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Data Science

30 Data Science Terms Explained in Plain English (with Examples)

10 minute read | March 21, 2019
Alexander Eakins

Written by:
Alexander Eakins

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If you’re just starting out with data science, you’re likely learning a lot of new terminology. From Hadoop to munging, it can be hard to keep it all straight. That’s where a comprehensive data science glossary comes in. We’ve compiled a list of data science terms below, complete with input from experts in the field.

30 Popular Data Science Terms

Let’s start at the beginning.

Data science

At its essence, data science is a field that works with and analyzes large amounts of data to provide meaningful information that can be used to make decisions and solve problems. Data science includes work in computation, statistics, analytics, data mining, and programming.

Related: What Is Data Science?

Data scientist

An analytical data professional with a high degree of technical skill and knowledge, usually with expertise in programming languages such as R and Python. Data scientists help businesses collect, compile, interpret, format, model, make predictions about, and manipulate all kinds of data in all manner of ways. They’re experts at both construction and deconstruction. Even though the role of data scientist is relatively new, it’s in high demand and pays well.

DJ Patil, who built the first data science team at LinkedIn before becoming the first chief data scientist of the United States in 2015, coined the modern version of the term “data scientist” with Jeff Hammerbacher (Facebook’s early data science lead) in 2008.

Patil has put it this way: “A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.”

Data analyst

An interpreter of data who typically specializes in identifying trends. They’re similar to data scientists, sans the coding experience. One way to think about data analysts is that they’re junior data scientists on their way to becoming full-fledged data scientists.

Martin Schedlbauer, associate clinical professor and director of Northeastern University’s information, data science, and data analytics programs, explains, “Data scientists are quite different from data analysts; they’re much more technical and mathematical. They’ll have more of a background in computer science.”

Related: Career Comparison: Data Analyst vs. Data Scientist

Business analyst

Like a data analyst, but more invested in the actionable implications of data to promote the progress and development of a business.

A business analyst will recommend action based on his or her interpretation of data, such as whether or not a business should continue to sell a particular product. Business analysts can use the work of data scientists to communicate the business side of the data to the ultimate decision-makers.

Related: Career Comparison: Business Analyst vs. Data Analyst

Data engineer

Anyone who designs, QAs, and maintains the systems that data scientists employ daily. Whereas a data scientist might be focused on data analysis, a data engineer focuses more on data preparedness.

“If a data scientist’s job is to analyze and translate data into meaningful and contextual data, it is the data engineer’s job to ideate and build up the software architecture that will enable it,” says Jamie Cambell, former Google security engineer and founder at Gobestvpn.com.

Likewise, they ensure that quality data comes through the pipeline. After all, iron cannot be refined into gold, to paraphrase Mark Twain.

The data engineer is the pit to the race car driver. They make sure data scientists have a well-oiled data pipeline to perform their jobs adequately.

Related: Career Comparison: Data Engineer vs. Data Scientist

Data governance

The management of the overall quality, integrity, relevance, and security of available data.

The semantics fit here. It’s not a lot different from governing a place. To govern is to “conduct the policy, actions, and affairs of (a state, organization, or people,” according to Google’s dictionary. Replace a state, organization, or people with data, and that’s pretty close.

Data governance usually involves a governing body that validates the relevance of data and maintains the status quo to the degree that it prevents disruption of data quality, integrity, or security.

Data set

Quite simply, a collection of data, particularly one that is specifically structured. They can be small and simple to work with or large and complex. Yelp’s popular data set, for example, includes over 1.2 million business attributes like hours, parking, availability, and ambiance.

Related: 19 Free Public Data Sets for Your First Data Science Project

Data mining

A process that data scientists employ to find usable models and insights in data sets. They use numerous techniques to accomplish this task such as regression, classification, cluster analysis, and outlier analysis.

Related: Data Mining in Python: A Guide

Data visualization

Any attempt to make data more easily digestible by rendering it in a visual context. Data visualization includes charting, graphing, infographing, and can even include cartooning—in generic use cases.

