12/20/18 12/20/18

Data Moved Me in 2018

Dear diary,

I'm writing this article so that a year from now when I've completely forgotten how cool 2018 was, I can look back on this post. I'm literally floored by all that transpired this year, here is a small snapshot in chronological-ish order:

I started a new position in January 2018 as a Senior Data Scientist at Constant Contact. I've been fortunate to work on interesting projects throughout the year that have often served as inspiration for blog posts.

I launched my first blog article (ever) in March of 2018. This was originally on the domain kristenkehrer.com which is no longer live. This first blog article was rejected by Towards Data Science on Medium. My 2nd blog article was accepted, and now I cross-post most of my articles on TDS. (I've said this before, but if you're blogging and you get rejected, just keep coming back ;)

I spoke on a panel at Hult International Business School on how to get into data science.

I launched datamovesme.com in July after banging my head against the wall trying to figure out Wordpress. I made this move because I knew I'd like to eventually launch a course on my own hosted site and the website builder I was using for kristenkehrer.com would not allow me to do that. In addition, my previous website was never going to rank for SEO.

I spoke with Mike Delgado at Experian on the DataTalk Podcast. So many laughs, fun, and data science in this episode, give it a listen :)

In the end of August I launched my first ever online course "Up-Level Your Data Science Resume." It has helped so many people effectively market themselves and land jobs in data science positions. When people email me to tell me that they have found a job it literally brightens my week.

I was invited to join the YouTube channel Data Science Office Hours with Sarah Nooravi, Eric Weber, Tarry Singh, Kate Strachnyi, Favio Vazquez, Andreas Kretz and newly added Matt Dancho. It's given me the opportunity to create friendships with these wonderful and intelligent people who are all giving back to the community. I want to give a special shout out to Mohamed Mokhtar for creating wonderful posters for office hours. You can check out previous episodes on the Data Science Office Hours YouTube channel (link above).

August 22nd was Favio Vazquez and I launched Data Science Live. We've had incredible guests, take questions from the community, and generally just talk about important topics in data science in industry. We already have some amazing guests planned for 2019 that I cannot wait to hear their perspective and learn from them.

I spoke at Data Science Go in October and had the time of my life. It was basically the king of data parties. I'm grateful to Kirill Eremenko and his team for giving me the opportunity. My talk was around how to effectively communicate complex model output to stakeholders. I went through 4 case studies and demonstrated how I've evolved through time to position myself as a though partner with stakeholders. I also had the opportunity to speak on a panel discussing women in data and diversity. I love sharing my experience as a woman in data and also how I'm able to be an ally and advocate for those who aren't always heard at work.

I was also on the SuperDataScience Podcast in November. Getting to chat 1-on-1 with Kirill was fantastic. He has great energy and was a joy to speak with.

In November I was #8 LinkedIn Top Voices 2018 in Data Science and Analytics. That still seems a little surreal. Then in December LinkedIn sent me a gift after I wrote an article about the wonderful data science community on LinkedIn. That's also pretty nuts.

I picked up a part-time job as a Teaching Assistant for an Applied Data Science online course through Emeritus. Being at DSGO made me think of how I'm contributing to the community, and having the opportunity to help students learn data science has given me extra purpose while helping to keep my skills sharp. It's really a win all around.

It's been a jam-packed year and at times a little hectic between the 9-5, my two young children, and all the fun data science related activities I've participated in. Luckily I have a husband who is so supportive; all of these extracurricular activities wouldn't be possible without him.

Looking to 2019:

I've set some big goals for myself and already have a number of conferences I'll be speaking at in the calendar. I can't wait to share some of these exciting new ventures in the New Year. I wish you a wonderful holiday and can't wait to see and engage with you in 2019.

10/21/18 10/21/18

Life Changing Moments of DataScienceGO 2018

DataScienceGO is truly a unique conference. Justin Fortier summed up part of the ambiance when replying to Sarah Nooravi's LinkedIn post.And although I enjoy a good dance party (more than most), there were a number of reasons why this conference (in particular) was so memorable.

