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!

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.

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:

1. “Sabermetrics 101: Introduction to Baseball Analytics — Edx”“An introduction to sabermetrics, baseball analytics, data science, the R Language, and SQL.”

2. “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:

1. “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.”

2. "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:

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

2. “Learn Python” — CodeAcademy

Python for Data Science:

1. “Applied Data Science With Python Specialization” — Coursera

2. “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:

1. “Machine Learning” — Coursera (This is an Andrew Ng course)

2. “Machine Learning A-Z™: Hands-On Python & R In Data Science” — Udemy (This is a Kirill Eremenko course)

3. “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!

Favorite MOOCs for Data Scientists. Data science courses. Data Science resources. Data analysis, data collection, data management, data tracking, data scientist, data science, big data, data design, data analytics, behavior data collection, behavior data, data recovery, data analyst. For more on data science, visit www.datamovesme.com

Beginning the Data Science Pipeline - Meetings

I spoke in a Webinar recently about how to get into Data Science. One of the questions asked was "What does a typical day look like?"  I think there is a big opportunity to explain what really happens before any machine learning takes place for a large project. I've previously written about thinking creatively for feature engineering,  but there is even more to getting ready for a data science project, you need to get buy in on the project from other areas of the business to ensure you're delivery insights that the business wants and needs.It may be that the business has a high priority problem for you to solve, but often you'll identify projects with a high ROI and want to show others the value you could provide if you were given the opportunity to work on the project you've come up with.The road to getting to the machine learning algorithm looks something like:

• Plenty of meetings

• Data gathering (often from multiple sources)

• Exploratory data analysis

• Feature engineering

• Researching the best methodology (if it's not standard)

• Machine learning

We're literally going to cover the 1st bullet here in this article. There are a ton of meetings that take place before I ever write a line of SQL for a big project.  If you read enough comments/blogs about Data Science, you'll see people say it's 90% data aggregation and 10% modeling (or some other similar split), but that's also not quite the whole picture. I'd love for you to fully understand what you're signing up for when you become a data scientist. 

Meetings: As I mentioned, the first step is really getting buy in on your project.  It's important that as an Analytics department, we're working to solve the needs of the business.  We want to help the rest of the business understand the value that a project could deliver, through pitching the idea in meetings with these stakeholders.  Just to be clear, I'm also not a one woman show. My boss takes the opportunity to talk about what we could potentially learn and action on with this project whenever he gets the chance (in additional meetings). After meetings at all different levels with all sorts of stakeholders, we might now have agreement that this project should move forward.

More Meetings: At this point I'm not just diving right into SQL.  There may be members of my team who have ideas for data that I'm not aware of that might be relevant.  Other areas of the business can also help give inputs into what variables might be relevant (they don't know they database, but they have the business context, and this project is supposed to SUPPORT their work).There is potentially a ton of data living somewhere that has yet to be analyzed, the databases of a typical organization are quite large, unless you've been at a company for years, there is most likely useful data that you are not aware of.

The first step was meeting with my team to discuss every piece of data that we could think of that might be relevant.  Thinking of things like:

• If something might be a proxy for customers who are more "tech savvy".  Maybe this is having a business email address as opposed to a gmail address (or any non-business email address), or maybe customers who utilize more advanced features of our product are the ones we'd consider tech savvy.  It all depends on context and could be answered in multiple ways.  It's an art.

• Census data could tell us if a customers zip code is in a rural or urban area? Urban or rural customers might have different needs and behave differently, maybe the extra work to aggregate by rural/urban isn't necessary for this particular project.  Bouncing ideas off other and including your teammates and stakeholders will directly impact your effectiveness.

• What is available in the BigData environment? In the Data Warehouse? Other data sources within the company.  When you really look to list everything, you find that this can be a large undertaking and you'll want the feedback from others.

After we have a list of potential data to find, then the meetings start to help track all that data down.  You certainly don't want to reinvent the wheel here.  No one gets brownie points for writing all of the SQL themselves when it would have taken you half the time if you leveraged previously written queries from teammates. If I know of a project where someone had already created a few cool features, I email them and ask for their code, we're a team.  For a previous project I worked on, there were 6 different people outside of my team that I needed to connect with who knew these tables or data sources better than members of my team.  So it's time to ask those other people about those tables, and that means scheduling more meetings.

Summary: I honestly enjoy this process, it's an opportunity to learn about the data we have, work with others, and think of cool opportunities for feature engineering.  The mental picture is often painted of data scientists sitting in a corner by themselves, for months, and then coming back with a model.  But by getting buy in, collaborating with other teams, and your team members, you can keep stakeholders informed through the process and feel confident that you'll deliver what they're hoping.  You can be a thought partner that is proactively delivering solutions.

Tips for starting a data science project. Data analysis, data collection , data management, data tracking, data scientist, data science, big data, data design, data analytics, behavior data collection, behavior data, data recovery, data analyst. For more on data science, visit www.datamovesme.com.