Business Science’s Time Series Course is Incredible

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I’m a time series fan.  Big fan.  My first job out of grad school was for a utility company building econometric time series analysis and forecasting models.  Lots of ARIMAs and neural nets. However, that was now over 10 years ago (don’t know how the hell that happened).

In almost every position I've held in data, a question has come up that involved a time series (not a surprise that business cares about what has happened over time).  Often, I was the only one who had any knowledge of time series on my team.  I'm not sure why it isn't taught as a standard part of most university programs that are training data scientists, but it's just unfortunately not.  I believe that understanding time series analysis is currently a great way to differentiate yourself, since many in the field are just not well versed in it.

I wanted to understand what was current in the world of applying time series analysis to business.  It had been a real long time since I had given the subject some of the love and attention, and I thought taking this Business Science course would be the perfect way to do that.

My History With Business Science Courses:

I’ve previously written about Business Science’s first course, you can check it out here.  I've also taken his first Shiny app course (there’s a more advanced one as well) and went from zero to Shiny app in 2 days using survey data I collected with Kate Strachnyi.  It was a real win.

via GIPHY

The app is still on my site here, just scroll down.  For this little flexdashboard app I went from basically zero Shiny to having something that was useful in 2 days leveraging only the first 25% of the course. The course cannot actually be completed in 2 days. It's also worth noting that the course builds an app with much more functionality than mine. It’s a long course.

Back to the Time Series Review:

It’s broken into three different section:

• Things I freakin’ love

• The sexy

• Everything else

Things I freakin’ love:

You’re learning about packages from the package creator.  Who is going to understand a library better than the person who wrote it?.  Matt built both modeltime and timetk that are used in this course. I find that super impressive.  These packages are also a step up from what was currently out there from a "not needing a million packages to do what I want" perspective.

He uses his own (anonymized) data fromBusiness Science to demonstrate some of the models.  I haven’t seen others do this, and I think it’s cool.  It’s a real, practical dataset of his Google Analytics and Mailchimp email data with an explanation of the fields.  If you don’t have analytics experience in e-commerce and are thinking about taking a role in e-commerce, definitely give some thought to this course.  

I love how in-depth he gets with the subject.  If you follow all that is covered in the course, you should be able to apply time series to your own data. 

The Sexy:

via GIPHY

Ok, so I’m sure some are interested in seeing just how “cutting edge” the course gets. 

Once you're combining deep learning Gluon models and machine learning models using ensembling methods, you might be the coolest kid at work (but I’m not making any promises). Gluon is a package that was created by Amazon in Python. So you’ll leverage both Python and R for Gluon.

Some of the deep learning algorithms you’ll learn how to leverage are:

• DeepAR

• DeepVAR

• N-Beats

• Deep Factor Estimator

Module 18 of the course is where you'll get into deep learning.  A couple years ago I might have said "deep learning, bah humbug, requires too much computing power and isn't necessary, simpler is better."  As things change and progress (and computers get even more beefy) I'm definitely changing my tune.   Especially as an ensemble N-Beats algorithm beat the ES-RNN's score in the M4 competition.  M competitions are prestigious forecasting challenges, and they've historically been won by statistical algorithms.  (I wouldn't have known this information without this course).  The stuff being taught in this course is very current and the sexy new techniques that are winning the big competitions.

Here's a look at the syllabus for preparing the data and learning about the DeepAR model.  You're doing log transformations, Fourier Series, and when you get to modeling the course even covers how to handle errors. I just love it.  I know I'll be referring back to the course when a time series use case pops up in the future.

The course covers 17 different algorithms. I'm trying to think if I could name 17 algorithms off the top of my head…  it’d take me a minute.   ARIMA is obviously included, because It’s like the linear regression of time series.  You’ll go through ARIMA, TBATS (a fave because you don’t need to worry about stationarity the way you do with ARIMA. I’ve used this one in industry as well). 

