What is Data?

Data is defined as facts and statistics gathered for analysis and reference. In this overview, you’ll learn more about what data is, what it can do, who uses it, and how to learn it to determine how to add these skills to your professional toolbox.

What Can You Do with Data?

Data has nearly endless uses. Data measures the effectiveness of marketing campaigns, determines budgets, forecasts company growth, and highlights industry trends, trends within an organization, and more. Understanding data science and data analytics can help business owners better manage their organizations, help marketing teams become more efficient and effective, assist business leaders in determining goals, or help launch your career as a Data Analyst or Data Scientist. 

Data science jobs include Python Engineers, Data Journalists, Data Engineers, Business Analysts, and more. Data Scientists create machine learning models that help process raw data, giving a company better insights. Analysts interpret and track this data to continuously improve processes. Data helps organizations make the most of every minute and every dollar, which is one reason Data Scientists and Data Analysts are in such high demand.

How Do You Get Data Tools? How Much Does it Cost?

Business Owners, Managers, Marketers, Accountants, and just about every professional uses data in some way. Whether you are budgeting for a department, calculating market share, projecting the next quarter’s sales, or highlighting trends within the organization, data is crucial in determining a company’s health, direction, and growth. Powerful data tools make it possible for Data Scientists, Data Analysts, and others to collect large amounts of data from multiple sources, filter and sort that data, draw conclusions, and create data visualizations to communicate these findings to stakeholders.

Perhaps the most famous data collection tool is Microsoft Excel. Excel is a spreadsheet application that organizes data in columns and rows. Users can manipulate this data through formulas. Businesses and organizations often have access to Excel through a Microsoft 365 subscription. Annual business subscription plans start at just $6 per user per month for web and mobile versions of Office software. Plans that include desktop applications start at $8.25 per user per month with an annual subscription. The highest cost plan is $22 per monthly user with an annual subscription. Microsoft offers a free trial of these plans. Excel can be used on Macs and PCs, making it ideal for businesses using multiple computer brands. OneDrive, SharePoint, and Teams allow teams to collaborate on Excel files from different points in the Microsoft 365 app collection.

Google Analytics is a tool that provides insights about your website or app. You can track your advertising ROI, social media analytics, and more. You can install Google Analytics for free.

Data Scientists use the Python programming language to visualize complex data sets and to create predictive machine learning models. Python is a free, open-source coding language users can install at no cost. Python data visualization tools like Matplotlib and Seaborn are also free to install. Tableau, another popular data visualization tool, requires a paid subscription starting at $70 per user per month, but a free version called Tableau Public is available. 

What Are the Benefits of Learning Data?

Why should you learn data? Data informs business decisions by displaying the numbers behind the business. Data reveals how much money a business spends, how efficient company processes are, the effectiveness of marketing efforts, the cost to obtain a customer, how much revenue the company has coming in, and more. Reliable data is crucial to any organization’s health and success, whether the organization is a multinational company or a local nonprofit.

Data science uses scientific methods, systematic processes, and algorithms to filter, sort, organize, and manage raw data from numerous sources. Data Scientists and Data Analysts are in high demand and have high earning potential. The U.S. Bureau of Labor Statistics found the 2021 median pay for Data Scientists was $100,910 per year, with anticipated job growth of 36% over the next decade. Even if you do not wish to pursue a career as a Data Scientist or Data Analyst, data plays a critical role in marketing, project management, team leadership, and more. 

Read more about why you should learn data.

Data Careers

Data is used by Marketers, Business Owners, and more, so learning essential data tools and skills can benefit anyone. Advanced data science and data analytics skills can launch your career as a Data Scientist or Data Analyst. 

What does a data science career entail? Data science job titles include Data Scientist, Machine Learning Engineer, and Data Engineer. A Data Scientist collects, organizes, and analyzes data to inform decision-making. This data provides crucial insights into the company’s historic, present, and future success by tracking budgets, projecting earnings, detecting trends, and more. Data Scientists earn anywhere from $120,000 to $140,000 on average. Machine Learning Engineers use computer programming and data science to develop systems that automatically learn and perform tasks through artificial intelligence. Machine Learning Engineers earn around $120,000 annually. Data Engineers format data infrastructures that Data Scientists use in their analysis. A Data Engineer earns an average of between $120,000 and $130,000 per year. 

A Data Analyst collects, processes, and analyzes data, then translates that knowledge into insights and actionable recommendations. Data Analysts can choose to work in almost any industry, including government, healthcare, finance, retail, and tech. Data Analysts in metropolitan areas earn around $100,000 annually.

How to Learn Data

Live classes include in-person and live online courses. Both types of training are led by an expert instructor, which offers several benefits. Instructor-led courses allow you to have your questions answered on the spot and allow for real-time feedback. In-person classes allow you to network with local professionals, while live online classes offer the convenience of remote learning. You can explore in-person and live online data classes to find the course that works best for you.