Community
Yoga + Dancing + Music + Fantastic Energy
Thought provoking keynotes (saving the most life changing for last)

Community:In Kirill's keynotes he mentioned that "community is king". I've always truly subscribed to this thought, but DataScienceGO brought this to life. I met amazing people, some people that I had been building relationships for months online but hadn't yet had the opportunity to meet in person, some people I connected with that I had never heard of. EVERYONE was friendly. I mean it, I didn't encounter a single person that was not friendly. I don't want to speak for others, but I got the sense that people had an easier time meeting new people than what I have seen at previous conferences. It really was a community feeling. Lots of pictures, tons of laughs, and plenty of nerdy conversation to be had.If you're new to data science but have been self conscious about being active in the community, I urge you to put yourself out there. You'll be pleasantly surprised.Yoga + Dancing + Music + Fantastic EnergyBoth Saturday and Sunday morning I attended yoga at 7am. To be fully transparent, I have a 4 year old and a 1 year old at home. I thought I was going to use this weekend as an opportunity to sleep a bit. I went home more tired than I had arrived. Positive, energized, and full of gratitude, but exhausted.Have you ever participated in morning yoga with 20-30 data scientists? If you haven't, I highly recommend it.It was an incredible way to start to the day, Jacqueline Jai brought the perfect mix of yoga and humor for a group of data scientists. After yoga each morning you'd go to the opening keynote of the day. This would start off with dance music, lights, sometimes the fog machine, and a bunch of dancing data scientists. My kind of party.The energized start mixed with the message of community really set the pace for a memorable experience.Thought provoking keynotes Ben Taylor spoke about "Leaving an AI Legacy", Pablos Holman spoke about actual inventions that are saving human lives, and Tarry Singh showed the overwhelming (and exciting) breadth of models and applications in deep learning. Since the conference I have taken a step back and have been thinking about where my career will go from here. In addition, Kirill encouraged us to think of a goal and to start taking small actions towards that goal starting today.I haven't nailed down yet how I will have a greater impact, but I have some ideas (and I've started taking action). It may be in the form of becoming an adjunct professor to educate the next wave of future mathematicians and data scientists. Or I hope to have the opportunity to participate in research that will aid in helping to solve some of the world's problems and make someone's life better.I started thinking about my impact (or using modeling for the forces of good) a couple weeks ago when I was talking with Cathy O'Neil for the book I'm writing with Kate Strachnyi "Mothers of Data Science". Cathy is pretty great at making you think about what you're doing with your life, and this could be it's own blog article. But attending DSGO was the icing on the cake in terms of forcing me to consider the impact I'm making.Basically, the take away that I'm trying to express is that this conference pushed me to think about what I'm currently doing, and to think about what I can do in the future to help others. Community is king in more ways than one.ClosingI honestly left the conference with a couple tears. Happy tears, probably provoked a bit by being so overtired. There were so many amazing speakers in addition to the keynotes. I particularly enjoyed being on the Women's panel with Gabriela de Queiroz, Sarah Nooravi, Page Piccinini, and Paige Bailey talking about our real life experiences as data scientists in a male dominated field and about the need for diversity in business in general. I love being able to connect with other women who share a similar bond and passion.I was incredibly humbled to have the opportunity to speak at this conference and also cheer for the talks of some of my friends: Rico Meinl, Randy Lao, Tarry Singh, Matt Dancho and other fantastic speakers. I spoke about how to effectively present your model output to stakeholders, similar to the information that I covered in this blog article: Effective Data Science Presentations This article is obviously an over simplification of all of the awesomeness that happened during the weekend. But if you missed the conference, I hope this motivates you to attend next year so that we can meet. And I urge you to watch the recordings and reflect on the AI legacy you want to leave behind.I haven't seen the link to the recordings from DataScienceGo yet, but when I find them I'll be sure to link here.