Along with these other algos:

• ARIMA Boost

• Prophet Boost

• Cubist

• KNN

• MARS

• Seasonal decomposition models

Then you’ve got your ensemble algos being leveraged for time series:

• GLMNET

• Random Forest

• Neural Net

• Cubist

• SVM

Strap in for 8 solid hours of modeling, hyperparameter tuning, visualizing output, cross-validation and stacking!

Everything else:

• Matt (the owner of Business Science) speaks clearly and is easy to understand.  Occasionally I'll put him on 1.25x speed.

• His courses in general spend a good amount of time setting the stage for the course.  Once you start coding, you’ll have a great understanding of where you’re going, goals, and context (and your file management will be top notch), but if you’re itching to put your fingers on the keyboard immediately, you’ll need to calm the ants in your pants. It is a thorough start.

• You have to already feel comfy in R AND the tidyverse. Otherwise you’ll need to get up to speed first and Business Science has a group of courses to help you do that.  You can see what's included here.

Before we finish off this article, one super unique part of the course I enjoyed was where Matt compared the top 4 time series Kaggle competitions and dissected what went into each of the winning models. I found the whole breakdown fascinating, and thought it added wonderful beginning context for the course.

In the 2014 Walmart Challenge, taking into account the “special event” of a shift in holiday sales was what landed 1st place. So you're actually seeing practical use cases for many of the topics taught in the course and this certainly helps with retention of the material.  

Likewise, special events got me good in 2011.  I was modeling and forecasting gas and the actual consumption of gas and number of customers was going through the roof!  Eventually we realized it was that the price of oil had gotten so high that people were converting to gas, but that one tripped me up for a couple months. Thinking about current events is so important in time series analysis and we'll see it time and again.  I've said it before, but Business Science courses are just so practical.

Summary:

If you do take this course, you’ll be prepared to implement time series analysis to time series that you encounter in the real world.  I've always found time series analysis useful at different points in my career, even when the job description did not explicitly call for knowledge of time series. 

As you saw from the prerequisites, you need to already know R for this course.  Luckily, Business Science has created a bundle at a discounted price so that you can both learn R, a whole lot of machine learning, and then dive into time series.  Plus you’ll get an additional 15% off the already discounted price with this link.  If you're already comfortable in R and you're just looking to take the time series course, you can get 15% off of the single course here. 

Edit:  People have asked for a coupon to buy all 5 courses at once.  That's something I'm able to do!  Learn R, machine learning, beginner and advanced Shiny app development and time series here.

My Favorite R Programming Course

Note: This article includes affiliate links. Meaning at no cost to you (actually, you get a discount, score!) I will receive a small commission if you purchase the course.

I've been using R since 2004, long before the Tidyverse was introduced. I knew I'd benefit from fully getting up to speed on the newest packages and functionality, and I finally decided to take the plunge and fully update my skills. I wanted a course that was going to cover every nook and cranny in R. My personal experience learning R had been pasting together tutorials and reading documentation as I needed something. I wanted to be introduced to functions I may not need now, but may have a use case for in the future. I wanted everything.

I've known for a while that the Tidyverse was a massive improvement over using base R functionality for manipulating data. However, I also knew my old school skills got the job done. I have now seen the light. There is a better way. It wasn't super painful to make the move (especially since I'm familiar with programming) and the Business Science's "Business Analysis with R" course will take you from 0 to pretty dangerous in 4 weeks.

For the person with no R experience who doesn't want to bang their head on the wall for years trying to get to a "serious R user" level. I highly recommend this Business Science's "Business Analysis with R" course. Don't let the name fool you, the course spends 5 hours of using the parsnip package for machine learning techniques. And more importantly, how to communicate those results to stakeholders.

The course was thorough, clear, and concise.

