Machine learning, a subfield of artificial intelligence, is huge right now. In 2015, the web search for this topic exploded, and interest in it continues to rise today. According to Entrepreneur, machine learning “has garnered particular attention from the pundits for its potential impact on the world’s most important industries.”
So, what does this mean for data scientists? If you haven’t taken a machine learning course, should you now? Our answer: Yes. Today, machine learning is considered a prominent component of data science. Taking a machine learning course is going to help you stay at the forefront of your field as well as gain greater insight and efficiency as a data scientist.
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The Basics of Machine Learning
Just in case, here’s a quick refresher on machine learning. (Don’t need one? Skip down to the best ways to learn it.) At a really high level, the goal of machine learning is to enable computers to learn on their own and predict outcomes through algorithms, such as neural networks, that mimic our own decision-making abilities.
How does machine learning actually work? A computer engineer or data scientist will develop an algorithm that allows a computer to spot patterns in large data sets. For example, if you wanted a machine to be able to identify an apple from a banana, you might gather data on color and density of different pieces of fruit and feed that information into the machine. Based on this data set, the computer will be able to accurately identify—on its own—whether a fruit is an apple or a banana. And as it repeats a task, the computer will continually learn and optimize its performance.
The power of machine learning is that it allows computers to make predictions, extract insights, or take actions without the need for human intervention.
Why Should Data Scientists Learn Machine Learning Now?
A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets.
It’s true that machine learning is only one piece of data science, but it’s an important one. According to Forbes, machine learning is the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change.
“Machine learning is a constantly growing and changing field,” says Dr. Abra Brisbin, assistant professor of mathematics at UW-Eau Claire and a faculty member for the UW Data Science program. “Understanding machine learning lets data scientists stay at the forefront of their industry, because they can adapt to those changes and use machine learning techniques as tools rather than black boxes.”
This cutting-edge technology enables data scientists to do things that were not possible five years ago. Because of this, many companies and organizations are rushing to hire talented professionals who can employ machine learning to gain valuable information, make processes more efficient, and develop new products.
Some real-world machine learning examples:
- Netflix uses machine learning algorithms to recommend movies to customers.
- Facebook employs machine learning to create audience interests for ad targeting.
- Autonomous vehicles have machine learning technologies that integrate data points from sensors to operate the vehicle.
- Twitter uses it to crop photos so that the most interesting part of the photo is displayed.
- Ticketmaster has machine learning-generated dynamic pricing, which is based on how well concert tickets are selling at any given moment.
- Retail websites use machine learning models to predict which products to recommend and when to give out discounts.
- Cybersecurity organizations can use machine learning to detect fraud, prevent, phishing, and defend against cyberattacks.
Oh, and one more reason to have specialized knowledge in machine learning: In the United States, it pays very well.
Online Machine Learning Courses
Two ways to learn machine learning are:
- An online machine learning course
- A data science degree program
Which is best for you? Base your decision on your learning preferences, career goals, and on how much you already know about programming and statistics.
Single Online Machine Learning Courses
If you have a solid foundation in both programming and statistics—and a casual interest in the topic—an online machine learning course could provide sufficient preparation for getting started implementing algorithms and building models.
Data science blogs, including KDNuggets, almost always recommend one course for machine learning newcomers: Andrew Ng’s machine learning course on Coursera. Coursera also offers a number of other beginner courses about AI, deep learning, and other similar topics. For a data science-specific course, Udemy offers an “Introduction to Machine Learning for Data Science.”
These are great resources for those who want a broad introduction to machine learning, data mining, and statistical pattern recognition. The downside of these courses? They are broad introductions. These single, online courses lack the structured, in-depth instruction and guidance of a degree program.
Data Science Degree Program
This brings us to a data science degree. This is your best choice if you want to become the machine learning specialist at work. Through in-depth training and instruction, a degree program will solidify your understanding of statistics and the theoretical underpinnings of machine learning. It will teach you practical implementation of algorithms and model-building in popular programming languages, such as Python and R.
However, be careful when selecting a program. Not all data science degree programs have coursework that teaches machine learning.
The online, 12-course University of Wisconsin Master of Science in Data Science is different. Machine learning and predictive analytics were woven into many of the courses when the program was created, because the faculty and industry advisory board recognized them as eventual must-have skills for data scientists.
For example, the first course, DS 700: Foundations of Data Science, introduces students to machine learning. After that, DS 740: Data Mining covers machine learning concepts and practical skills in detail; DS 745: Visualization and Unstructured Data Analysis gives students in-depth understanding of machine learning for text analysis; and DS 775: Prescriptive Analytics emphasizes applications of machine learning to inform the business decision-making process.
UW graduate Christian Acosta learned machine learning in the Data Science program. He said this:
“I’m finding that the specific projects I chose are applying to my current work in marketing analytics with Kohl’s Department Store. Specifically, I have used topic modeling and various supervised machine learning to analyze past performance. In the future, I hope to use our data to build shopping propensity models for our consumers.”
Also, don’t forget that to be great at machine learning, you need strong strategic thinking and business intelligence skills. UW Data Science helps students hone these kinds of soft skills.
“The art of being able to do machine learning well comes from seeing the core concepts inside the algorithms and how they overlap with the pain points trying to be addressed. Great practitioners start to see interesting overlaps before ever touching a keyboard.”
—Sean McClure, ThoughtWorks senior data scientist talking to KDNuggets
A master’s degree is the more expensive way to learn machine learning, but it’s also the most worthwhile. If you have a Master of Science in Data Science on your resume, it will hold more weight than a single course. Plus, many of our students receive employer reimbursement, so we recommend checking if this is an option for you.
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Have questions about the UW Data Science capstone, curriculum, tuition, admissions, and more? An enrollment adviser would be happy to discuss these things with you!
Or, to explore the Master of Science in Data Science program, start here.