Data modeling

Modeling is all about turning data into predictive and actionable information. “Building models that can predict and explain outcomes,” says Daniel Jebaraj, vice president at syncfusion.com, a company that provides enterprise-grade software to companies for such purposes as data integration and big data processing.

Often data modeling involves the process of visually documenting complex data using text and symbols.

Data wrangling

The process of formatting or restructuring raw data to suit specific needs or increase its decision-making power (sometimes referred to as data munging). Think in terms of livestock wrangling, if it helps. To wrangle livestock is to herd or move animals to a specific purpose. Rather than livestock, data scientists have, you guessed it, data. To rein in that raw data, whether for legibility or something else, it needs structure.

“This is typically messy work and takes time. Without adequate preparation, results are difficult to use,” Daniel Jebaraj says.

Data scientists often spend somewhere between 50 and 80 percent of their time data wrangling.

Related: A Comprehensive Introduction to Data Wrangling

Big data

Big data comes from Moore’s Law, a theory that computing power doubles every two years. This has led to the rise of massive data sets generated by millions of computers.

Put simply, big data is a collective term that describes data that is too large to fit on a single computer. Conventional tools like SQL and Excel are typically unable to handle big data, so new ones have been developed to take their place.

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Algorithm

A series of repeatable steps, usually expressed mathematically, to accomplish a specific data science task or solve a problem. An important part of a data scientist’s job is his or her ability to recognize an algorithm’s suitability for certain tasks, as it’s impossible to rely on one algorithm as a panacea to all problems.

A few commonly used algorithms in data science include: linear and logistic regression, Naive Bayes, and KNN (K-Nearest Neighbors).

Artificial intelligence

Well-known by its acronym, AI is the apparent ability of machines to act “intelligently” and has become an increasingly popular and useful area of computer science.

The definition of intelligence is broad here, and there’s disagreement about what constitutes machine intelligence. According to Science Daily, the modern definition of AI is “the study and design of intelligent agents,” agents being a system that studies its environment and acts in the interest of maximizing chances of success.

AI is responsible for everything from your favorite triple AAA video game NPCs to Facebook’s algorithms to single out and ban inappropriate content.

Machine learning

The computational process wherein a machine “learns” and adjusts its behaviors based on feedback from data. Usually manifesting as an adaptable algorithm, machine learning helps computers predict outcomes without explicit human input.

“Machines learn a function from data without the specific function being explicitly programmed. Given certain inputs what is the function that produces observed outputs? Such a function should also be able to handle previously unseen data (generalize),” adds Daniel Jebaraj.

As more data becomes available, machine learning uses statistical analysis to adjust and update behavior to more accurately predict the future.  

Machine learning engineer

A data scientist does the statistical analysis required to determine which machine learning approach to use, then they model the algorithm and prototype it for testing. At that point, a machine learning engineer takes the prototyped model and makes it work in a production environment at scale.

A machine learning engineer isn’t necessarily expected to understand the predictive models and their underlying mathematics the way a data scientist is. A machine learning engineer is, however, expected to master the software tools that make these models usable.

Related: How to Become a Machine Learning Engineer

Deep learning

A branch of machine learning that attempts to mirror the neurons and neural networks associated with thinking in human beings. It’s the enemy of many a dystopian sci-fi novel where robots become smarter than humans and cause the downfall of mankind. We’re not quite there yet, but recent advances in artificial intelligence employ deep learning technology for speech recognition, translation, and image recognition software.

Supervised learning

A common branch of machine learning in which a data scientist trains the algorithm to draw what he or she believes to be the correct conclusions.

“It’s similar to the way a child might learn arithmetic from a teacher,” writes Nikki Castle in this Datascience.com article.

This is distinctly different from unsupervised learning, which does not rely on human guidance. An example use case for supervised learning might include a data scientist training an algorithm to recognize images of female human beings using correctly labeled images of female human beings and their characteristics.