8/1/18 8/1/18

Favorite MOOCs for Data Scientists

I had asked on LinkedIn recently about everyone’s favorite MOOCs in data science. This post started a lot of great discussion around the courses (and course platforms) that people had experience with. Certain courses were mentioned multiple times and were obviously being recommended by the community.Here was the post:Biggest takeaway:

Anything by Kirill Eremenko or Andrew NG were highly regarded and mentioned frequently.

favorite mentions graph for data science

So I decided to revisit this post, and aggregate the information that was being shared so that people who are looking for great courses to build their data science toolkit can use this post as a starting point.You’ll notice that below Coursera had the most mentions, this is mostly driven by Andrew Ng’s Machine learning course (11 mentions for that course alone) and Python For Everybody (6 mentions, also on Coursera). Similarly, Kirill has a number of courses on Udemy that all had multiple mentions, giving Udemy a high number of mentions in the comments as well. (Links to courses are lower in this article).The 2 blanks were due to one specific course. “Statistical Learning in R” it is a Stanford course. Unfortunately I wasn’t able to find it online. Maybe someone can help out by posting where to find the course in the comments?

Update! Tridib Dutta and Sviatoslav Zimine reached out within minutes of this article going live to share the link for the Stanford Course. There was also an Edx course that was recommended that is not currently available, “Learning From Data (Introductory Machine Learning)" so I won’t be linking to that one.

If you’re familiar with MOOCs, a number of platforms allow you to audit the course (i.e. watch the videos and read the materials for free) so definitely check into that option if you’re not worried about getting graded on your quizzes.To make the list, a course had to be recommended by at least 2 people (with the exception of courses covering SQL and foundational math for machine learning, since those didn’t have a lot of mentions, but the topics are pretty critical :).I've organized links to the courses that were mentioned by topic. Descriptions of courses are included when they were conveniently located on the website.

Disclaimer: Some of these links are affiliate links, meaning that at no cost to you, I’ll receive a commission if you buy the course.

SQL:

“Sabermetrics 101: Introduction to Baseball Analytics — Edx”“An introduction to sabermetrics, baseball analytics, data science, the R Language, and SQL.”
“Data Foundations” — Udacity“Start building your data skills today by learning to manipulate, analyze, and visualize data with Excel, SQL, and Tableau.”

Math:

“Mathematics for Machine Learning Specialization” — Coursera“Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning.”

Tableau:

“Tableau 10 A-Z: Hands-On Tableau Training for Data Science!” — Udemy (This is a Kirill Eremenko course)

R:

“R Programming” — Coursera “The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.”
"R Programming A-Z™: R For Data Science With Real Exercises!" — Udemy (This is a Kirill Eremenko course)"Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2"

If you're looking for the best R course that has ever existed, read about my favorite R programming course. I wouldn't call it a MOOC, because you have direct access to the instructor through Slack. But if you're serious about learning R, check this out. Link

Python:

“Python for Everybody Specialization” — Coursera“will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language.”
“Learn Python” — CodeAcademy

Python for Data Science:

“Applied Data Science With Python Specialization” — Coursera
“Python for Data Science” — Edx “Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets.”

Machine Learning:

“Machine Learning” — Coursera (This is an Andrew Ng course)
“Machine Learning A-Z™: Hands-On Python & R In Data Science” — Udemy (This is a Kirill Eremenko course)
“Python for Data Science and Machine Learning Bootcamp”— Udemy “Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!”

Deep Learning:

“Deep Learning Specialization” — Coursera (This is an Andrew Ng course)" In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.”No one had anything bad to say about any particular course, however, some people did have preferences in terms of platforms. You can read the original post yourself here.I hope these courses help you widdle down the plethora of options (it’s overwhelming!) and I hope you learn some great new information that you can apply in your career. Happy learning!