Tuesday, April 5, 2016

Techies Project

Image Credit: Helena Price, Techies Project

I participated in an interview with Helena Price for her Techies Project.  You can read the full interview here:


I talk about being a women in astrophysics / tech, a person with a disability, and a Bay Area local who has watched her home change dramatically due to the tech industry.

Helena will be tweeting highlights of the 100 interviews.  Follow the project on Twitter, Instagram, and Facebook.

Sunday, April 19, 2015

What is a Data Scientist?

Data Scientist has been called the Sexiest Job of the 21st Century... but many people (including some companies trying to hire data scientists) don't really understand what this job means.  The term is used to describe a wide variety of roles; A data scientist at one company doesn't necessarily do the same thing as a data scientist at another company.

Below I break down some of the different 'types' of data scientist jobs there are and the skills needed for these various roles.  Please note that this list is not exhaustive, and sometimes a data science position expects someone to fill multiple of the below roles:

Data Analyst
  • Derive business insight from data. 
  • Work across all teams within an organization. 
  • Answer questions using analysis of data.
  • Design and perform experiments and tests. 
  • Create forecasts and models.  
  • Prioritize which questions and analyses are actionable and valuable.
  • Help teams/executives make data-driven decisions.
  • Communicate results across the company to technical and non-technical people.
Required Skills: SQL, Statistics, programming, data management, data analysis, data modeling, data visualization, experimental design, decision making, prioritization, project management, product development, communication. 

Data Architect
  • Design systems to get raw data into an easily analyzable form.  
  • Act as a bridge between engineers and analysts. 
  • Organize data into useful database tables for analysis.
  • Optimize data sets for efficient analysis.
  • Create ETL Systems for your data sets. 
Required Skills: SQL, computer programming, backend software engineering, database design, database management, data optimization, data modeling.

Data Engineer
  • Work with analysts to build internal tools for analyzing, visualizing, and sharing data. 
  • Design and maintain A/B testing systems.
  • Work with engineers to insure that the right data is being collected.
  • Create systems which allow analyst work to scale.
  • Work with data architects / operations to insure the data is organized for optimal analysis.
Required Skills: SQL, computer programming, full-stack software engineering, data visualization, database management, communication.

Domain Experts
People who have advanced specializations like:
Required Skills: Advanced degree in computer science, math, statistics, finance, or economics. Programming, statistics, data management, data analysis, data modeling, data visualization, experimental design, communication.

Someone who does all of the above.
(basically impossible to find, thus the name)

Some FAQs about Data Science Jobs

Q: Do I need an advanced degree to be a data scientist?
A: No
In general, the tech industry cares very little about degrees or pedigree.  I have been at companies where the CTO didn't graduate high school, and I have been at companies where many people have PhDs.  I would be very surprised if any company would not consider an (otherwise qualified) candidate for a data role just because they didn't have a certain degree.

That being said, many of the skills required to be a data scientist overlap with the skills required to be a scientific researcher.  People who have worked as researchers (either because they did an advanced degree, or because they worked in a lab) tend to be good candidates for data science jobs.

However, you can get this experience in a lot of other ways.  For instance, many people start out as (junior) analysts where they pick up many of the skills needed to be data scientists and then become data scientists after 3-5 years of industry experience.  Some people start out as software developers and work on more and more data-oriented projects and get into data science through engineering. Other people start out as financial, marketing, or business analysts and become data scientists through that path.

If you want to be a data scientist, I would look at the skills required for the above roles and then find ways to develop those skills either through schooling, your current job, or self-directed projects.

Q: What are the most important skills needed to be a data scientist?
A: SQL, Data Analysis, Programming
Of course, this depends on exactly what is expected for a particular role.  Some data science roles are more analyst-oriented, some are more engineering-oriented, some are more specialized.  This is something you can assess from the job description, but should also be discussed during the interview process.

However, in general I would say the most important skills are the following:
1) SQL
2) Data Analysis
3) Statistical Programming

So if you were going to learn one thing, I would say learn SQL.  Then do a project that involves analyzing a data set and deriving results.  Then learn a programming language like Python or R.

Q:  How do I develop these skills?
A: Online course, hackathons, meet-ups, volunteer organizations.
There are a lot of great online resources which can help you develop these skills.