Unsupervised learning

A branch of machine learning where the algorithm does not rely on human input, and is, instead, self-learning. This more closely resembles what some experts call true artificial intelligence.

This form of machine learning is extremely complicated and is not always the go-to for simpler tasks. However, it can be used to solve complex problems that people would not normally undertake, according to Nikki Castle.

Whereas the supervised algorithm would accept and use the labels assigned to it to classify female human characteristics, an unsupervised algorithm would learn the differences on its own, free of bias, and assign its own labels to differentiate.

Reinforcement learning

An area of unsupervised machine learning where the machine seeks to maximize reward. The machine, or “agent,” learns through trial and error as well as reward and punishment.

If you’ve heard of positive and negative reinforcement, those same principles are applied here. Reinforcement learning problems are usually explained in terms of games. Let’s take chess, for example. The machine’s goal is to win at chess. It’s positively reinforced when it makes moves that win material, such as capturing a pawn, and negatively reinforced when it makes moves that lose material, such as having a pawn captured. Combinations of these rewards and punishments result in a self-learning machine that improves at chess over time.

API

An acronym that stands for application programming interface. APIs provide users with a set of functions used to interact with and deploy the features of a specific application or service.

Facebook, for example, provides developers of software applications with access to Facebook features through its API. By hooking into the Facebook API, developers can allow users of their own applications to log in using Facebook, or they can access personal information stored in Facebook databases, such as date of birth or workplace.

Python

An object-oriented programming language often used in data science because users have developed an extensive array of tools applicable to the field. Python is free to use for commercial or personal projects, and it’s often commended for its learnability for programmers and non-programmers alike.

Related: An Introduction to Machine Learning in Python

R

An open-source language and environment for statistical computing and analysis. Like Python, R is often used in data scienceand knowledge of it is often expected for job applicants. Sometimes considered more difficult to learn than languages like Python, R shines most brightly for its graphical and plotting capabilities and its many data science-driven packages.

Ruby

A scripting language that is also popular with data scientists, though not on the same level as Python and R. It does not contain the volume of specialized libraries available in R and Python, and reasons for using it are mostly historical.

SQL

An acronym that stands for structured query language, this programming language is designed to interact with databases. Of course, where databases are involved, data scientists aren’t far away. SQL is another must-learn language for data scientists in the making.

Excel

One of the most used spreadsheet applications on the market. There’s no way you haven’t come into contact with Excel. It’s used in data science for obvious reasons, but it’s used in practically every professional environment and, at the very least, a familiarity with it is expected in any job you’ll encounter. Excel does great with crunching numbers; visualizing data; reading, importing, and exporting CSV files commonly used in data science; and much more.

Hadoop

An open-source software framework that allows data scientists to process big data using clusters of hardware running simple programming models. Many herald Hadoop as a solution to big data problems. It allows you to manage much more data than you can on a single computer.

Pandas

An open-source software library for Python. The library is widely used in the data science community for data manipulation and analysis because it’s free and distributable under the BSD license.

It is much quicker to process larger datasets than Excel, and it has more functionality. You can clean data by applying programmatic methods to the data with pandas. You can, for example, replace every error value in the data set with a default value, such as zero, in one line of code.

Decision tree

A tool of data scientists and related professions to visually lay out decisions and decision making. As the name suggests, the visual model for the decision-making process is a tree. It’s widely used in data mining and machine learning.

Unstructured data

Any data that does not fit a predefined data model. Often this data does not fit into the typical row-column structure of a database. Images, emails, videos, audio, and pretty much anything else that might be difficult to “tabify” might constitute examples of unstructured data. 


The field of data science is wildly complex and deep. These are just some of the data science terms you’ll encounter often, and they only represent a high-level discussion of the field. If you delve further into each of these data terms, you’ll find even deeper topics for discussion. Hopefully, this serves as a primer to pique the interests of aspiring data scientists, and a reference for those looking to keep things straight.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Alexander Eakins

Alexander is a freelance technical writer and programming hobbyist.