Machine learning (ML) is a key component of a data scientist’s job. Machine learning skills are valuable and in high demand not only to data scientists but also marketing and human resources professionals. These professionals use machine learning to answer questions using predictive or prescriptive modeling, said Libby Duane Adams, cofounder and chief advocacy officer at Alteryx.

“Machine learning is all about the automation of using data to be able to find those intricacies, those patterns in the data that can drive those models that machine learning technologies can build,” Adams said.

Business analysts in HR, finance, marketing and tax departments as well as supply chain use data science, which incorporates machine learning. Professionals in these lines of business are looking to upskill to learn data modeling and machine learning, Adams said.

“It's no longer just about giving these job responsibilities to a true data scientist,” Adams said. “It's about upleveling everyone in the organization to be able to use those data assets.”

How to Study Machine Learning Along With Data Science

In one example of machine learning training, online learning platform Springboard recently partnered with Washington University in St. Louis to offer bootcamps in data science and data engineering.

The data science course at Washington University incorporates lessons in statistical inference and ML to manipulate data and make conclusions from research. The course also teaches how to use supervised and unsupervised machine learning algorithms as well as the metrics to evaluate the performance of algorithms.

High demand in the job market signaled the urgency to build a program with key data science skills, according to Joe Streit, director of Washington University's Technology & Leadership Center.

“When we look at the job market and available openings and the need for this kind of training for the market, it is imperative that we get started,” Streit said. “Organizations have to make data-driven decisions, and this training helps organizations find the people with those skills to make key business decisions.”

The Springboard data science class at Washington University teaches classes on statistics and various data models to prepare data scientists to use machine learning, Alloy said. It teaches students on the types of machine learning models they would need in the role of a data scientist, according to Sanam Raza, vice president and general manager of university partnerships at Springboard. The program also teaches about neural networks, image processing, and text and categorical data processing.

A priority for the Springboard team was mapping work experience directly to their curriculum, said Laura McDonald, director of learning experience design at Springboard.

In the Springboard program’s advanced machine learning track, students can map relevant work experience to advanced machine learning skills, McDonald said. In this track, the Washington University Springboard class will offer training in advanced time series analysis and deep learning implementation, including neural network architectures, as well as production machine learning methods. It also covers image processing and network analysis.

Why Data Cleaning Is Important to Machine Learning Training

Data cleaning and data processing are key parts of training in the data science bootcamp at Washington University. A large part of the data science class covers using machine learning to determine what needs fixing among millions or billions of lines of data before performing data modeling, according to Adam Alloy, senior copywriter for Springboard’s technical courses.

“The model is useless if you don't do the data cleaning,” Alloy said. “Data that gets put into a database is messy, and there are all kinds of errors and inconsistencies and just things that are in the dataset that if they aren't dealt with, they will just ruin any predictions and ruin any modeling,” Alloy says.

Real-world examples of using machine learning as part of data science include labeling an X-ray as cancerous or using speech recognition for voice dialing, voice search and medical diagnoses, McDonald said. In addition, training in machine learning helps in algorithmic trading and analyzing market microstructures.

Machine learning also incorporates feature engineering, including training data models and labeling data for facial recognition. “It will know, for instance, that the edge of a face is the edge of a face and not just some pixels,” Alloy says.

The work of data scientists entails creating machine learning models and preparing data for those models so they can collect meaningful insights and analyze the data, Alloy says.

Springboard and the Washington University instructors also teach future technologists on when to use different types of machine learning models. “Whether they're going to use the K-means clustering versus random forest, they need to be able to make those decisions,” Raza says. “And that's what we enable them to do in our courses. That's what we understand are the most critical decisions they're making around ML. This is the role of a data scientist to do that.”

Corporate Machine Learning Training Programs

Alteryx also provides training in machine learning through its SparkEd, which includes training in predictive and prescriptive analytics. A free program, it upskills students in the classroom with in-demand analytics skills. Marketing, economics, finance and accounting programs at universities incorporate the data science and machine learning training via SparkEd; it serves as another option beyond four-year degrees for people looking to develop data science or data engineering skills, according to Adams. She noted that Alteryx no longer requires two- or four-year degrees for their open roles.

The SparkEd program accommodates learners looking to change careers. “They're learning more about data analytics, they're developing these skills, they're following a curriculum and they're working on their Alteryx certification,” Adams said.