It doesn’t take a lot of math to understand machine learning, but you will need some calculus and linear algebra. If you haven’t covered these or it’s been long enough that you need to brush up, there are great free options out there.
The excellent Khan Academy covers Linear Algebra, Differential Calculus, and more. Khan is an incredible resource, but if you are intimidated by calculus or just prefer a methodical pace that allows you to develop intuition about the concepts as you go, I highly recommend Jim Fowler’s Calculus 1 Coursera course.
You’ve got R and R Studio installed–now what? If you are more of a book-learner, I’ll give you some places to start in the next post, but we’ll start with the MOOC space. There is a lot of new content coming all the time; so all I can give you is a snapshot as of February of 2015, but I’ll provide updates occasionally.
Coursera Data Science Track
Put together by three professors from the Johns Hopkins University Bioinformatics program, the Data Science Specialization from Coursera is nine 4-week courses on R and Data Science. As of this writing, I’ve taken R Programming, Getting and Cleaning Data, Statistical Inference, and Practical Machine Learning.
- Consistent use of R. You will become pretty proficient with R just by taking a few of these courses.
- Nice combination of video lectures, quizzes, and practical projects.
- Popular courses with active discussion groups during class offerings.
- Coverage of topics like statistical inference and machine learning not in-depth enough to be called anything other than surveys.
- Some of the content and assignments appear to have been rushed and not well edited.
- The profs don’t interact on the discussion boards.
Machine Learning by Andrew Ng
Machine Learning by Stanford’s Andrew Ng was one of the earliest and most popular courses on machine learning. This course goes into enough depth for you to not just use machine learning as a black box, but to understand how and why it works. That level of understanding comes with a caveat: you’ll need to remember a bit of your college calculus and linear algebra (although Ng provides an optional section on the linear algebra you’ll need).
Statistical Learning with Trevor Hastie and Robert Tibshirani is new and in it’s first session. I started the course, but haven’t been able to make the time to keep up with it. However, I think this may be the best course to start with for several reasons:
Udacity offers some machine courses that look pretty good, but to access the course materials and exercises (which is necessary to really learn), you must use the paid version which is pretty expensive. Also check out Pedro Domingo’s Machine Learning course.
Data Science in the Cloud with Azure ML and R is a short eBook that steps you through building a model and deploying it to Azure ML as a Web service. The book assumes you already know how to use R; so, it’s not the best starting point if you are new to R. However, I’d go ahead and pick up the book. It covers the critical area of how to deploy a model once it’s built.
You’ve chosen R as your tool for getting started learning data science and machine learning. If you are coming from a background on the Microsoft technology stack, your decision to choose R was affirmed by the recent announcement that Microsoft acquired Revolution Analytics, a leader in the R world.
Download and install R from CRAN, The Comprehensive R Archive Network. You’ll find installers for Mac, Windows, and Linux. I’ve installed on both Mac and Windows. They are both simple and straightforward.
Next, download and install R Studio. Even if you are a command-line person who thinks that IDEs rot the mind and inhibit true learning of a new language, trust me–you will still be writing R code in a Notepad-like experience and the integrated help, plots, and data views make R Studio a must-have. Just like R, R Studio is a a straightforward instal on Mac or Windows.
While you’re at it, download a copy of Introduction to Statistical Learning with Applications in R and The Elements of Statistical Learning. Two of the best data science books on the R platform are made freely available by the authors in electronic format!
One of the first questions I confronted when setting out to learn Data Science was what platform to use. As you begin to look at books and courses you realize that you’ll need a basic platform for working with data. Think of it as an IDE for data manipulation, statistics, and algorithms. For example, if you take Andrew Ng‘s popular Machine Learning course, you’ll be doing the exercises in Octave. If you take the machine learning course on Pluralsight, you be using ENCOG.
Data Scientists Love Them Some Python
Python is the most popular general purpose programming language in the machine learning world. I’m not a Python guy (yet), but you can start at SciPy and go from there.
Why I Chose R
I initially started working through Andrew Ng’s course, but I wasn’t sold on spending a lot of time learning Octave. I had a Data Mining book with all the exercises in Weka, but I wasn’t loving that idea either. I kept hearing about this statistics language called R. After some investigation, I found that the R language is nothing to write home about, but R Studio and the vast collection of available packages make R a great choice.
R Studio has been great to work in. The popular Coursera Data Science specialization is essentially an extended course in R. Azure ML Studio now supports the R language. The list goes on and is growing. The folks at Kaggle show the popularity of tools used by their competitors, with R as the clear winner…
Bottom line… if you have an tool that makes sense for you, then use it. Otherwise, start with R.
Extracting meaning from data is nothing new, but the world has really woken up to the value of predictive analytics and machine learning… preference and recommendation engines, effective marketing, spam filters that actually work, better medicine, even self-driving cars. This new focus has created a scramble as companies have tried to find people with the skills needed to get them into the predictive game. This scramble has led to two problems: 1) what, exactly am I looking for (not just programmers and not statisticians), and 2) where are these people?
Emergence of the Data Scientist
The world has settled on the terms Data Science and Data Scientist. HBR famously referred to the Data Scientist as the sexiest job of the 21st century.
I like the term because its practitioners are applying the scientific method while working in the medium of data–creating and validating hypotheses, making discoveries, and improving life in myriad ways.
A data scientist is more than a statistician:
- The data is not sitting in nice, neat SAS datasets. It’s in unstructured social media networks, streaming off of sensors, or in various other messy forms.
- The machine learning algorithms bringing the breakthrough innovations are more computational than mathematical.
