Business Science’s Time Series Course is Incredible
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).
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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.
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:
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.
Data Moved Me in 2019
2019 was my first full year blogging! Although if you've been following you've probably noticed that I've reduced the frequency of my writing. There is no end to the amount of stuff I could be doing, but let's take a look at what I actually did this year!I gave workshops this year on assessing where you are in the journey towards becoming data driven, and I gave talks on my favorite subject: effectively communicating machine learning results to non-technical stakeholders. Some of the places I spoke where:
My youngest is 2 years old, so although I love speaking, I did try to keep speaking to a minimum in 2019. I will be speaking at Predictive Analytics World - Business, so I'll be there in June 2020 in Vegas. Hope to see you there, maybe we can hit the blackjack tables.Other amazing things that happened in 2019.
I left corporate in January 2019! I'm now doing consulting/training and I've learned a ton this year by taking the leap.
I became an instructor for UC Berkeley Ext. This has been incredibly fun and rewarding.
I became a community partner for ODSC.
I'm interviewed in a book, that's pretty cool. My longtime friend Jacqueline Nolis included me in the book she co-authored with Emily Robinson titled "Build a Career in Data Science" . Check the book out if you're looking to build a career in data science, Jacqueline and Emily have created an amazing resource.
Kate Strachnyi and I are really close to finishing writing "Mothers of Data Science" . The book is currently with the editor and we hope to publish it in the first half of 2020.
One of my long-term goals/dreams was to be able to create a schedule for myself where I would be available when my kids got off the school bus. My daughter Susie started kindergarten this year and I've been able to get her on and off the bus. Only time will tell if I'll continue this way for future school years. It's honestly been a challenge with the number of half-days and days off, it's possible I might revisit this "dream" going forward. But with everything in life, it's hard to know what truly makes you happy until you take the leap and try. I had posted on LinkedIn that I had achieved this goal, and it became an article in Working Moms.
All in all, 2019 was a year where I blogged a little less, was on social media a little less compared to 2018. But I tried new things, took on new exciting contracts, realized I took on too much, and course corrected. It's been an incredible journey with a ton of growth this year. I've also worked on a lot of back end stuff in my business, setting up a CRM, hiring an assistant. Lots of things behind the scenes that have been fun and nerdy in their own right.
I launched t-shirts on my site, then something went wrong with the integration with the fulfiller and then took the shirts off of my site. I still wear my shirts almost everyday, and I'm still pretty proud of this Bayesian butt pun.
Thank you for reading my short "year in review". I promised myself I would not miss the opportunity to write the yearly "Dear Diary", but at the same time I'm still learning from my mistake of taking on too many deliverables at once. 2020 will be an opportunity to practice better boundaries and not bite off more than I can chew. I look forward to being a part of your 2020 as well :)