In this first episode of "Season 2" of Becoming a Data Scientist podcast, we meet Jasmine Dumas, a new data scientist who tells us about going from biomedical engineering into a data science project experience and then finding her first job as a data scientist.
Podcast Audio Links: Link to podcast Episode 14 audio Podcast's RSS feed for podcast subscription apps]]>
In this first episode of “Season 2” of Becoming a Data Scientist podcast, we meet Jasmine Dumas, a new data scientist who tells us about going from biomedical engineering into a data science project experience and then finding her first job as a data scientist.
Podcast Audio Links:
Link to podcast Episode 14 audio
Podcast’s RSS feed for podcast subscription apps
Podcast on Stitcher
Podcast on iTunes
Podcast Video Playlist:
Youtube playlist of interview videos
More about the Data Science Learning Club:
Data Science Learning Club Welcome Message
Activity 14: Hidden Markov Models
Activity 15: Neural Nets for Text
Data Science Learning Club Meet & Greet
Mentioned in the episode:
Hadley Wickham’s Advanced R book
shinyGEO: a web-based application for analyzing gene expression omnibus datasets
]]>In this episode, Renee interviews Bioinformatics PhD and Data Scientist Erin Shellman about her path to becoming a data scientist, including jobs at Nordstrom Innovation Lab and zymergen. Erin discusses school, job interviews, teaching, and eventually getting to do data science within her field of scientific expertise.
Podcast Audio Links:
Link to podcast Episode 6 audio
Podcast’s RSS feed for podcast subscription apps
Podcast on Stitcher
Podcast on iTunes
Podcast Video Playlist:
Youtube playlist of interview videos
More about the Data Science Learning Club:
Data Science Learning Club Welcome Message
Learning Club Activity 6: k-Means Clustering [coming soon]
Data Science Learning Club Meet & Greet
Bioinformatics
Evolutionary Biology
Economics Game Theory
Machine Learning
Biostatistics
Information Science
Systems Biology
Systems Modeling
Comparative Genomics
Nordstrom Innovation Lab (old innovation lab links inactive – appears to be the Nordstrom Technology People Lab now)
Jim Vallandingham (d3)
Crushed It! Landing a Data Science Job
University of Michigan Computational Medicine and Bioinformatics
R
dplyr
ggvis
ggvis interactive controls
ggplot2
R Markdown
Hadley Wickham
Elements of Statistical Learning book
BI Tech CP303 (course Erin taught at University of Washington – use arrow keys to go through slides)
GitHub repository for class
regression
classification – logistic regression, trees
market basket analysis
clustering
UW Business Intelligence Certification
The first activity involved setting up a development environment. Some people are using R, some using python, and there are several different development tools represented. In this thread, several people posted what setup they were using. I posted a “hello world” program and the code to output the package versions.
Activities 1-3 built upon one another to explore a dataset and generate descriptive statistics and visuals, culminating with a business Q&A:
- Activity 1 – Find & Explore a Dataset
- Activity 2 – Visuals for Exploratory Data Analysis
- Activity 3 – Business Questions & Data Answers
I analyzed a subset of data from the eBird bird observation dataset from Cornell Ornithology for these activities. Some highlights included:
– Learning how to use the pandas python package to explore a dataset (code)
– Learning how to create cool exploratory visuals in Seaborn and Tableau. Here is an example scatterplot matrix made in Seaborn:
– I was most excited to learn how to build interactive Jupyter Notebook inputs, which I used to control Bokeh data visualizations to display Ruby-Throated Hummingbird migration into North America (notebook). Unfortunately, until I host them on a server where you can run the “live” version, you won’t be able to see the interactive widgets (a slider and dynamic dropdowns), but you can see a video of the slider working here:
Here’s my final output for Activity 3, a Jupyter Notebook (with code hidden, and unfortunately interactive widgets disabled) with the Q&A about the hummingbird migration:
Ruby-Throated Hummingbird Migration into North America
Activity 4 was built as a catch-up week for those of us who were behind, but had some ideas of math concepts to learn for those who had time.
We’re currently working on Activity 5, our first machine learning activity where we’re implementing Naive Bayes Classification.
All of my work is available in this github repository: https://github.com/paix120/DataScienceLearningClubActivities
I strongly encourage you to click through the forums and look at some of the other data explorations the members have been doing, including analysis of NFL data, personal music listening habits, transportation in London, German Soccer League data, top-grossing movies, and more!
It’s never too late to join the Data Science Learning Club! If you aren’t sure where to start, check out the welcome message for some clarification.
I’ll post again when I complete some of the machine learning activities!
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