- Implementation of the insights coming from the data require significant programming.
A data scientist is more than a programmer:
- Programmers don’t normally think in terms of designing and executing experiments.
- They must understand what data these experiments require and what can be inferred from the data.
- The big data aspect requires specialized skills in distributed computation.
So, What is a Data Scientist?
This rare combination of skills–and the hype surrounding the field–has led to some fun definitions of the data scientist:
These snarky definitions have been pretty popular as well:
- “Data Scientist is a Data Analyst who lives in California”
- “A data scientist is a business analyst who lives in New York.”
- “A data scientist is a statistician who lives in San Francisco.”
- “Data Science is statistics on a Mac.”
Hype and cynicism aside, the world needs more technologists that can program, handle data, and have a mastery of inferential statistics. There is an incredible need and the work is intellectually stimulating. This has motivated many developers to learn to be data scientists, myself included.
Next up… approaching the data science field as a developer.
It was just over a year ago when I started talking to small company in Columbia, SC about heading up their Engineering team. They were a .NET shop–right in my wheelhouse. All I had to do was pick up the insurance domain and figure out what predictive analytics and machine learning are all about.
Technically, I didn’t have to understand machine learning because the company has a core research team that develops and maintains algorithms. I would lead the team that turns those algorithms into great software solutions and user experiences for the insurance industry. Of course, no engineer worth his salt is going to be content to treat the heart of his system as a mysterious black box. So, for me, taking the job meant diving into machine learning, which I knew nothing about. As I spent the previous five years building mobile and web field service automation solutions, I knew “big data” was a hot topic, but I had missed the rise to prominence of predictive analytics and the whole Data Scientist craze—the sexiest job of the 21st century.
Drew Conway created a helpful and widely referenced venn diagram of skills that define the Data Scientist:
I’ve spent two and half decades filling in the red circle. As a Domain-Driven Design adherent, I’ve always committed myself to learning the domain my software is designed for—in this case insurance. However, I hadn’t given serious thought to higher math and statistics beyond batting averages and occasionally having to remind myself how obscure three sigma outliers are. I enjoyed these subjects in college, but left them behind as i built systems where the most complicated math could be done by a middle schooler. Sure, cryptography has some interesting math, but we rely on libraries for that.
I’m going to use this space to chronicle my journey from a transactional business system developer to a data scientist—or at least a machine learning/predictive analytics specialist. I’m early in the journey, but I’ve made enough missteps as well as positive steps that I can help others looking get into the predictive analytics space.
I enjoyed a moment today that would warm the heart of any geeky dad. Sitting in church this morning, the pastor told the story of his college days when he read Chariots of the Gods? and came to believe that life on Earth was seeded by aliens. As an aside, he added that he hadn’t thought to ask the question of where the aliens came from, and if they came from aliens, then where did those aliens come from. My 11-year-old leaned over and whispered in my ear… “It’s turtles all the way down.”
The 70’s and 80’s had The Mythical Man-Month. The 90’s had Peopleware. These works helped software managers understand and communicate to non-software people the dynamics involved in effectively managing software teams. The management models that got cars and TVs built at ever cheaper costs didn’t work on software projects. Brooks, Lister, and DeMarco helped thoughtful software managers figure out how to best manage professionals who must bring a challenging combination of creativity and technical rigor to their work.
In Drive: The Surprising Truth About What Motivates Us, Dan Pink provides the same kind or resource for a more general audience. He argues that the models for understanding human motivation that worked in the past are outdated and don’t apply to today’s knowledge workers.
Pink contrasts internal and external motivation. Our internal motivation is driven by three needs: autonomy, mastery, and purpose.
Autonomy: We need more than “buy in” or even independence. We crave true self-direction. To provide meaningful autonomy to our teams, we need to give our people choice over:
- Task – What they do
- Time – When they do it
- Team – Who they do it with
- Technique – How they do it
Mastery: We are driven to grow, improve, and be increasingly capable of solving more and more complex problems.
- Mastery is a mindset: It requires the capacity to see your abilities not as finite, but as infinitely improvable. Internally motivated people tend to have an incremental theory of intelligence, prize learning goals over performance goals, and welcome effort as a way to improve at something that matters.
- Mastery is pain: It demands effort, grit and deliberate practice. The path to mastery – becoming ever better at something you care about – is a difficult process over a long period of time.
- Mastery is asymptotic: It’s impossible to fully realize, which makes it simultaneously frustrating and alluring.
Purpose: The old models for understanding motivation assumed that we are primarily motivated by money. Today we see money as a necessary but not sufficient reward of our work. We want to know that what we do makes a difference in the world.
Much of Drive is derivative of the research done in cognitive psychology and behavioral economics, but Pink brings it all together in an engaging and practical way that will allow managers to put these ideas into action. I highly recommend it to anyone who oversees the work of anyone else.
For a small taste of the ideas in the book, check out this presentation.
It’s only two days before the Marathon Data Systems sponsored Jersey Shore Comeback-a-thon, a 24-hour hackathon with a theme of bringing business back to the Jersey Shore in the first season after hurricane Sandy.
Many businesses along the Shore have worked very hard and made great investments to rebuild, clean up, and claw their way back into business in time for the summer season. Unfortunately, the images of devastation of the storm have left many would-be visitors thinking there is no beach to vacation to this year.
The $1000 grand prize will be awarded to the individual or team that comes up with the best application, system, or other creative use of technology to help bring business back to the Shore. There will also be two $300 runner up prizes.
We’ve had good coverage from patch.com, njbiz.com, and triCity News. Get more details here.