Wednesday, February 4, 2015

So You Want to Be a Data Scientist?

People reach out to me a lot asking how I got into data science and wanting advice about breaking into the field.  Fortunately this is something I have written and talked about quite a bit!

Below is a compilation of the various things I have "out there" about my transition from academia to data scientist and what it's like to be a data scientist:

What is a Data Scientist? - General overview the answers to some frequently asked questions.

Transitioning Advice / Information
Astronomer to Data Scientist - Advice on how to make the transition from academia to data scientist.
Astronomer to Data Scientist (Talk) - More advice, and some details about what I do for my job.
Astronomy vs. Data Science - Compares and contrasts academia and the tech industry.
Nailing the Data Science Interview - Advice for preparing for your interview and what to expect.
Astronomy to Data Science, Three Years Later - An update on my transition.
Interview with Lady Paragons - What my job is like, what I do, some other stuff too.
Podcast with Lady Paragons - More about what it is like to be a data scientist.
Interview with AAS on Women in Astronomy - More about my transition and what my job it like.

Recruiting Advice
Interview with HP - Advice on Where to Find Data Scientists:

Do you have more questions? Ask them below!

Thursday, January 29, 2015

Astrophysicist to Data Scientist (Talk Slides)

Here are the slides from the Astrophysicist to Data Scientist talk I gave (among other places) at AAS 225 this January. Please follow me on twitter for more information about data science! This talk was part of a special session by the Working Group on Astroinformatics and Astrostatistics.

Wednesday, November 12, 2014

The Best Part of My Week

Two years ago I made the transition from academic science to data science. There are many aspects of industry that mesh better with my working style. However one very important industry practice that I feel is lacking in academia (at least for many of the people I have spoken to) are mechanisms for regular evaluation and feedback  especially for graduate students and postdocs.

Lately I've been facilitating workshops on the Impostor Syndrome and having many conversations with people about my process of dealing with and overcoming my own impostor feelings. For me a huge problem with my experience in graduate school was a constant nagging fear that I wasn't performing at an adequate level. There are so few metrics by which to measure success; if I didn't published N papers, make any major discoveries, or win any prizes or grants  how was I to know if I was ‘cutting it?’ And even if I did accomplish some of these milestones, there were always stories of other people who did it more, better, and faster. This was the perfect breeding ground for my impostor thoughts.

Read the full post at Women in Astronomy.

Science Behind Interstellar Explained

If you’ve recently been to the movie theaters to see Christopher Nolan’s latest film Interstellar, you may have left the movie like “OMG,” but possibly also like “WTF?” Through the film’s storyline, the audience is introduced to a number of captivating yet complicated topics that former NASA pilot Cooper (Matthew McConaughey) and his team must tackle on their quest into unknown regions of space in order to save mankind.
To help demystify a few astrophysics-specific topics discussed in the movie such as using wormholes to travel to distant parts of the galaxy, the physics behind alternate dimensions, tidal forces caused by the gravitational pull of black holes, Einstein’s Theory of Relativity, and the physics behind aging at different rates explained by the famous Twin Paradox just to name a few, InstaEDU has teamed up with its own physics, astrophysics, and cosmology tutors to not only help shed light on these topics but to open a dialogue between you, the reader, and our experts who can help explain the answers that you’re looking for.
Here to answer questions about the physics of Interstellar is one of InstaEDU’s “Interstellar” tutors, Jessica K, PhD from University of California, Berkeley and astrophysics tutor on InstaEDU.
Read the full interview on the InstaEDU Blog.

Wednesday, October 22, 2014

An astrophysicist schools us on where to find data scientists

Below is my interview with HP Discover Performance about how to build data science teams.

An astrophysicist schools us on where to find data scientists

It’s hard to find qualified data scientists. Where are they hiding? We found one and asked her.

Yet even as “data scientist” is declared the next “it” career in the IT world, enterprises are struggling to not only define the role, but also find candidates with this unique skill set. Which may be part of the problem. We asked Jessica Kirkpatrick, an astrophysicist who’s now the director of data science at online education company InstaEDU, how to solve the talent shortage, and she said many companies are looking for the wrong skill sets, in the wrong places. The Big Data buzz often focuses on the tools and hardware that crunch your data, but that won’t do much good if you don’t have really smart people interpreting that data to give actionable insight to the business.