Increased Internet speeds and advanced technology means data science is high in demand. According to Glassdoor, a career as a data scientist is the third-best job in the United States for 2022. This increase in popularity means that all IT professionals, and aspiring professionals, should be familiar with our list of data science terms.
For those looking to become a data scientist, in-depth knowledge of both basic and advanced data science terminology is vital. Our glossary of data science terminology will act as a data science terminology cheat sheet of basic and advanced terms as you start your journey as a data scientist.
What Is Data Science?
Data science is a multidisciplinary field that brings computer science, math, and statistics together. While the field of data science uses techniques and models from deep learning, machine learning, and other areas of artificial intelligence (AI), these specialties are not the same.
Data scientists have strong computer science skills, advanced knowledge of statistical models, and strong programming skills. They use various analysis methods in order to manipulate, interpret, and present solutions to complex problems. Data scientists use graphical models and data visualization tools to present results.
Who Uses Data Science Terminology?
Data analysts, data engineers, and data scientists most commonly use data science terminology. These jobs often involve presenting data, therefore, you must use and understand data science terminology correctly to present your findings clearly.
List of Data Science Terms: Things Every Data Scientist Should Know
- Approximation error
- Bayesian network
- Big data
- Business intelligence
- Confidence interval
- Cluster analysis
- Data cleaning
- Data integration
- Deep learning
- Dependent variable
- Descriptive analytics
- Linear regression
- Logistic regression
- Machine learning
- Neural networks
- Nonparametric model
- Predictive models
- Response variables
- Standard error
Glossary of Data Science Terminology: 5 Common Data Science Terms
Data science incorporates concepts and techniques from various disciplines such as computer programming, information systems, math, and science. Below is a glossary of data science terminology that will give you a headstart in learning core data science concepts.
Cluster analysis uses an unsupervised learning algorithm to group data points based on similarities with no outcome variable. This shows patterns within clusters, between clusters, and the cluster groups that are most similar to each other.
Why Data Scientists Need to Know About Cluster Analysis
Cluster analysis helps data scientists better understand consumer behaviors, such as buying patterns, by revealing key insights into data. This means they can target customers who are similar to each other.
Data cleaning cleans and corrects data from irrelevant values, duplicate values, missing values, and typing errors. The data collected is from multiple sources and methods which are not ready for analysis. This is especially useful when working with advanced statistics.
Why Data Scientists Need to Know About Data Cleaning
Data scientists use data cleaning to prepare data for predictive analytics and enhance the quality of the dataset for statistical models. Analytical software platforms like Tableau and programming languages like Python can be used for data cleaning to assist with advanced analytics.
Linear regression is a statistical method for predictive analysis and structuring regression models. It is used to find a straight line between a known independent and dependent output variable to find the strength in the relationship between the variables. This type of supervised machine learning algorithm requires data scientists to provide input data for the machine to learn patterns and trends from an observed dataset.
Why Data Scientists Need to Know About Linear Regression
Data scientists use linear regression to make predictions for businesses. If you know Python and use the least-squares method, you can implement linear regression. It can help find relationships and patterns for sales, marketing, and consumer behaviors.
A pre-trained model solves a previously similar problem. Pre-trained models help to problem-solve when building new models. Pre-trained models can be fine-tuned to adjust parameters and variables.
Why Data Scientists Need to Know About Pre-trained Model
Pre-trained models help data scientists minimize costs and save time. Using pre-trained models typically results in better and more accurate results. There are various pre-trained models for image classification such as convolutional neural network models.
Visualization is used as a tool to graphically represent data that summarizes insights and findings. Visualization allows you to present data in a more impactful, insightful, and easy to understand way.
Why Data Scientists Need to Know About Visualization
Data scientists use a variety of software such as Microsoft Excel, Bokeh, Tableau, and Altair to represent and share data insights. Learning how to use visualization is key when presenting data in a clear and structured manner.
Data Science Terminology Cheat Sheet: 5 Advanced Data Science Terms
Data science is an exciting field with many different roles, even if you are not an expert in mathematics. There are many tools and software to help you learn and practice data science techniques. Below are five advanced terms in data science that you should know.
Big data is any large and complex collection of data. These collections of data are often continually growing. Machine learning algorithms are applied to big data sets to find patterns and make predictions.
Why Data Scientists Should Know About Big Data
Global digital data is multiplying which requires data scientists to be able to understand big data. Big data has made it possible for them to work with datasets that keep growing. They must also be familiar with software tools that allow them to store, analyze, and process big data.
Business intelligence is used to analyze highly structured and static data. This puts a focus on present data and often looks back on historical data in order to come up with solutions. Data science is generally associated with making predictions and future analyses.
Why Data Scientists Should Know About Business Intelligence
Data scientists can utilize business intelligence to help a business grow and succeed by using data sourcing, data mining, and data visualization techniques. This allows them to compare businesses with their competitors and identify business trends.
A Bayesian network is a graphical model that shows the relationships of random variables. This tool can help you understand future events by using past data and assigning probabilities to make predictions for future outcomes.
Why Data Scientists Should Know About Bayesian Network
Data scientists apply algorithms using Bayes Theorem and conditional probability to find solutions for complex problems. Bayesian networks in machine learning allow for predictions that traditional statistics can’t offer.
Machine learning is the use of computer algorithms to imitate how humans learn. The three types of machine learning are supervised, unsupervised, and reinforcement learning. Applications of machine learning include pattern recognition, image classification, high volume data extractions, and facial detection.
Why Data Scientists Should Know About Machine Learning
Machine learning models provide data scientists with a new and smart alternative to analyzing large data sets. Machine learning can acquire more accurate results through the use of algorithms and data-driven models. This allows for the real-time processing of results and insights.
Neural networks are algorithms that replicate how the human brain works. Neural networks have an input layer, a hidden layer, and an outer layer. In supervised learning, neural networks are used to classify and label datasets.
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Why Data Scientists Should Know About Neural Networks
Data scientists benefit from neural networks as they are suitable for nonlinear relationships. Neural networks are used to uncover patterns from dependent variables and independent variables. They have helped data scientists make advancements in natural language processing so computers can better understand human speech.
How Can I Learn Data Science Terminology in 2022?
Data science bootcamps are the best way to learn data science terminology in 2022. They offer comprehensive and accelerated programs covering data science topics and practical learning. You will also learn statistical analysis techniques such as random forests, market basket analysis, and probability distribution methods.
Online data science courses and classes are also great resources that can help you learn basic terminology related to data science, machine learning, and artificial intelligence. There are also various data science blogs you can explore to expand your knowledge of data science terminology.
Data Science FAQ
Data analysts examine and make sense out of data. Data scientists explore new and advanced ways to capture data, which is then used by data analysts. Data scientists have more responsibilities and are considered to be a more senior role than data analysts.
It takes four years to earn a Bachelor’s Degree in Data Science, and another two years if you want a master’s degree. If you enroll in a data science bootcamp it will take between three and six months. However, this intense learning period is better suited to those with knowledge in programming, computer science, or AI.
Yes, data scientists must learn how to code. Extensive knowledge of software engineering and advanced programming is required for anyone looking to become a proficient data scientist. Python is the most popular computer language used by data scientists according to a recent report by Anaconda.
The average annual salary of a data scientist is $98,230, according to the US Bureau of Labor and Statistics. The top 10 percent of data scientists earn an average wage of $165,230 per year. California, New York, and Washington are the highest-paying states for data science jobs.
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