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.
Getting into Data Science FAQs
I often see similar questions in my inbox or asked in webinars. I'd like to set the record straight with data. However, I didn't need to start from scratch, there was an excellent article on KD Nuggets by Jeff Hale. Here is the link to his article: "The Most in Demand Skills for Data Scientists". He had already scoured multiple job search site and aggregated data on the demand for different skills for data scientist. I recreated some of his analysis myself, so that I could come up with some points for this article, and to make sure his numbers matched mine before posting. The charts I created below are based on data from searches on indeed.com only. A search for "Data Scientist" was the denominator, and the numerator would be "Data Scientist" plus another term I was looking to see results for. I'm not sure how many job descriptions listed on indeed.com might be duplicates, so this is not gospel, but still interesting.This article will cover a couple of "Frequently Asked Questions" using the methodology above (that was adopted from Jeff).
Questions I'm frequently asked:
Should I learn R or Python?
As a Computer Science major, can I get into data science?
How important is SQL?
Should I learn R or Python?This would most likely be the most frequently asked question (although I've never analyzed the questions that I'm asked). In Jeff's article, you were able to see that Python has the edge in terms of coming up in job listings. I recreated the methodology for myself to look at this a little further.55% of the job listings actually list both tools, as in the company would like to see that you have experience with "Python and/or R". That should make those who have a preference for one tool feel better. If you're looking to pick up either R or Python and you're just getting your hands dirty, I'd suggest python. For listings that only specify one tool, Python is almost 5x more likely to be listed as the tool of choice compared to R.I was happy to see this, as I've mentioned in a number of webinars and comments on social media that it "feels like" Python is slightly more popular. It would have been a bummer if I had been giving misinformation this whole time.
% of Data Science Positions Mentioning a Particular Skill on Indeed.com
Pulled this data by doing a search on indeed.com 11/2018
As a Computer Science major, can I get into data science?I'm always surprised when I see this question, because as someone who's been in the field for a long time, it just seems clear that this is a fantastic skill as your foundation for moving into data science. Data science requires a number of different skills to be successful, and being able to program is definitely one of the core pillars. Analytics and Statistics are coming in first, but Analytics and Statistics could easily be mentioned somewhere in the job description other than specifically where preferred degrees are mentioned. If a job description says "computer science" they're most likely speaking to the degrees they would prefer from candidates. More than 50% of job descriptions mention "computer science". There you have it, a degree in computer science is something "in demand" for getting into data science.
% of Data Science Positions Mentioning a Particular Skill on Indeed.com
Pulled this data by doing a search on indeed.com 11/2018
How important is SQL?I'm frequently asked this question, and I was honestly surprised that SQL came in third behind Python and R in terms of skills. However, 51% of jobs do mention SQL. So it is certainly desired for the majority of positions, but I expected it to rank higher. Is it possible this skill is becoming assumed as a prerequisite? Or are companies figuring that SQL is easily learned and therefore not necessary to list on the job description? I wouldn't mind a job where all the datasets were aggregated for me before data cleaning and applying machine learning, I'm just not sure how many of those jobs exist. If you're a data scientist, and you haven't had to understand relational databases at any point, let me know. I'd love to hear about it.Conclusion:We saw that Python is preferred over R, but that either tool will allow you to apply to the majority of data science jobs in the US. Computer science degrees are a great stepping stone to getting into data science, and the majority of listings will expect you to know SQL.I also want to point out that "communication" was very much in the top list of skills. 46% of job descriptions listed communication in the job description. This means I'll continue to keep writing about how I use softer skills to be effective in my job. I think we sometimes do not talk about communication enough in data science, it's really imperative to delivering models and analysis that are aligned with what the business is looking for. If you'd like to see how Jeff used the data from the job search websites to discuss most in demand skills, here is the link one more time. Link.
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.