Checklist on your resume

A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.

  • Linux scripting
  • Git/GitHub (give your GitHub handle)
  • Tidyverse
  • Data visualization (ggplot2)
  • Frontend development (shiny, web app)
  • Databases, SQL (Google BigQuery)
  • High-performance computing (HPC) on cluster (if you use Hoffman2)
  • Cloud computing (GCP)
  • Deep learning with Keras+TensorFlow (PyTorch and Caffe are friendlier for research)

  • Rcpp, parallel computing, GPGPU (next week)

  • Make your GitHub repo biostat-203b-2022-winter public (after Mar 18) and show your work to back your resume. Feel free to modify the reports even after this course. You can make your GitHub repository into a webpage by using GitHub Pages.

  • Use these tools in your daily work: use Git/GitHub for all your homework and research projects, write weekly research report using RMarkdown, give presentation using ggplot2 and Shiny, write blog/tutorial, …

What’s not covered

  • Machine/statistical learning methods. Familiar with methods in Elements of Statistical Learning and software, e.g., scikit-learn.

    For non-statistician/biostatistician, I recommend An Introduction to Statistical Learning: With Applications in R, which is less technical and more application oriented.

  • Computational algorithms. Spring quarter’s Biostat 257 will cover numerical linear algebra and numerical optimization algorithms.

  • Public health applications.

  • Be open to languages. Python is a more generic programming language and widely adopted in data science. JavaScript is dominant in web applications. Scala is popular for implementing distributed programs. Julia is attractive for high performance scientific computing.

Course evaluation

Please do it now.

http://my.ucla.edu

Today

  • HW3.

  • Neural network examples.