Stephanie and John talk about the academic data science portfolios. What are they? How are they different than industry data science portfolios or more traditional academic portfolios? Also check out the bloopers at the end! Some good links about portfolios and examples are: https://davidventuri.com/portfolio, https://www.dataquest.io/blog/build-a-data-science-portfolio/. We discuss data driven curriculum vitae (CV), including the package by Nick Strayer: https://github.com/nstrayer/datadrivencv. Additional tools are the scholar (https://cran.r-project.org/package=scholar), gcite (https://cran.r-project.org/package=gcite), and rscopus (https://cran.r-project.org/package=rscopus) packages. Disclaimer: John wrote gcite and rscopus. We acknowledge the impetus for gcite was from this blog post by Jeff Leek: https://simplystatistics.tumblr.com/post/13203811645/an-r-function-to-analyze-your-google-scholar. See John and Stephanie's CVs at https://johnmuschelli.com/CV and https://www.overleaf.com/read/zkhjvkdbbpvv. Follow us at https://twitter.com/stephaniehicks, https://twitter.com/strictlystat, and https://twitter.com/correspondauth or email us at [email protected].

Stephanie and John talk about what is an "Academic Data Science Portfolio". While there is a lot of great information about what are (i) data science portfolios in industry and (ii) academic portfolios, we aimed to discuss the intersection of these two, why you need one, what goes in it, and so on. Follow us at https://twitter.com/stephaniehicks, https://twitter.com/strictlystat, and https://twitter.com/correspondauth or email us at [email protected].

Stephanie and John talk about the hiring process and outreach of a post-doc. Topics include: funding, cold emails, interviews, and negotiable items. We also discuss difficulties in choosing amongst multiple offers. Conferences discussed ENAR: https://www.enar.org/meetings/ JSM: https://www.amstat.org/ASA/Meetings/Joint-Statistical-Meetings.aspx Discussion of the "ideal worker" and a great insight into feeling overwhelmed with duties outside of work is included in: https://www.amazon.com/Overwhelmed-Work-Love-Play-When/dp/1501209981

Stephanie and John discuss some fun pandemic-changes, like 2 Zoom's at once. John thinks 2 Zooms at once should be a crime. We talk about choosing a post-doc, moving, and the pros and cons of post-docs vs. applying to academic positions vs. industry. We use industry in a large general bucket, though there are many different roles. We talk about how you push on a field of research, similar to the graphic here: http://matt.might.net/articles/phd-school-in-pictures/ Tweet at us at https://twitter.com/strictlystat and https://twitter.com/stephaniehicks and The Corresponding Author: https://twitter.com/correspondauth

Stephanie and John discuss the paper "Documenting and Evaluating Data Science Contributions in Academic Promotion in Departments of Statistics and Biostatistics" by Lance Waller: https://doi.org/10.1080/00031305.2017.1375988 and https://www.biorxiv.org/node/29325.abstract. We discuss the promotion and tenure process and how they may be a bit different for data scientists in academia. We discuss personal statements, your CV, key publications, recommendation letters. We reference the PPM, which is the policy and procedure manual.

Stephanie and John talk about COVID19/SARS-CoV-2 and all that. This episode was a Zoom video (not just audio!) where we discuss the influx of COVID-related funding. Feel free to reach out to us at https://twitter.com/correspondauth, https://twitter.com/stephaniehicks, https://twitter.com/StrictlyStat, or email us at or [email protected]. All podcasts will be available via https://soundcloud.com/the-corresponding-author. Link to "Strict Workflow" (https://chrome.google.com/webstore/detail/strict-workflow/cgmnfnmlficgeijcalkgnnkigkefkbhd?hl=en), which follows the Pomodoro Technique (https://francescocirillo.com/pages/pomodoro-technique).

Stephanie and John talk to (newly minted) Dr. Ben Ackerman (https://twitter.com/backerman150), the day after his dissertation defense! They discuss Ben's research in mental health and leadership in graduate student mental health at Johns Hopkins. We wish him the best in his next endeavor! Send messages to https://twitter.com/correspondauth or [email protected] for questions or requests for new episodes.

Edited and Mixed by Jessica Crowell, with special thanks. John and Stephanie discuss Deep Learning and AI. They try to map out their definitions of machine learning, deep learning, and AI. John discusses his concern with AI and reproducibility, referencing his blog post https://hopstat.wordpress.com/2020/02/04/the-way-people-use-ai-is-ruining-reproducible-science-again/. They reference Geoff Hinton's prediction about Radiology: https://www.youtube.com/watch?v=2HMPRXstSvQ. We also discuss the Anil Potti Duke reproducibility case briefly: https://www.economist.com/science-and-technology/2011/09/10/an-array-of-errors Follow us at https://twitter.com/CorrespondAuth, https://twitter.com/stephaniehicks, and https://twitter.com/strictlystat.

Stephanie and John talk about more about interviewing at academic institutions. They go over study sections, questions to ask the department, and writing accountability groups (WAGs). Send messages to https://twitter.com/correspondauth for questions or requests for new episodes. The book referenced that describes WAGs and how to write a lot is: How to Write a Lot: A Practical Guide to Productive Academic Writing (2018 New Edition) by Paul J. Silvia: Link https://www.amazon.com/dp/1433829738/ref=cm_sw_em_r_mt_dp_U_p.5dEbFCSAA24 Edited and Mixed by Jessica Crowell, with special thanks.

Stephanie and John discuss interviewing at academic institutions. They go over the job talk, researching your interviewers, being excited, and the dinner. Send messages to https://twitter.com/correspondauth for questions or requests for new episodes.

Data Science Jobs: Stephanie and John discuss academic job searches, data science positions, and tenure-track vs not in this episode. See Stephanie's insights on her post here: https://github.com/stephaniehicks/classroomNotes/blob/master/academicJobNotes.md and John's post here: https://hopstat.wordpress.com/2016/10/05/tips-for-job-search/. The reference to the downloads from R packages is located at: https://github.com/muschellij2/CV/blob/master/R_packages.Rnw#L5, which uses the package cranlogs: https://cran.r-project.org/package=cranlogs.