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Essential Machine Learning Knowledge for Data Scientists

Machine learning (ML) is an indispensable aspect of a data scientist's role, with valuable and highly sought-after skills that extend beyond the realm of data science.

According to Libby Duane Adams, the co-founder and chief advocacy officer at Alteryx, marketing and human resources professionals also utilize machine learning for predictive or prescriptive modeling to derive insights and make informed decisions.

“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.

Professionals in various business functions such as HR, finance, marketing, tax, and supply chain leverage the power of data science, which encompasses machine learning. As per Adams, business analysts in these fields are interested in upskilling themselves to acquire knowledge of data modeling and machine learning.

“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

An instance of machine learning education involves the collaboration between the online learning platform, Springboard, and Washington University in St. Louis to provide bootcamps in data science and data engineering.

The data science program at Washington University covers topics such as statistical inference and machine learning, enabling students to effectively handle data and draw insights from research. Additionally, the curriculum delves into supervised and unsupervised machine learning algorithms and provides an understanding of metrics to assess algorithm performance.

Joe Streit, the director of the Technology & Leadership Center at Washington University, highlighted the pressing need to develop a program that encompasses essential data science skills, citing the high demand for professionals in this field.

“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 data science program offered by Springboard at Washington University provides instruction in statistics and diverse data models to equip data scientists with the necessary skills to work with machine learning, as per Alloy. The curriculum covers a range of machine learning models required for a data scientist role, according to Sanam Raza, the Vice President and General Manager of University Partnerships at Springboard. Furthermore, the program includes education on neural networks, image processing, and the processing of text and categorical data.

Laura McDonald, the Director of Learning Experience Design at Springboard, highlighted the team's priority of aligning work experience with their curriculum.

According to McDonald, the advanced machine learning track of the Springboard program enables students to leverage their relevant work experience and acquire advanced machine learning skills. The program offers training in advanced time series analysis and deep learning implementation, covering topics such as neural network architectures and production machine learning methods. Additionally, the program also includes instruction in image processing and network analysis. These comprehensive courses will be offered in the Washington University Springboard class.

Why Data Cleaning Is Important to Machine Learning Training

In the data science bootcamp at Washington University, data cleaning and processing are fundamental components of the training program. The curriculum places a significant emphasis on using machine learning techniques to identify and rectify issues among large datasets consisting of millions or billions of lines of data before proceeding to the data modeling stage. As per Adam Alloy, the senior copywriter for Springboard's technical courses, this aspect constitutes a substantial part of the data science class.

“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.

According to McDonald, machine learning is widely used in data science and has numerous real-world applications. Examples of these include using machine learning to label X-rays as cancerous, developing speech recognition systems for voice dialing, voice search, and medical diagnoses. Additionally, training in machine learning is valuable for analyzing market microstructures and algorithmic trading.

Incorporating feature engineering is also a critical aspect of machine learning, which involves training data models and labeling data to enhance facial recognition systems. “It will know, for instance, that the edge of a face is the edge of a face and not just some pixels,” Alloy says.

According to Alloy, data scientists are responsible for creating machine learning models and preparing data for analysis in order to extract meaningful insights from it.

In addition, Springboard and Washington University instructors also provide education to upcoming technology professionals on how to determine which types of machine learning models to use in different situations. “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 offers machine learning training through its SparkedEd program, which covers both predictive and prescriptive analytics. The program is free and helps students acquire in-demand analytics skills. SparkedEd is also integrated into marketing, economics, finance, and accounting programs at universities, providing an alternative to four-year degrees for individuals seeking to develop data science or data engineering skills, as Adams explained. Furthermore, she pointed out that Alteryx no longer mandates four-year degrees for its open positions.

The SparkedEd program is designed to cater to individuals who are interested in switching 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.

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