What is Data Analytics?

The roots of data analytics date back to ancient Egyptian times, when statistical measurements were used to build the pyramids. However, with the advent of computers, data analytics expanded dramatically in its use and popularity. The past 50 years have seen rapid developments in data analytics, with the introduction of data warehouses, business analytics, cloud computing, and other modern technologies. In this overview, you’ll learn more about what data analytics is, what it can do, who uses it, and how to learn it so you can determine how to add this skill to your professional toolbox.

What Can You Do with Data Analytics?

The field of data analytics has many real-world applications across industries and professions. At the core of the data analytics process is the goal of using data to make better decisions. Companies rely on the data's insights to make more informed business decisions, which can help their organization grow, increase revenue, or better serve employees and customers.

Data analytics can help with marketing campaigns by eliminating the bulk of the guesswork in planning, creating content, and developing products. It also provides an overview of customers, which can help organizations better meet their needs. By better understanding your audience and what they want, targeting them with more pointed marketing initiatives and campaigns is easier.

Another widespread use of data analytics is to improve customer service. When data is analyzed, it reveals important insights about customers, which makes it possible to offer them more personalized service. Data can provide information on how customers prefer to communicate, what interests they have, what concerns they hold, and what products they look at most often. In addition, having data stored in a central location allows an organization’s customer service team to be on the same page as their marketing and sales teams.

Another use of data analytics is to improve the efficiency of operations within an organization. Data can reveal weaknesses in operations, as well as opportunities to streamline various processes. This not only helps the business perform more efficiently but can save money over time. For example, if you have a clearer idea of what your target audience is looking for, you won’t need to waste time and resources designing ads or writing content that doesn’t directly target their interests. This leads to less wasted money, as well as better campaign results.

How Do You Get Data Analytics Tools? How Much Do They Cost?

When working in data analytics, you’ll rely on several different tools, computer programming languages, and applications to help organize, analyze, and visualize the data.

Microsoft Excel is a spreadsheet application available as part of the Microsoft 365 suite. This industry-standard tool has a range of applications in data analytics, from quickly performing repetitive tasks to visualizing data in the form of charts and graphs. This app is currently available to download from Microsoft. Price plans start at $6/user/month and include access to other Microsoft apps, like Word, PowerPoint, Teams, and OneDrive. Microsoft also offers a free Excel trial.

Those who work with big data often use programming languages to do so. Python, a multi-functional programming language, is available as a free download. This language’s easy-to-learn syntax and extensive set of free libraries make it a go-to for Data Analysts and Data Scientists. Python also helps users create data visualizations, such as charts, graphs, and interactive plots. Its built-in analytics tools make it easy to spot correlations and patterns in large datasets, which makes it a vital tool for data analytics.

Another tool most Data Analysts use is SQL or structured query language. SQL is used to communicate with the data stored in relational database systems. Because it’s based on English syntax, SQL is considered easy to learn, even for those who don’t come from a technical background. Regarding price, you can download SQL Server 2019 Developer for free from Microsoft. In addition, a free trial of SQL Server 2022 is also available.SQL Server Standard Edition Server Licensing costs $931, SQL Server Enterprise Edition costs $7,128 for each core, and SQL Server Standard Edition costs $1,859 per core.

One of the most popular data analytics and visualization tools available is Tableau. This business intelligence platform helps users create interactive visualizations to display their data findings. This program is accessible to users from all backgrounds. It has applications for finding patterns and trends within data and designing visual depictions of these numbers. These data visualizations can then be shared with various stakeholders so that the team can make better business decisions. Tableau Public is available for free, and Tableau Desktop currently includes a free trial.

What Are the Benefits of Learning Data Analytics?

The driving principle behind data analytics is solving problems. These problems can affect entire organizations and their employees and customers. A background in data analytics empowers you to think analytically when approaching these problems, a skill that extends beyond the professional sector. In addition, this field requires individuals to have excellent communication skills, leadership training, and attention to detail, all transferable skills that can help in all facets of life.

The big data boom has created more of a need than ever before for those with skills in data analytics to help organizations understand their data to capitalize on the insights these numbers reveal. For this reason, Data Analysts are in demand in nearly every industry that works with data. As more businesses and sectors begin to see the value in collecting data and using it to fuel their decision-making process, the more value they see in the data analytics process. This is why pursuing a career in data analytics can lead to high-paying professional options in many industries.

Read more about why you should learn data analytics.

Data Analytics Careers

Because more data is being created than ever before, and because so many organizations realize its importance, the field of data analytics is one of the most popular professions currently available. Those with skills in analyzing data are valued in various industries because they can help organizations translate these numbers into actionable insights.

Data Scientists rely on elements of the analytics process, such as creating algorithms, performing statistical analysis, and running data tests to understand and use data to help an organization’s decision-making process. Machine Learning Engineers also rely on data analytics tools and skills as they design models, algorithms, frameworks, and systems to help machines independently carry out functions.

Those working as Business Intelligence Analysts also use data analytics to help improve how their organization operates. Their job often entails collecting, sorting, and analyzing internal and external data to spot patterns, trends, or issues that require attention. These data findings provide policymakers with insights that can help them devise strategies to improve the business's overall operations.

Data Architects work with data analytics when they create data management frameworks designed to help their organization translate business objectives into a system that can be used across an organization. Those who work as Marketing Analysts draw from their data analytics skills as they work to help their company perform market research, spot purchasing trends, and process customer surveys. Their strategies empower their company to connect with customers more effectively and better connect with existing customers.