On-demand/self-paced classes include pre-recorded video courses you can watch anytime. Some video courses include supplemental material such as documentation and assignments. You can use free YouTube video courses to gain a high-level overview of a topic, such as learning data. Learning platforms like Udemy, Coursera, and LinkedIn Learning offer on-demand classes through paid subscriptions. The primary advantage of asynchronous classes is that you can learn on your schedule. You choose the time, place, and pace at which you learn. The main disadvantage of on-demand classes is the lack of instructor access. You may have an instructor you can email or meet with at specified times, but both options require waiting several hours or days for a response. For this reason, many people use on-demand courses as a starting point to better understand a topic, then follow up with an instructor-led course. Explore on-demand data classes to find free and paid training options.

Noble Desktop offers free resources to help you advance your data skills. Noble’s Data Learning Hub includes articles, tutorials, and other resources all in one place. You can gain an overview of data science with the free video seminar Get Started in Data Science and watch other free videos through the Python, Data Science, & SQL YouTube playlist.

Noble also has Learning Hubs for specific data tools and skills, including Tableau, SQL, Excel, and Python. 

You can research and compare multiple in-person and online data class options to find the best training options with Noble Desktop’s Classes Near Me tool.

Read the full guide on how to learn data.

A Brief History of Data Tools

This section provides a brief history of Excel, Google Analytics, Python, and Tableau data tools. 

Microsoft Excel is a spreadsheet application launched by the Microsoft Corporation in 1985 for Apple’s Macintosh computer. In 1987, Microsoft launched a new version of Excel for the Windows operating system. The user interface was redesigned in 2007 to allow for smoother integration with Microsoft’s Word and PowerPoint software. Today, Excel is part of Microsoft 365, which can be purchased as a paid subscription. 

Google Analytics began in 2005 after Google acquired the web analytics program Urchin, which was initially developed in 1998. Google set out to make web analytics accessible to the masses so companies of all sizes could work smarter. Today, organizations around the world use Google Analytics to analyze website and app traffic, track marketing and advertising campaigns, and more.

Python is a free, open-source programming language named after the famous British comedy troupe Monty Python. Guido van Rossum created Python in 1991. Today, the Python Software Foundation manages updates, documentation, and installation of Python. Thousands of programmers worldwide have contributed to Python’s improvement and advancement over the last thirty years. This programming language remains popular because it is easy to use and obtain. Data Scientists use Python for machine learning and automation purposes. 

Tableau is a data visualization software that translates data into graphs, charts, and other data visualization tools. Tableau Software was created in 2003 by Chris Stolte, Christian Chabot, and Pat Hanrahan. Tableau reports that two million data visualization authors today use the free Tableau Public platform.

The Difference Between Data Science and Computer Science

Data science and computer science often get used interchangeably, but there are a few key distinctions between these terms, particularly regarding the professions associated with each. 

Computer science studies how coding languages and software work and how mathematical models can function as interactive tools between people and computers. Computer science careers typically include IT jobs and software engineering. A Computer Scientist may write code, develop websites, or build applications. 

Data science focuses less on how software works than on developing methods for gathering and managing data. Data Scientists may use artificial intelligence and machine learning to create automatic processes for gathering, filtering, and organizing data, detecting patterns, and making predictions. Based on these findings, they then make suggestions to stakeholders, helping businesses discover new opportunities, project future earnings and growth, create detailed improvement plans, and target concrete goals. Data Scientists engage with internal stakeholders and may also study customer behavior to increase customer satisfaction. 

Learn Data with Hands-on Training at Noble Desktop

Noble Desktop offers hands-on training in data science and data analytics. These courses are led by expert instructors and include hands-on projects, small class sizes, and free retake options. Noble offers a Data Analytics Certificate and Data Science Certificate. Both certificate programs include one-on-one mentoring, setup assistance, flexible payment options, and career preparation. 

You can also focus on learning specific data science or analytics skills through classes and bootcamps. Learn how to write SQL queries, join tables, aggregate data, and filter results with SQL Bootcamp. The Tableau for Data Visualization course shows you how to convert raw data into interactive visualizations. 

You can explore multiple data analytics and data science training options here.

Key Insights

• Data is defined as statistics and facts gathered for reference and analysis. 
• Data reveals the current state of an organization, highlights trends, and helps businesses make informed decisions regarding goals, objectives, budgets, and more.
• Data tools include free tools like Google Analytics and Python programming and paid tools like Tableau and Microsoft Excel.
• Google Analytics provides insights on website traffic and app usage. Google Analytics is free to use.
• Data Scientists use Python programming to create automated processes for gathering, storing, organizing, and drawing conclusions from data. Python is a free, open-source programming language that you can install and use at no cost. 
• Tableau is a data visualization tool that translates data into graphs, charts, and other visuals. Tableau Public is a free tool. Paid Tableau subscriptions start at $70 per user per month.
• Microsoft Excel is a spreadsheet application included in Microsoft Office 365. It is used to format, sort, filter, and manipulate data. With an annual commitment, paid Office 365 subscriptions start at $6 per user per month.
• Data is used daily by almost any professional, but advanced data gathering and analysis primarily involve Data Scientists and Data Analysts.
• You can receive comprehensive data training through an in-person or live online course with Noble Desktop.