Course Coverage

General:

The course takes you from the very beginning:

• Installing R
• Setting up your work environment
  • full disclosure, I even learned new tips and tricks in that section
• and then straight into a relevant business analysis using transactional data

This course "holds your hand" on your journey to becoming self-sufficient in R. I couldn't possibly list everything in this article that is covered in the course, that would make no sense. However, the most life changing for me were:

• regex using stringr
  • Working with strings is a different world in the Tidyverse compared to base R. I can't believe how much more difficult I had been making my life
• working with date times using lubridate
  • The beginning of my career was solely in econometric time series analysis. I could have used this much earlier.
• formatting your visualizations
  • This is another area where I have lost significant hours of my life that I'll never get back through the process of learning R. Matt can save you the pain I suffered.

All of the material that I would have wanted was here. All of it.

Modeling & Creating Deliverables:

Again, do not let the title of the course fool you. This course gets HEAVY into machine learning. Spending 5 HOURS in the parsnip library (it's the scikit learn of R).

The course goes through:

• K-means
• Regression & GLM
• tree methods
• XGBoost
• Support Vector Machines

And then teaches you how to create deliverables in R-markdown and interactive plots in Shiny. All in business context and always emphasizing how you'll "communicate it to the business". I can't stress enough how meticulous the layout of the course is and how much material is covered. This course is truly special.

How many tutorials or trainings have you had over the years where everything looked all "hunky dory" when you were in class? Then you try to adopt those skills and apply them to personal projects and there are huge gaping holes in what you needed to be successful. I have plenty of experience banging my head on the wall trying to get things to work with R.

Things You'll Love:

• Repetition of keyboard short-cuts so that I'll actually remember them.
• Immediately using transactional data to walk through an analysis. You're not only learning R, you're learning the applications and why the functions are relevant, immediately.
• Reference to the popular R cheatsheets and documentation. You'll leave here understanding how to read the documentation and R cheatsheets - and let's be honest, a good portion of being a strong programmer is effective googling skills. Learning to read the documentation is equivalent to teaching a man to fish.
• Matt has a nice voice. There, I said it. If you're going to listen to something for HOURS, I feel like this a relevant point to make.

For the beginner:

• Instruction starts right at the beginning and the instruction is clear.
• Code to follow along with the lecture is crazy well organized. Business Science obviously prides itself on structure.
• There is no need to take another R basics course, where you'd repeat learning a bunch of stuff that you've learned before. This one course covers everything you'll need. It. Is. Comprehensive.
• e-commerce/transactional data is an incredibly common use case. If you're not familiar with how transactional data works or you've never had to join multiple tables, this is fantastic exposure/a great use case for the aspiring data scientist.
• A slack channel with direct access to Matt (course creator) if you have any questions. I didn't personally use this feature, but as a newbie it's a tremendous value to have direct access to Matt.

I'm honestly jealous that I wasn't able to learn this way the first time, but the Tidyverse didn't even exist back then. Such is life.

The course ends with a k-means example with a deliverable that has been built in R-markdown that is stakeholder ready. This course is literally data science demystified.

In Summary:

Maybe I'm too much of a nerd. But seeing a course this well executed that provides this much value is so worth the investment to me. The speed of the transformation you'll make through taking this course is incredible. If this was available when I first started learning R I would have saved myself so much frustration. Matt Dancho (owner of Business Science) was kind enough to give me a link so that you can receive 15% off of the course. Link

The 15% off is an even better deal if you buy the bundle, but to be honest I haven't taken the 2nd course yet. I certainly will! And I'll definitely write a review afterwards to let you know my thoughts. Here is the link to the bundle: Link

If you're feeling like becoming a data science rockstar, Matt launch a brand new course and you're able to buy the 3 course bundle. The new course is "Predictive Web Applications For Business With R Shiny": Link

If you take the course, please let me know if you thought it was as amazing as I did. You can leave a testimonial in the comment or reach out to me on LinkedIn. I'd love to hear your experience!

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.

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.

1. Community
2. Yoga + Dancing + Music + Fantastic Energy
3. 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.