How to Learn Data Analytics

One of the most popular and effective ways to learn data analytics is through in-person and live online data analytics classes. These learning options provide an environment where you connect with an expert instructor in real-time, who is there to field questions, offer guidance, and provide feedback. In-person study also provides a classroom with computers with the latest data analytics software and tools installed. Live online study has the added benefit of allowing learners to complete coursework from the comfort of their home or office space. If you want to search for learning options close to home, Noble’s Classes Near Me tool is a great resource to help you choose from different in-person and live online data analytics courses provided by reputable educators.

Suppose you aren’t fully ready to commit to a data analytics class that meets at regularly scheduled times. In that case, on-demand data analytics classes are available from Noble Desktop and other reputable educational providers. You can complete these classes at your own pace and from any location. Some of these resources are free, whereas others require a fee or a platform subscription for study.

Noble also has various free seminars to help anyone interested learn about big data and other data-related concepts. They offer content on topics related to data analytics, such as a free Data Science seminar and an Intro to SQL seminar. These resources provide beginner-friendly instruction that can help you get a feel for whether working with data is right for you.

Read the full guide on how to learn data analytics.

A Brief History of Data Analytics

The field of data analytics is thousands of years old, dating back long before the term “data analytics” was first used. In 18,000 BCE, ancient tribes marked notches onto bones to record their trade activity and supplies. Then, thousands of years later, in 2400 BCE, ancient Babylonians used the abacus to perform calculations and store data. Then, between 100-200 CE, the first mechanical computer, the Antikythera Mechanism, was used for astrological tracking and to keep a record of the Olympic Games cycle.

In 1663, John Graunt completed the first statistical data analysis experiment on the Plague. However, it wasn’t until 1880 and the advent of the Hollerith Tabulating Machine that data management became much easier and faster. This machine was able to take the U.S. Census Bureau’s backlog of data from the prior decade and process it in just months instead of the years it would have otherwise taken,

Modern data storage began in the 1920s. In 1928, Engineer Fritz Pfleumer successfully stored information magnetically on tape. In the 1960s, the first data center was created by the U.S. Government. In 1970, Edgar Codd made the initial relational database system, a framework still used in contemporary data services. When the internet was introduced in the 1990s, relational databases grew in popularity as they tried to keep up with the increasing demand. At this time, non-relational databases emerged to help manage the onslaught of information. Around this time, data mining became popular for uncovering patterns in large datasets.

The first two decades of the twenty-first century led to an even more rapid evolution of the field of data analytics. In 2008, servers worldwide processed more than nine zettabytes of information. 2010 saw the emergence of data lakes. In 2015, deep learning, machine learning, and AI emerged in the field of data science. And from 2015-2020, the number of datasets available for different data types more than doubled.

Comparable Fields

The field of data science is comparable in many ways to the field of data analytics. Data analytics involves asking open-ended questions, then searching for answers using statistical data analysis. When the answers are found, the Data Analysts provide actionable insights designed to improve their organization’s operations and increase their revenue stream. This process relies on data visualization, computer programming, and different forms of analysis.

On the other hand, data science is more concerned with the questions that need to be asked about the data rather than answering these questions. Data Scientists are often involved with open-ended research and data modeling. This provides a foundation for subsequent analyses. Like data analytics, this process entails statistical modeling, data wrangling, and computer programming. Unlike Data Analysts, who typically respond to decision-makers’ requests, Data Scientists are the driving force behind the decision-making process. Learning data science is comparable to data analytics because they both rely on computer programming languages such as Python, R, and SQL, as well as Microsoft Excel skills. However, data science focuses more on machine learning, whereas data analytics involves more data visualization.

Learn Data Analytics with Hands-on Training at Noble Desktop

There’s never been a better time to study data analytics. If you’re interested in learning more about this popular field, Noble Desktop has you covered. Noble offers several data analytics classes for beginners and those with a background in data who want to master specific or advanced tools and skills.

Noble’s Data Analytics Certificate is a project-based course designed to teach students how to collect, wrangle, analyze, and visualize data. Participants complete several real-world projects using predictive and prescriptive analytics to prepare them for careers as Business Analysts or Data Analysts. This course provides all students with one-on-one mentoring, as well as the option of a free course retake to revisit the material.

In addition, Noble’s SQL Bootcamp is a great learning option for learners interested in studying SQL to help with their data journey. This 18-hour class teaches participants how to use PostgreSQL to find information in databases. Those who enroll will learn how to write SQL queries, aggregate data, join tables, and filter results.

For students who want to work with Tableau for data analytics and visualization, Noble’s Tableau Bootcamp is a solid choice. This class is taught by an expert instructor and is available both in-person in Manhattan and in the live online format for those who wish to study remotely. This course takes students on a learning journey, starting with the basics of data visualization. Those enrolled also work with Tableau Public and its various tools for analysis and visualization. By course completion, participants will be able to analyze, filter, structure, and visualize data and publish the results. All students have the option of a free course retake.

Key Insights

• The field of data analytics has been around for thousands of years. In 18,000 BCE, the first records of trade activity were recorded on bones. It wasn’t until the 1920s that people stored large amounts of data on magnetic tape.
• If you are interested in studying data analytics, some of the tools you likely will use are programming languages like Python and SQL, spreadsheet applications such as Microsoft Excel, and data visualization tools like Tableau.
• One of the fields closely related to data analytics is data science. While data analytics is concerned more with asking open-ended questions and finding answers to these questions, data science relies more on the questions that businesses should ask rather than the answers.
• You can receive comprehensive data analytics training by enrolling in an in-person or live online course with Noble Desktop.