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Welcome and Introduction Welcome and Introduction Programming for Statistical Programming for Statistical Science Science Shawn Santo Shawn Santo 1 / 20 1 / 20 Supplementary materials Companion videos Overview 2 / 20 Introduction

  1. Welcome and Introduction Welcome and Introduction Programming for Statistical Programming for Statistical Science Science Shawn Santo Shawn Santo 1 / 20 1 / 20

  2. Supplementary materials Companion videos Overview 2 / 20

  3. Introduction Introduction 3 / 20 3 / 20

  4. Who am I? Shawn Santo shawn.santo@duke.edu Office hours (Zoom link in Sakai) Mondays 8:15pm - 9:15pm Fridays 11:30am - 12:30pm All times listed are in Eastern Time. 4 / 20

  5. Who else is involved? Federico Ferrari federico.ferrari@duke.edu Office hours (Zoom link in Sakai) Tuesdays 4:00 - 5:00pm Quinn Frank quinn.frank@duke.edu Office hours (Zoom link in Sakai) Tuesdays 1:30pm - 2:30pm Thursdays 1:30pm - 2:30pm All times listed are in Eastern Time. 5 / 20

  6. What is statistical programming? Source: Deborah Nolan & Duncan Temple Lang (2010) Computing in the Statistics Curricula, The American Statistician, 64:2, 97-107, DOI: 10.1198/tast.2010.09132 6 / 20

  7. Some topic covered Fundamentals of R Data types and functions S3 OO system Parallelization Data visualization with package Version control with git and GitHub ggplot2 Shell Package tidyverse Reproducible reports with R Markdown Web scraping Debugging and testing Web based applications with RShiny Spark Wrangling and managing big data Make Databases, SQL, and NoSQL Full course schedule 7 / 20

  8. Why this class matters Programming Languages: Python and R continue to dominate. The new entry this year was Javascript, which got a respectable 6.8% share. Julia share has increased, while most other languages have declined. Platform 2019 % share 2018 % share % change Python 65.8% 65.6% 0.2% R Language 46.6% 48.5% -4.0% SQL Language 32.8% 39.6% -17.2% Java 12.4% 15.1% -17.7% Unix shell/awk 7.9% 9.2% -13.4% C/C++ 7.1% 6.8% 3.7% Javascript 6.8% na na Other programming and data languages 5.7% 6.9% -17.1% Scala 3.5% 5.9% -41.0% Julia 1.7% 0.7% 150.4% Perl 1.3% 1.0% 25.2% Lisp 0.4% 0.3% 46.1% Source: https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html 8 / 20

  9. Why this class matters Some 2020 internships: Mayo Clinic : Interns will work with statisticians, bioinformaticists, and clinical investigators on research projects in areas such as clinical trials, statistical genetics, and bioinformatics. Experience with SAS and/or R preferred. Netflix (Science and Analytics): Comfortable coding in at least one language (e.g., R, Python, Java, Scala, C++), experience preferred with version control (e.g., git), great communication skills, both oral and written. Two Sigma: Use the scientific method to develop sophisticated investment models and shape our insights into how the markets will behave. Create and test complex investment ideas and partner with our engineers to test your theories. You should possess the following qualifications: Demonstrate intermediate skills in at least one programming language, performed an in-depth research project, examining real-world data, are an independent thinker who can creatively approach data analysis and communicate complex ideas clearly. Source: https://stattrak.amstat.org/2019/12/01/2020-internship-listings/ 9 / 20

  10. Course essentials Course essentials 10 / 20 10 / 20

  11. Text toolkit These are recommended textbooks - all are available for free online . There is no required textbook for this course. 11 / 20

  12. Software toolkit 12 / 20

  13. Course structure This class is about you doing as opposed to you just watching or listening. In-person and Zoom, lectures and labs will be interactive. My role as instructor is to introduce you to new tools and techniques, but it is up to you to take them and make use of them. If you only read the code and never run it or experiment with it, then you will not get much out of this course. Most slides will include supplemental resources for you to delve deeper into the topic of discussion. Occasionally, there will be pre-class readings in order to enrich our lecture and lab experiences. 13 / 20

  14. Grading Grade Item Percentage Homework 45% Exam 1 20% Exam 2 20% Project 15% The exact ranges for letter grades may be curved and cutoffs will be determined at the end of the semester. However, if you have a cumulative numerical average of 90 - 100, you are guaranteed at least an A-, 80 - 89 at least a B-, 70 - 79 at least a C-, and so on. 14 / 20

  15. Teams I will initially construct teams based on the first-day class survey (link available later in the slides). Team expectations: Each member must commit to giving equal effort. Each member must read, run, and understand all code in a final submission. Each member must honestly complete the intragroup peer evaluation. 15 / 20

  16. Policies - sharing / reusing code Similar reproducible examples (reprex) exist online that will help you answer many of the questions posed on labs and homework assignments. Use of these resources is allowed unless it is written explicitly on the assignment. You must always cite any code you copy or use as inspiration. Copied code without citation is plagiarism and will result in a 0 for the assignment. There may also be additional punitive measures taken depending on the severity of plagiarism. Copying and citing a large amount of code to satisfy a main objective of an assignment will result in a 0 for the assignment. Discussion (not code sharing / copying) with other students and groups is allowed unless stated otherwise on an assignment. Carefully read each assignment so you know what is permitted and what is not. If you are ever unsure about what is allowed, please ask myself or one of the TAs. 16 / 20

  17. Getting help Post your content and course related questions on Slack. Attend office hours. Set up a Zoom meeting with me or one of the TAs. Email me or one of the TAs. 17 / 20

  18. Links to bookmark Course page: http://www2.stat.duke.edu/courses/Fall20/sta523/ GitHub organization: https://github.com/sta523-fa20 I'll send out an invite to join once I have everyone's user name DSS RStudio servers: to access, first connect to Duke's network via VPN. Navigate to one of: rook - http://rook.stat.duke.edu:8787/ knight - http://knight.stat.duke.edu:8787/ 18 / 20

  19. To do list Before Wednesday's lecture please complete the following (in order). 1. Create a GitHub account. https://github.com/join 2. Join the course's Slack workspace. link was sent in the welcome email on 08-14-20 3. Verify you can log-in to the Department's RStudio Pro servers. 4. Complete the first-day survey. posted in Slack under #general 5. Download the most up-to-date versions of R and RStudio locally. 19 / 20

  20. References 1. Deborah Nolan & Duncan Temple Lang (2010) Computing in the Statistics Curricula, The American Statistician, 64:2, 97-107, DOI: 10.1198/tast.2010.09132 2. Piatetsky, G. (2019). Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis. Kdnuggets.com. Retrieved from https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning- platforms.html 20 / 20

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