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data science for business - data science for employees

Why Data Science for Employees Matters

Over the next five years, global data creation is projected to grow to over 180 zettabytes. The sheer volume of data being created is an opportunity, and a company’s ability to unlock that data and use it to its full potential can create a significant competitive advantage.

For 2021 and beyond, the ability to work with data is an incredibly valuable skill that’s no longer limited to data scientists.

While a data scientist gathers, uses, and gains insights from data, utilizing a wide range of skills, not every employee needs to become a full-stack data scientist to maximize the potential of data.  Data science for employees across the organization offers a way to ensure data is being tapped into in all departments. 

Here’s what your employees need to know about data science in order to excel in their day-to-day roles. 

1. Data Science Fundamentals

Being able to leverage the power of data science starts with having a basic understanding of key concepts as they apply to the field, such as statistics, probability, dimensionality reduction, central tendency, hypothesis testing, and sampling theory. 

Employees should also be familiar with common tools and technologies as well as the difference between data science, data engineering, business analytics, and related disciplines. 

The level of knowledge required depends on their position, but data science for employees should focus on helping them be aware of how data informs business practices and conversant in data science fundamentals.

2. Machine Learning and Data Modeling

Artificial intelligence (AI) is behind much of the technology we use every day. Machine learning-based tools automate and optimize processes to improve the way companies run. Data modeling is what drives machine learning by training machines using mathematics and algorithms to categorize and make predictions.

Employees should know that machine learning is an iterative process that continuously improves as it runs data and analyzes results. They should also be aware that the answers are in the data sets, not outside of the data.

Most of all, it’s important for your employees to be well-acquainted with machine learning’s possibilities and limitations.

3. Programming Skills

While fundamental data science for employees doesn’t require everyone to become a skilled software engineer, lacking basic programming skills and knowledge holds employees back.

We all need to be able to communicate with the computers or machines we use every day in the workplace. Learning a coding language, such as Python, not only has practical application but also promotes problem-solving and critical thinking skills. 

When data science is in action — making predictions, solving problems, promoting efficiency, and more — Python programming is what’s making it happen. Understanding how programming works, the capabilities of coding, and how it applies to data science enables employees to push the envelope as they look for answers, identify opportunities, and promote efficiency and growth.

4. Data Manipulation and Analysis

Data manipulation, also known as data wrangling, refers to the process of making sure the data you’re working with is clean so it can be transformed into a format that can be efficiently analyzed. Data analysis involves a series of steps where the goal is to discover useful information to inform conclusions and support decision-making. 

Knowing how to work with databases is key. Nearly every database uses Structured Query Language (SQL), so it’s a valuable skill for any employee who uses databases. With SQL, they can share and manage data, query, update, and recognize data, and create and modify data structures. 

Companies rely on SQL for data wrangling and preparation, dealing with Big Data tools, and performing analytics operations with data stored in relational databases. Data-driven organizations need employees to know how SQL works and how to put it to work.

5. Data Visualization

Data visualization is the graphical representation of data. It’s how data is translated and simplified through charts, graphs, or other visuals, marrying data science, communication, and design to offer meaningful and intuitive insights into complicated data sets.

When it comes to data science for employees, knowing how to create, read, and use data visualizations will ensure clear communications while helping people ask better questions, and make well-informed decisions.  

6. Communication and Storytelling Skills

Effective communication is more than a soft skill. Combined with data science, employees’ communication and storytelling becomes more clear, persuasive, and impactful. Being able to present a problem, solution, strategy, or idea using data to back it up makes all the difference. 

Data tells a story and speaks for itself, but providing accompanying interpretations, insights, and context is what will move the needle. When your employees use data to communicate, they’re able to convey and highlight what’s most important, clarify confusion, and influence critical decisions. 

Data Science For Employees Matters Now More Than Ever 

With the amount of data created every single day the truth is that data science is not just for data scientists. You can think of data science for employees as an essential skill to thrive in any role.

All modern, competitive organizations have data-driven practices and technology which means employees and employers can benefit from deepening data science skills across the organization.

Help your organization by helping your employees upskill in data science. Explore Udacity’s Data Science Nanodegree and Enterprise Offerings for more information. 

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Jennifer Shalamanov
Jennifer Shalamanov
Jennifer is a content writer at Udacity with over 10 years of content creation and marketing communications experience in the tech, e-commerce and online learning spaces. When she’s not working to inform, engage and inspire readers, she’s probably drinking too many lattes and scouring fashion blogs.