curriculum engineering at scale

I've always been an educator and an explainer. In my previous incarnation in academic research, I spent a lot of time teaching statistics, programming and practical data science to researchers at Yale University, New Haven, and the Max Planck Institute for Cell Biology and Genetics, Dresden, where I was a postdoctoral fellow.

I joined DataCamp to teach data science at scale, originally as a Python Curriculum Engineer, charged with building out a curriculum that leveraged modern browser technologies to teach skills to work with data, from machine learning to data visualization, data wrangling and statistics, among many others. The vision has always been Learn By Doing and, to this end, after showing learners short videos by experts in the field, we get them coding as quickly as possible in the browser and provide automated, tailored feedback.

Joining DataCamp as the 7th employee (we're now ~150), I've had the pleasure of playing a pivotal role in the development of our product and curriculum, at a high level, on the technical side of things, and as a community builder to connect the company as a whole to the broader data community. Early, fond memories involve working with Travis Oliphant and Peter Wang of Anaconda to build out core pandas and data visualization courses, working with Andreas Müller (core developer & co-maintainer of scikit-learn) on our Supervised Learning with scikit-learn course, with Katharine Jarmul (kjamistan) on Natural Language Processing in Python and with Justin Bois (Caltech) on several Statistical Thinking in Python courses.

My courses at DataCamp

tutorials/workshops

I love to teach in person. There is no substitute for the electricity generated by throwing around ideas, tools, techniques and data challenges in a room with other humans. And it's something that I miss dearly when I don't do it so I make sure to: I am a Software Carpentry and Data Carpentry Instructor. In Summer, 2018, I had the great pleasure of teaching a Genomics Data Carpentry and Machine Learning in R for the Carpentries at Cold Spring Harbour Laboratory with Jason Williams, where we piloted a Machine Learning in R lesson, which was really well-received (but there's much more work to be done!).

I also regularly teach workshops and tutorials at conferences, such as SciPy and PyCon. You can check out some videos and materials under Talks below.

other initiatives

I'm interested in exploring other ways to teach and discuss data science, machine learning and AI. To this end, I piloted a series of Facebook Live coding sessions at DataCamp, which saw up to 40K unique viewers. Two of my favourites are Getting Started with the Tidyverse through the Titanic data set and Web Scraping & NLP in Python, in which I scrape novels from the web and plot word frequency distributions.

I enjoy writing tutorials. You can find a bunch I've written on DataCamp's community page by searching for my name. Here are a few to get started with:

Groupby, split-apply-combine and pandas Hierarchical indices, groupby and pandas Preprocessing in Data Science (Part 1) Preprocessing in Data Science (Part 2) Preprocessing in Data Science (Part 3)

I'm constantly thinking about how data science notebook technologies can be used to design productive educational environments. You can check out Eric Ma's and my interactive Jupyter notebooks for our Bayesian data science workshops here on Binder (more context in the github repo here). I also built a DataCamp project that leverages the capabilities of Jupyter notebooks to create a novel educational experience: it's called "Word Frequency in Moby Dick" and in it, you'll get to scrape the novel Moby Dick from the website Project Gutenberg (which contains a large corpus of books), extract words from it and dive into analyzing the distribution of words using the Natural Language ToolKit (nltk).

I've given a lot of webinars for business leaders, managers and learning and development leaders across several verticals. Highlights include: What Managers Need To Know About Machine Learning, Inside the Data Science Workflow and Data Literacy in the 21st Century.

What Data Scientists Really Do, According to 50 Data Scientists

This is a talk about what data scientists really do and what they consider to be the most pressing issues in the space. It's a fun talk to give as it's based on 50 conversations that I had on the DataFramed podcast. I have given it several times, including at PyData NYC and the New York Open Statistical Programming Meetup

slides

Bayesian Data Science Two Ways: Simulation and Probabilistic Programming

SciPy 2018 Tutorial

This was a tutorial that I co-taught with Eric Ma to build participants’ knowledge of Bayesian inference, workflows and decision making under uncertainty. We started with the basics of probability via simulation and analysis of real-world datasets, building up to an understanding of Bayes’ theorem. We then introduced the use of probabilistic programming to do statistical modelling. Throughout this tutorial, we used a mixture of instructional time and hands-on time. During instructional time, we used a variety of datasets to anchor our instruction; during hands-on time, which immediately followed instructional time, our participants applied the concepts learned to the Darwin’s finches dataset, which permeated the entire tutorial.

Tutorial material

Bayesian Data Science by Simulation

PyCon 2019 Tutorial

This tutorial was an Introduction to Bayesian data science through the lens of simulation or hacker statistics. Learners became familiar with many common probability distributions through i) matching them to real-world stories & ii) simulating them. They worked with joint/conditional probabilities, Bayes Theorem, prior/posterior distributions and likelihoods, while seeing their applications in real-world data analyses. They then saw the utility of Bayesian inference in parameter estimation and comparing groups and we wrapped up with a dive into the wonderful world of probabilistic programming using PyMC3.

Tutorial material