS/ISE data integration, reports, SDRG and ADRG Manage and audit programming documentation • Communicates with Biometrics groups, CRO’s, Data Managers, Medical Affairs and statisticians. • Apply variety of analytics to identify trends or insights from multiple data sources/streams. • Knowledge of many prog. languages and technologies, less of any expert in any one. • Construct prototypes of analytics workflows. • Design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, custom analysis. • Communicates to multiple functional groups across organization. Skills Software SAS, R Programming, SAS datasets, CSV files Spotfire used more Python, R, Tensorflow, Tableau used more BS or MS in Computer Science, Mathematics, Statistics MS or PhD in Mathematics, Statistics, Data Science Edu. • Work in Predictive models • Use of machine learning or AI to automate process for Business Analytics and other business or functional groups • Ways to make use of streaming data (wearables), where the amount of data is enormous, taking a record every second. • Customize the data to produce an output which displays the behavior of the data.  • Contribute to data standards creation, management, and governance. • Systems development - macros and utility programs, evaluation and introduction of new technologies including visualization • Resourcing and Project Management CurrentInitiatives WUSS 2018

8. How to Stay Relevant R O C K E T S - Real-World Evidence to enrich the understanding of data - Open mind to open source (R, Python, etc.) and new technologies (Tableau, Spotfire, ggplot2, shiny, etc.). - Structured and Unstructured Data; propose solutions to handle difficult and new types of data - Communication. The ability to share findings in ways the organization can understand and act on. - Knowledge; take courses, go to workshops, or study statistics on your own - Engage in the full lifecycle of the study - Therapeutic area expertise cannot be replaced WUSS 2018

9. CONCLUSION • Data Scientist roles are quite broad and job role is applied differently across industries. • Data Science business initiatives are focused on • Development of Predictive Models • Use of Machine Learning or AI to Automate Process • The Challenges Ahead to Make Use of Streaming Data and Turning That into an Endpoint • Statistical Programmers specialized work and industry specific knowledge cannot be replaced. • Rockets to Relevance -embrace todays challenges in data analysis and continue to build your domain and industry expertise. WUSS 2018

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