Business Science’s Time Series Course is Incredible

This post contains affiliate links that help to offset the cost of running the blog, plus the link gives you a special 15% discount.  If you use the link, thank you!

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.

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

How Blogging Helps You Build a Community in Data Science

Holy Moly. I started blogging in March and it has opened my eyes.I want to start off by saying that I didn't magically come up with this idea of blogging on my own. I noticed my friend Jonathan Nolis becoming active on LinkedIn, so I texted them to get the scoop. They told me to start a blog and jokingly said "I'm working on my #brand". I'm the type of person to try anything once, plus I already owned a domain name, had a website builder (from working at Vistaprint), and I have an email marketing account (because I work for Constant Contact). So sure, why not? If you're thinking about starting a blog. Know that you do not need to have a bunch of tools already at your disposal. If needed, you can create articles on LinkedIn or Medium. There are many options to try before investing a penny . . . but of course, you can go ahead and create your own site.

I have since moved to self-hosted Wordpress. I've fallen in love with blogging, and Wordpress lets me take advantage of lots of extra functionality.With my first post, my eyes started to open up to all the things that other members of the Data Science community were doing. And honestly, if you had asked me about who I most looked up to in Data Science prior to starting my blog, I'd probably just rattle off people who have created R packages that have made my life easier, or people who post a lot of answers to questions on Stack Overflow. But now I was paying attention on LinkedIn and Twitter, and seeing the information that big data science influencers like Kirk Borne, Carla Gentry, Bernard Marr, and many others (seriously, so many others) were adding to the community.

I also started to see first hand the amount of people that were studying to become a data scientist (yay!). Even people who are still in school or very early in their careers are participating by being active in the data science community. (You don't need to be a pro, just hop in).  If you're looking for great courses to take in data science, these ones have been highly recommended by the community here.I've paid attention to my blog stats (of course, I'm a data nerd), and have found that the articles that I write that get the biggest response are either:

1. Articles on how to get into data science

2. Coding demos on how to perform areas of data science

But you may find that something different works for you and your style of writing. I don't just post my articles on LinkedIn. I also post on Twitter, Medium, I send them to my email list, and I put them on Pinterest. I balked when someone first mentioned the idea of Pinterest for data science articles. It's crazy, but Pinterest is the largest referrer of traffic to my site. Google Analytics isn't lying to me.

I've chatted with so many people in LinkedIn messaging, I've had the opportunity to speak with and (virtually) meet some awesome people who are loving data and creating content around data science. I'm honestly building relationships and contributing to a community, it feels great. If you're new to the "getting active in the data science community on LinkedIn" follow Tarry Singh, Randy Lao, Kate Strachnyi, Favio Vazquez, Beau Walker, Eric Weber, and Sarah Nooravi just to name a few. You'll quickly find your tribe if you put yourself out there. I find that when I participate, I get back so much more than I've put in.Hitting "post" for the very first time on content you've created is intimidating, I'm not saying that this will be the easiest thing you ever do. But you will build relationships and even friendships of real value with people who have the same passion. If you start a blog, I look forward to reading your articles and watching your journey.

Building community in data science through blogging. 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

What I Enjoyed Most at ODSC East 2018

Last week I had the opportunity to attend Open Data Science Conference (ODSC) in Boston.  It was awesome to see people just walking around who I had previously read about or I'm following them on twitter.  It was even nicer to meet some of these people, and I was amazed at how friendly everyone was.

Of course you can't attend everything at a conference like this, at one point there was 11 different sessions going on at once.  It was really difficult to determine which sessions to attend given the number of great options, but I tried to align the information I'd be consuming closely with what I'd be able to bring back to my day job and implement.

