Will I be good at Machine Learning? Try Data Science.

Data drives machine learning.

Innovations in artificial intelligence are made each year, but the effectiveness of an Intelligent Assistant is still determined by the quality of data fed into its machine learning algorithms. Thankfully, there is a field of study centered around creating, maintaining, and organizing quality data sources.

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According to a report from LinkedIn, the number one career field in America in 2019 is a data scientist.Data science also topped Glassdoor’s list of Best Jobs in America for the past three years. Professionals in this field report high job security, high salaries, and high job satisfaction.

Before we explore if you’re a good fit for a career in data science, let’s look at what a data scientist does. A data scientist leverages existing data sources, and aids in creating new ones, to extract meaningful information and actionable insights. These insights drive business decisions and strategies for achieving business goals.

The key skill of data science is not the ability to build machine learning models, but the stewardship of the scientific method.

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Examples of projects that a data scientist might be tasked with include:

• Product Recommendations (e.g., Amazon and Netflix recommendations)
• Automated processes and decision-making (e.g., customer on boarding and success)
• Scoring and ranking (e.g., NPS, Feedback)
• Segmentation (e.g., demographic-based marketing)
• Optimization (e.g., risk management)

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Machine learning is a perfect complement to data science. Where algorithms excel at uncovering patterns in data, data scientists excel in discovering and determining what data sources to use.

We’ve gotten to a point where machine learning is becoming just another way of building software. And increasingly, the tools are becoming accessible enough that theory and math are abstracted away behind services doing all the heavy lifting for you. Even so, it is important for data scientists to understand the math and theory underlining artificial intelligence.

What is the magic math behind machine learning?

The key skill of data science is not the ability to build machine learning models, but the stewardship of the scientific method. Data scientists must communicate complex results to business stakeholders. More important than any math skill is thinking critically and quantitatively while utilizing domain-specific knowledge to answer questions that drive business growth.

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The data science job of tomorrow will include providing an editorial as well as ethical review to ensure the AI provides the desired outcomes.

A data scientist simplifies the complexities of data for the rest of the business. When someone introduces you to the inference function in logistic regression, you’ll say, “That’s just linear algebra!” Then to optimize over that linear model they’ll show you gradient descent, and again you’ll note, “That’s just calculus!” How about Naive Bayes? Just probability theory. Backpropagation?  Just more calculus. Not harder, just more.

But if all this math is still daunting to you, don’t fret. Thankfully we have really large calculators that Microsoft, Google, and Amazon have put ‘in the cloud’. All you need is an interface like swivl.

We need more empathy, creativity, and diversity in AI.

AI is the automation component that can simplify and streamline your processes. However, while AI-powered automation can deliver efficiency and 24/7 access, leveraging humans to deliver personalization and creativity is critical to making the customer experience enjoyable.  This is why it is critical to keep Humans-in-the-Loop. The data science job of tomorrow will include providing an editorial as well as an ethical review to ensure the AI provides the desired outcomes.

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We invite you to meet Hoover, our Intelligent Assistant to learn more about swivl, how we can help you make your data more actionable,  and bring personalization into customer experiences keeping Humans-in-the-Loop.

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