Blog

Topics


Data science

How to enter data science

  1. The target
  2. The statistics
  3. The analytics
  4. The engineering
  5. The people

Careers

  1. Transitioning to data science from academia
  2. Lessons from the first two data scientists at a startup

Statistics

  1. Visualizing the danger of multiple t-test comparisons
  2. Linear regression via gradient descent

Theory

  1. Fish schools as ensemble learning algorithms

External

  1. Data quality at Aquicore
  2. How we created the morning overshoot detector

Projects

Building a full-stack spam catching app

  1. Context
  2. Backend
  3. Frontend & Deployment

Data engineering

  1. SQL vs. NoSQL databases in Python
  2. 3 levels of technical abstraction when sharing your code
  3. A hands-on demo of big data with Spark

Python

Random Python

  1. Perspectives on Python after R
  2. Efficient type validation for Python functions

R

Introduction to R

  1. Intro to R
  2. Random data and plotting
  3. For loops and random walks
  4. Functions and if statements
  5. The apply functions

Random R

  1. For loops vs. apply - a race in efficiency
  2. How to be fancy with comparisons
  3. Visualizing my daily commute

Academic advice

Grad school life

  1. 1st-year reflections
  2. 2nd-year reflections
  3. 3rd-year reflections
  4. 4th-year reflections
  5. Advice for generals/quals/prelims

Applications and writing

  1. How to get into a biology Ph.D. program
  2. How to get a National Science Foundation Graduate Research Fellowship (NSF-GRFP)
  3. How to get a Gates Cambridge scholarship p.1 (written by Dr. Paul Bergen)
  4. How to get a Gates Cambridge scholarship p.2 (written by Dr. Corina Logan)
  5. How to write the self-contained universe

Tips for undergrads

  1. Retrospective advice for undergrads to make the most of their four years
  2. Why start grad/med/law school right away? There’s a whole world out there

Behind the scenes in research

  1. Behind the scenes for Couzin et al. 2011, Science