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Thursday, December 9, 2021

Key Data Terms Every Analyst Should Know

Becoming a data analyst continues to be a promising career path. Over the last decade, big data tools have advanced significantly, providing new insights to businesses in nearly every industry. Although technology does most of the heavy lifting, skilled data analysts are needed to make sense of the information that emerges. This is a highly technical field requiring skills in programming, statistics, machine learning, key data terms, and “soft” skills like communication. 

Advanced education is often needed for a job as an analyst. Getting into the field of data analysis can be challenging, and you need to keep your skills sharp as you advance your career to stay competitive. If you’re working in the industry or you’re planning to embark on a new career, there are some key data terms you need to know. 

Key Data Terms Required for Analysts

The rate of data collection is exploding and the field of data science has become more standardized as organizations leverage these tools to their fullest potential. New data mining and processing methods have increased the need for a new “language” among analysts.  

Analysts need to be able to communicate quickly and clearly with one another. Key data terms that are often known as “buzzwords” have emerged in the last decade to help analysts work together efficiently so they can process more data. 

Some of the most important words and phrases data analysts use include the following: 

  • Big data – extremely large datasets that an organization may collect and use for gaining strategic business insights. Analysts are needed to extract meaning from these datasets. 
  • Artificial intelligence – technology that is designed to mimic human thinking. 
  • Machine learning – a type of artificial intelligence that can learn the “rules” of a dataset and apply them to unknown data, refining its knowledge over time as it processes more data. 
  • Predictive analytics – a blanket term used to describe a process of applying historical data to predict future outcomes. 
  • Predictive modeling – the specific techniques used to “train” the machine on historical data to perform predictive analytics. 
  • Data mining – an often-misunderstood term that refers to mining large datasets for insights and patterns. Many believe that this term refers to collecting the datasets, rather than analyzing them.

These are key data terms that are required for any data analyst, but the list certainly goes beyond these basic phrases. Those working in the industry will need an in-depth knowledge of the technology behind these terms and should be able to confidently discuss the process in detail. Depending on their role, a data analyst will need to know many additional terms. 

Big Data, Statistics, Data Mining, and Analytics Terminology 

Because the industry is relatively new, there is some overlap between different roles and there can be some confusion about what different terms mean. “Big data” is pretty basic and self-explanatory, but what about terms like business analytics vs. data science? Is there a difference? 

Well, yes. Data scientists are most often tasked with setting up systems for data mining later on. Analysts then use their algorithms to gain strategic insights with business analytics. Data scientists may be in charge of the whole process from start to finish, or they may work with a team of analysts. 

Those who want to work in the industry must decide where they want to work within the entire process of data analysis. Different roles require different levels of education and slightly different skill sets. However, depending on the size of an organization, an analyst might find themselves in a “jack-of-all-trades” role in sorting, preparing, and leveraging data. Most roles involving data analysis involve some key skills, such as proficiency with SQL and programming ability. 

Sometimes, the most difficult part of learning key data terms is determining the difference between terminology that can have very similar meanings. Fortunately, there is a growing number of high-quality data analysis programs at universities across the United States. Pursuing a relevant degree is the easiest way to gain the knowledge and skills needed for a successful career in data analysis. 

Picking the Jargon Up as You Go 

While it’s important to know the basics as soon as possible, you’ll learn a lot as you build a career as a data analyst. Don’t worry too much about it when you’re first starting out—it can get overwhelming. 

With that said, it’s always good to go back and ensure that there are no gaps in your knowledge. These key data terms make up the foundation for data analysis and are essential for proper communication within the industry.


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Kiara Dawson
Kiara Dawson comes from an Engineering background, with a specialization in Information Technology. She has a keen interest and expertise in Web Development, Data Analytics, and Research. She trusts in the process of growth through knowledge and hard work.

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