Data Science Terms

Data Science Terms

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Businesses and organizations in the modern world are driven by data. Big data informs the key strategies and major decisions made by businesses every day. However, the massive amounts of data being generated every second are in raw form. Data science is a multidisciplinary field that combines various tools, machine learning patterns, and algorithms to uncover trends and patterns in the raw data. These trends and patterns are then used by businesses to optimize their productivity and revenue.

Data scientists analyze raw data to gain valuable insights for businesses or organizations. An important part of their job is working with stakeholders to understand their business goals, and working out how they can use data to meet those goals.

As the field of data science expands and becomes more central to business operations, it can be challenging for beginners and people with no tech background to understand the various terms being thrown around by data scientists.

So here's a run-through of some of the common technologies, words, and phrases used in data science:

  • Algorithms – A repeatable set of instructions given to a computer to perform a data scientist’s task of processing vast amounts of information. Algorithms are usually in a language that humans can comprehend. They can range from easy to super complex.

  • Artificial Intelligence (AI) – Machines that can use the data fed to them and act in an intelligent manner are referred to as artificial intelligence. It’s one of the most exciting, quickie-evolving aspects of data science. These intelligent machines can process the data they are fed and use it to learn, adapt, and make decisions, replicating the human brain to an extent. For example, self-driving cars use data from multiple sources to make decisions regarding speed, turns, and passing others while on the road.

  • Big data – As worldwide internet connectivity increases, more and more data is produced every second. Big data refers to the massive amount of data that is generated at a high speed and an exponential rate. The potential of data science has increased tremendously because of big data.

  • Behavioral analytics – Behavioral analytics uses data to understand why and how consumers act a certain way. The understanding of consumer behavior using data allows businesses to predict their actions in the future. These predictions further help the business or data scientist in achieving favorable outcomes.

  • Bayes theorem – A mathematical formula used to determine conditional probability, or the probability of one event happening with respect to another event happening or not happening. Bayes theorem is used for probabilities and outcomes that depend on unknown variables, and it’s incredibly useful in the realm of data science.

  • Classification – A technique of sorting new data into pre-existing categories. Classification is a data mining function performed by algorithms. It’s about predicting new behaviors, outcomes, or events based on past examples. It looks for specific patterns in the data to make a prediction. Classifications are discrete and don’t imply any order. The data science process that performs classification is called a classifier. Classifiers can be useful even when they are not 100% accurate.

  • Clustering – This refers to grouping similar or homogenous data together. When an algorithm receives data, it groups similar points of the data. Clustering is different from classification in which data is segregated in predetermined groups. Clustering is common in exploratory data mining, which every data scientist does on the job.

  • Deep learning – In deep learning, machines improve on their own by learning and examining algorithms. It helps computers and machines perform human actions easily. Deep learning is an advanced form of machine learning and helps in solving complex problems. It requires multiple rounds of data input and output, and is one of the more recent developments to come out of data science.

  • Decision trees – A decision tree is a structure used to classify information in a way that the computer easily understands. It’s named so because it starts with a root problem and, like a tree, branches out into multiple solutions. The branches represent exclusive choices. In the context of data science, the tree is used to show how and why one choice can lead to another.

  • Data mining – The process of extracting useful information from a data set is called data mining. It’s done by collecting data, combining various sources, and discovering trends and patterns within it. This is a key responsibility of data scientists in any industry.

  • Data visualization – Common throughout data science, this refers to the use of graphs, charts, infographics, and charts to visually represent data.

  • Data engineering – Any data that’s collected has multiple applications. Data engineering is a specific segment of data science that deals with the practical application of this data and its analysis.

  • Data wrangling (munging) – A common responsibility of data scientists, this is the process of changing data from a raw form into a version that will be more valuable and accurate for data analytics.

  • Exploratory data analysis (EDA) – EDA is the process of investigating data. Data scientists do this to discover patterns, check for glitches, and to test hypotheses. EDA uses statistical analysis to summarize the main characteristics of data sets, often with visual methods.

  • ETL – In computing, ETL refers to the process of data integration that consists of three steps: extract, transform, and load. At the stage of extraction, data is collected from multiple types of sources. Then this data is transformed into a form that can be placed into the next database. The last step is when the data scientist writes the transformed data into the target database.

  • Fuzzy logic – ‘Fuzzy’ means imprecise. Using fuzzy logic allows partial truth and is used to handle concepts that are innately ambiguous, which are common throughout modern data science. It allows statements like ‘mostly true’ and ‘a little false’ to exist.

  • Neural networks – In the human brain, neural networks are a dense system of nodes that have multiple layers like input, output, and hidden layers above and below it. Neural networks in data science are based on a similar design. Through a neural network, data is moved in one direction. Just like neurons, nodes pass information to other nodes in the network. Neural networks solve problems through a trial and error method, and develop output without any programmed rules.

  • Machine learning – A specific area of data science, this is the practical application of artificial intelligence. It’s the science of improving computers by feeding them data extracted from the real world. With the help of this data, machines and computers learn and act more like humans. Machine learning aims to make computers learn and make adjustments with no human assistance.

  • Supervised learning – A branch of machine learning where the computer is trained using ‘labeled’ data. Through supervised learning, an algorithm is taught to analyze labeled training data and produces functions that can be used to predict outcomes for unforeseen data. For example, take an algorithm that is being trained to identify female human beings. In supervised learning, the data scientist will use labeled images of female human beings to ‘teach’ this data to the algorithm, which is fed to it in a labeled manner.

  • Unsupervised learning – Another branch of data science and machine learning where the model is not supervised, and is instead allowed to discover information on its own. Unlike supervised learning, it deals with unlabeled data. For example, the algorithm being trained to identify female human beings under unsupervised learning will not be fed any labeled data. Instead, it will learn the characteristics of female humans on its own and then assign its own labels to distinguish female humans from others.

  • Standard deviation – In data science, standard deviation is a calculation used to measure how far removed a value is from the average. The value of standard deviation can be used to infer why a piece of data differs from the norm.

  • Statistical analysis – The process of generating statistics and discovering patterns and trends in the data.

  • Predictive model – A predictive model uses data available from past events to predict future events or outcomes. Algorithms analyze a large amount of data to make accurate predictions. Data scientists frequently rely on predictive models to do their work quickly and accurately.

  • Python – An open-source programming language, meaning it can be edited and changed by anyone. It’s used in the creation of sites like YouTube that attract heavy traffic. Python is a general-purpose language that can be used to develop websites, web applications, and desktop GUI applications.

  • R – A programming language used for statistical computing and developing statistical software. R is generally preferred by statisticians when working with large data sets, and it’s one of the most useful languages for data scientists.

  • Structured Query Language (SQL) – A programming language that’s designed to interact with databases. SQL is commonly used to update and retrieve data from a database.

Now that you’re familiar with the most commonly used terms in the industry, you can learn more about the exciting world of data science and get inspired to start your own career in data.

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