Every industry has its own jargon, a set of words or phrases that are instantly understood when relayed to another from the same industry, even if the meaning is lost on the average person. Data science is no different. The problem is that the lingo used by a data science team is constantly in a state of flux.
Technology is advancing very rapidly, as anyone with a mobile device or social media account can agree. The buzzwords of the data science industry must also change rapidly. As new concepts are developed and realized, the data science teams, vendors, and any business that needs data analysis must come up with new words to match the new technologies.
Who Needs to Know These Buzzwords?
People from many different backgrounds will benefit from keeping up-to-date with the latest data science buzzwords. Those who work directly with developing new technology and the data analysts have to keep up, but they are likely to stay familiar with the changing lingo naturally—they are the ones creating the jargon. Also, anyone who is looking for work in the data science field should be aware of the current terminology.
Others who need to stay up-to-speed may not seem so obvious. For instance, HR professionals need to be fluent in data science buzzwords so that they know how to write up an accurate job description. Frequently, listings for the same type of job will be listed under different headings because the HR departments of different businesses struggle to keep up with the rapidly changing jargon.
As e-commerce grows ever more important and technology transforms the health care industry, these new buzzwords are going to continue to increase. Knowing what these words mean and the contexts in which they are used is vital for anyone who works in, or adjacent to, the fields of business intelligence, data analysis, and computer science.
The following is a list of 12 data science buzzwords that are frequently used in the data science industry today.
12 Data Science Buzzwords Every Data Professional Should Know
1. Big Data
Every time you search the internet, make a credit card purchase (either online or in-person), use social media, make a phone call, send a text, or complete any other task that uses a digital connection, you leave a data footprint. Big data is the term used to describe the massive amount of data generated by these footprints.
In 2001, an expert in data science working at Gartner, a premiere research firm, came up with the 3 Vs to describe big data. They are:
- Volume. The tremendous amount of data gathered in these repositories is mind boggling. In 2020, big data was estimated to contain over 44 zettabytes, or 44 trillion gigabytes, of data.
- Velocity. Velocity is the incredible speed needed to capture the data streaming from every connected device around the world simultaneously.
- Variety. This data comes in a wide variety of forms, including video, audio, images, texts, and real-time data. Each of these forms requires different methods and means to be processed and mined for useful information.
2. Artificial Intelligence
Artificial intelligence, or AI, has long been a term used by science-fiction aficionados. Now it has come out of the world of fiction and become reality. A greater understanding of the human brain’s neural networks has led to the creation of machine learning algorithms that, with varying levels of success, mimic the function of parts of the brain. This has led to the concept of unsupervised learning.
3. Unsupervised Learning
Unsupervised learning is a subset of machine learning that allows the machine to process, adapt and understand unlabeled data based on its own experiences, or things it has been taught so far. This is a major step towards cognitive computing in which a machine becomes capable of making its own complex decisions.
4. Natural Language Processing
Another subset of machine learning is natural language processing or NLP. This is the process by which machines, through the use of NLP algorithms, can interpret the intricacies of human languages. It’s used for things like autocorrect on your smartphone, filtering of emails, and voice recognition.
5. Deep Learning
Deep learning is the most complex subset of machine learning. Machine learning uses binary coding to interpret and reproduce information, but it’s limited in what it can accomplish. Deep learning uses an artificial neural network to use logic to interpret data and come up with unique solutions, much like the human brain.
6. Decision Tree
A decision tree is an algorithm used to allow a machine to make decisions. Each piece of data is evaluated and, based on previous input, predictions are made as to what the outcome of each decision will be.
7. Predictive Analytics
Predictive analytics use data to make predictions about current data, or what will likely happen next, based on previously input data. It’s usually used in concert with prescriptive analytics, which provides an appropriate course of action based on the predictions.
Regression is used in predictive analysis to locate and monitor continuous streams of quantitative data. It can be used to find relationships between data and predict future values of similar data.
Classification is also used in predictive analysis to identify and label similar data from different sources. These models take historical data and use it to classify incoming data.
10. Descriptive Analysis
Descriptive analysis is used to find correlations between different groups of data. It is used to take raw data and summarize it into groupings that are easier to interpret and understand. It’s widely used in business intelligence and by other data analysts to predict trends.
11. Internet of Things
Internet of Things or IoT, is the term used to describe the network of things that contain software, sensors, or other means to connect to other devices containing these technologies through the internet. These devices remain connected and may continuously transmit data back and forth without human intervention.
12. Real-Time Analytics
Real-time analytics is the process, frequently through the use of AI, that allows data analysis virtually as soon as the data arrives. This allows for almost immediate translation and interpretation of the data. It is used by businesses to allow them to see trends and make any necessary changes very quickly.
Whether you are a newbie to the sciences of data analytics or have been working in the field for years, or if you are one of the many vendors or HR personnel that work with them, it’s imperative that you keep yourself up-to-date on the current data science buzzwords and data analytic tools. If you don’t have a clear understanding of them, you may find yourself a step behind those who do.
Is data science the right career for you?
Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.
Check out Springboard’s Data Science Career Track to see if you qualify.
Not quite ready to dive into a data science bootcamp?
Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.