1. Ethics and Professionalism (Privacy and fairness regulations, professional standards, public policy concerns, or similar topics applicable to data and predictive analytics)
- Review guidelines of relevant professions, such as ASB code of conduct and ASA code of ethics and CPCU code of professional conduct
- Select Regulation and Professionalism track sessions from CAS RPM Seminars
- Reading on data privacy regulations like the GDPR and HIPAA, and the NIST standard on PII
- Reading public policy criticism of the use of data science models
2. Predictive Analytics/Modeling Techniques (Data sources, database architectures, distributed file systems, and related matters)
- Reading relevant articles in research journals such as the Journal of the Royal Statistical Society, the Journal of Computational and Graphical Statistics, the Journal of Statistical Software, ACM Transactions on Knowledge Discovery from Data, Biometrika, Econometrica, etc.
- Select Modeling track sessions from CAS RPM seminars (from 2018 the following are good examples: M-4, M-5, M-8, M-10, M-11, M-12, M-13, M-15, M-16, M-17)
- Participate in predictive modeling competitions using a technique that is new to you, or applying a familiar technique in a new way
- Modeling work within your job that involves using a technique that is new to you, or applying a familiar technique in a new way. (Only count the units spent learning, not working.)
- Reading R vignettes or books to learn new data science / statistical techniques
- Attending relevant talks at conferences such as JSM, KDD, UseR!, StanCon, the SIAM Data Mining conference, the Conference on Statistical Practice, the Data Science Conference, Advanced Research Techniques Forum of the American Marketing Association, conferences on natural language processing, on image analytics, etc. Also, local R or Python meetups or local chapter ASA meetings with relevant educational talks
- Coursera and similar courses on relevant topics
3. Data Management (Machine learning and other statistical modeling approaches and related techniques; also survey design and experimental design)
- Data focused session from Modeling track and Product Management Track sessions at CAS RPM Seminars (from 2018 the following are examples: M-6, PM-1, PM-2, PM-3, PM-6)
- Conferences and literature sponsored by ACM-MOD/PODS
4. Industry Knowledge (P&C) (Learning more about the business the CSPA is applying data science to, or could apply data science to in the future)
- CAS, CPCU, and PLUS conferences. Insuretech and telematics conferences. Claims and Risk management conferences. Also conferences and literature specific to the sector you are involved in insuring
- Reading material from Best’s Review, Coverage Opinions (https://www.coverageopinions.info/), the other industry periodicals, for P&C and/or for the industry you are involved in insuring
- Reading Material from Risk Management and Insurance Review
5. Business oriented skills (Enhancing soft skills of the CSPA)
- Training on public speaking, giving presentations, project management, expert witness testimony, sales skills, etc.
6. Other (Learning relevant data science skills not included in any of the above categories. For example, learning a programing language such as Python or C++, or analyzing the data science services offered by various vendors.)
- Learning relevant programming languages, such as R, Python, Julia, Scala, C, C++, Fortran, Java, or learning relevant tools or toolchains such as version control software or Could be learning from a book, a coursera course, etc.