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:

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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.

Strong Data Science Content for Your Resume

The biggest pain point or challenge I hear when people are writing their resume is that they want concise, crisp, effective content that sounds impactful. But they’re not sure how to write that wonderful content they want.

There is so much to consider when thinking about your content. There are many different traits you want to showcase on your resume that the business values for any given position. There is much more to a successful data science hire than just technical and machine learning ability, and we'll want to think about how to best position these skills as well. Here are some quick examples of how you can up-level the content on your resume to get you started.

Here we’re going to cover:

• Starting with a verb
• Ending with the value you provided

Starting with a verb

Strong statements start with an action verb. A short list of some verbs that you can try to apply to your experience include:

• Built
• Delivered
• Developed
• Increased efficiency
• Created
• Evaluated
• Trained

Try to vary your verbs as well. Don’t use the same one over and over again throughout your resume.

So we have some words, let’s look at some real examples from resumes and how the statements improve by starting with a verb.

This first example comes from a math teacher who is learning data science through MOOCs and is planning to make a career change.

Original: “I ran live lessons on Blackboard Collaborate and attended meetings via the computer.”

Updated: "Presented math training virtually, delivered mathematical concepts in a way that students could easily comprehend and learn."

This shows that she is able to break down material and communicate well. The following would also work:

Updated (another version): "Conducted virtual meetings with expert communication. Provided students the ability to receive one-on-one guidance to keep them on pace in a way that fit their schedule."

The next example is from a BI professional who is also looking to make a move to data science:

Original: “Participation in Global Transformation Program as Commercial Finance Business Intelligence (BI) expert (Credit and Collections), in the definition of KPIs and Global template Reports. Testing, Business Readiness and Post Go live support for Ecuador implementation (Releases 1, 2 and 3). Support to front office area (sales and distribution).”

Here, our example owned the definition of KPIs and reporting. She also contributed cross-functionally to help make this project a success. Talking about ownership of KPIs, and being a strong contributor cross-functionally sounds stronger when we begin with a verb instead of “participation” (noun).

Updated:  "Owned definition of KPIs and reporting, ensuring accuracy and allowing for self-service of key metrics by stakeholders."I'd certainly need to create more bullet points to capture all of the information in the original, but this is an idea of what we're trying to achieve.

Ending your statements with the result or value

Let’s look at an opportunity for improvement that was on my resume for a while.

Original: “Built Neural Network models to forecast hourly electric load.”

Cool story, but did I just build it for fun? Or was it useful? Especially in a space where businesses are all too familiar with someone building a fancy model, and then it never gets used for anything, it is of utmost importance that you clearly demonstrate how your work was utilized.

Spell. it. out.

Updated: “Built Neural Network models to forecast hourly electric load. Model output was imperative during extreme weather and was used for capacity planning decisions.”

Now I have a statement that shows not only that I delivered a model, but that model delivered value to the business.

Maybe your previous work experience doesn’t involve building a model. Maybe you built a dashboard. Did that dashboard allow your stakeholders to get valuable information on their own (referred to as self-service)? That’s value. Did the dashboard reduce the amount of time spent on ad-hoc, low value data aggregation so you could focus on higher value initiatives? That’s value, because here you’re increasing efficiency.

Using verbs as your starting point and demonstrating the value your work provided is a great step towards marketing yourself and showcasing your talents. Think deeply about what was the purpose of the work, and spell that out on your resume.