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What Your Employees Need to Learn to Work With Data in the 21st Century

Hugo Bowne-Anderson, data scientist and host of our podcast DataFramed, deconstructs the essential topics and skills that employees need to know to work with data.
Jul 2019  · 5 min read

Hugo Bowne-Anderson (@hugobowne), a data scientist at DataCamp and host of our podcast DataFramed, conducted a webinar with Chief Learning Officer (CLO) yesterday on the essential topics and skills that employees need to know to work with data in the 21st century. Here are our key takeaways.

What is data science?

“More than anything, what data scientists do is make discoveries while swimming in data.”
– Thomas Davenport and DJ Patil, Data Scientist: The Sexiest Job of the 21st Century

“Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.”
Wikipedia

The goal of data science is to make sense of data in a way that is meaningful and can drive better decisions.

Many companies want to tackle major AI initiatives right now. But before they can, they need to start by building foundational data science skills. Monica Rogati’s Data Science Hierarchy of Needs is a great foundation for data science work and AI. It illustrates what a company must build on before they can get their AI initiatives off the ground. They must follow the sequence to collect data, store it properly, transform it, aggregate it, and optimize it before they can progress to AI and deep learning.

Source

Why data science?

When so much data is informing decisions across so many industries, you need to at least have a basic understanding of the data ecosystem in order to be part of the conversation. And no matter what you do for work, it’s very likely that data science and analytics is already reshaping your industry and will continue to do so in the long term.

Need some examples? Farmers use data gathered by drones to optimize crop yields. Financial institutions use algorithms to predict stock prices. Healthcare institutions diagnose disease using imaging data. Airbnb calculates the value of a listing based on city, average nightly price, and forecasted availability.

What do data scientists do?

Jacqueline Nolis breaks down data science into three main components on DataFramed:

  • Descriptive analytics (Business Intelligence) is essentially about getting useful data in front of the right people in the form of dashboards, reports, and emails, e.g., which customers have churned?
  • Predictive analytics (Machine Learning) is about how to take data science models and put them continuously into production, e.g., which customers may churn?
  • Prescriptive Analytics (Decision Science) is about taking data and using it to help a company make a decision, e.g., what should we do about the particular types of customers that are prone to churn?

Although many working data scientists are generalists and do all three, we’re seeing distinct career paths emerging, as in the case of machine learning engineers.

What do data scientists need to learn?

Communication skills

You might think that the key skill for data scientists is the ability to build and use sophisticated deep learning infrastructures. But what’s even more important is having the right soft skills: learning on the fly, communicating well to answer business questions appropriately, and explaining complex results to non-technical stakeholders. Critical thinking and quantitative skills remain in demand, but they’re useless without the ability to understand the problems and translate data into actionable insights.

Technologies

Hard skills are still essential for data scientists. Technologies to focus on include Excel for getting started in analytics, SQL for querying data, and Python or R for analysis, data visualization, and machine learning.

Technical skills

Important technical skills include data collection and cleaning, building dashboards and reports, data visualization, and building models for statistical inference and machine learning.

Data thinking, data manipulation, and visualization

Employees must have data intuition to understand how data is generated, how it is stored, and what it feels like. They also must have the ability to conduct statistical data visualization and modeling.

Statistical thinking skills

They must have statistical intuition to be critical of their data in order to get the most out of it. Data intuition requires thinking probabilistically and being aware of statistical biases.

Machine learning skills

They must also understand the concept of machine learning, which is required to conduct supervised and unsupervised learning, deep learning, and AI.

Let DataCamp help upskill your workforce. We offer the best platform to learn data science and analytics online. Our curriculum spans Rogati’s entire Data Science Hierarchy of Needs and we are constantly adding to it. If you are a business leader, learn more at datacamp.com/business or click here to schedule a demo of our platform. Click here to view the full webinar recording.

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