In this article I'll cover some learnings/ favorite moments from:

• one of the trainings
• a couple different workshops
• the sweet conference swag
• mention one of the keynotes

Trainings:My original plan was to take an R training in the morning on Tuesday and take a Python training that afternoon.  However, what really happened was I went to the R training in the morning, this training left me feeling super jazzed about R, and so I ended up going to another R training that afternoon (instead of the Python training I had originally planned on).  The morning R training I took was "Getting to grips with the tidyverse (R)" given by Dr. Colin Gillespie.  This was perfect, because I had been struggling with dplyr (an R package) the night previously, and this training went through parts of dplyr with great explanations along the way.  Colin also showed us how to create plots using the package "Plotly".  This was my first time creating an interactive graph in R. Easy to use, and super cool. He was also nice enough to take a look at the code I was currently working on, I definitely appreciated this.

The afternoon R training I attended was given by Jared Lander entitled "Intermediate RMarkdown in Shiny".  It was my first introduction to Shiny.  I had heard about it, but had never ventured to use it, now I don't know what I was waiting for. If you ever have the opportunity to hear Jared speak, I found him incredibly entertaining, and he explained the material clearly, making it super accessible.  I like to think Jared also enjoyed my overly animated crowd participation.  

Workshops:

On Thursday I attended "Uplift Modeling and Uplift Prescriptive Analytics: Introduction and Advanced Topics" by Victor Lo, PHD. This information really resonated with me.  Dr. Lo spoke about the common scenario in Data Science where you'll build a model to try and predict something like customer attrition.  You'd maybe take the bottom three deciles (the people with the highest probability of cancelling their subscription, and do an A/B test with some treatment to try and encourage those customers to stay.  

In the end, during analysis, you'd find that you did not have a statistically significant lift in test over control with the usual methods.  You end up in a situation where the marketers would be saying "hey, this model doesn't work" and the data scientist would be saying "what? It's a highly predictive model".  It's just that this is not the way that you should be going about trying to determine the uplift.  Dr. Lo spoke about 3 different methods and showed their results.  

These included:

• Two Model Approach
• Treatment Dummy Approach
• Four Quadrant Method

Here is the link to his ODSC slides from 2015 where he also covered these 3 models (with similar slides): here 

I've experienced this scenario before myself, where the marketing team will ask for a model and want to approach testing this way.  I'm super excited to use these methods to determine uplift in the near future.

Another workshop I attended was "R Packages as Collaboration Tools" by Stephanie Kirmer (slides).  Stephanie spoke about creating R packages as a way to automate repeated tasks.  She also showed us how incredibly easy it is to take your code and make it an R package for internal use.  Here is another case that is applicable currently at my work.  I don't have reports or anything that is due on a regular cadence, but we could certainly automate part of the test analysis process, and there are currently ongoing requests asked of Analytics in our organization that could be automated.  Test analysis is done in a different department, but if automated, this would save time on analysis, reduce potential for human error in test analysis, and free up bandwidth for more high value work.SWAG:

Although conference swag probably doesn't really need a place in this article, Figure Eight gave out a really cool little vacuum that said "CLEAN YOUR DATA".  I thought I'd share a picture with you.  Also, my daughter loved the DataRobot stickers and little wooden robots they gave out.  She fashioned the sticker around her wrist and wore it as a bracelet.  3 year olds love conference swag:

  Keynote:The keynote was Thursday morning.  I LOVED the talk given by Cathy O'Neil, a link to her TED talk is here.  She spoke about the importance of ethics in data science, and how algorithms have to use historical data, therefore, they're going perpetuate our current social biases. I love a woman who is direct, cares about ethics, and has some hustle.  Go get em' girl. I made sure to get a chance to tell her how awesome her keynote was afterwards.  And of course I went home and bought her book "Weapons of Math Destruction".  I fully support awesome. Summary:I had an incredible time at the ODSC conference.  Everyone was so friendly, my questions were met with patience, and it was clear that many attendees and speakers had a true desire to help others learn. I could feel the sense of community.  I highly suggest that if you every get the opportunity to attend, go!  I am returning to work with a ton of new information that I can begin using immediately at my current job, it was a valuable experience.  I hope to see you there next year.