Data Science Glossary presents a collection of key terms related to Data Science with brief definitions and descriptions categorized into separate topics. It takes time to familiarize yourself with Data Science terminology as these words may not be used as part of your daily vocabulary. However, once you start reading about the topic and hearing about these terminologies, you will comprehend the importance of these terms in Data Science and eventually you will be interested to learn more. I, in this article, have grouped key Data Science terminologies into categories. Let us now have a look at these categories into which these terminologies are grouped.
- The Fundamentals of Data Science
- Sectors Involving Data Science
- Statistical Tools and Terminologies
- Machine Learning Tools and terminologies
- Deep Learning Key Terms
Sectors Involving Data Science
Data is the foundation for every task happening across all sectors. It has become an integral part of human life and is affecting how industries operate to a very large extent. Data Science is all about how we gather data, broadcast, study, stock and carry out. It is one of the most important jobs out there. That is the reason why Data Science has application in every sector that exists today. Let’s learn about sectors that are effectively utilizing Data Science and benefitting from it.
Artificial Intelligence (AI) is the process in which machines broadcast their intelligence. It is an incredibly exciting and powerful Machine Learning technique. As per the existing trends in the field of AI, it seems that every enterprise will be data driven and will accumulate the competence to access Machine Learning in the cloud to power Artificial Intelligence apps.
Business Analytics is about learning data over statistical and operations analysis, the formation of predictive models, application of optimization techniques, and the communication of the collective outcomes to clients, business partners, and others. It necessitates quantitative means and evidence-based data for business demonstration and decision making.
Business Intelligence (BI)
Business Intelligence is about technologies, claims, and practices for the assortment, incorporation, examination, and demonstration of business data. The purpose of Business Intelligence is to make improved business decisions. Fundamentally, Business Intelligence schemes are data-driven Decision Support Systems (DSS).
Data Analysis is the process of analytically implementing statistical and/or logical methods to define and demonstrate, abbreviate and summarize, and assess data. This process is the main constituent of Data Mining and Business Intelligence (BI). It is the key perception that drives business verdicts.
Data Architecture essentially deals with designing and creating data resources. This method provides procedures to design, conceptualize and implement a completely cohesive, business-driven data resource that includes real-time trials and objects, with a suitable operating atmosphere.
Data Engineering is an interdisciplinary field that encompasses Data Visualization, Data Analysis, knowledge engineering, and certainly the subject of the application. The execution of data extraction for engineering is partly through analysis.
Data Journalism is using data and number crunching on journalism to discover healthier ways of describing and/or delivering context to broadcast stories. As said by the Data Journalism Handbook,” data is either the tool to tell a story, or the source upon which a story is woven, or both”.
It is a process of extracting operational data from a bigger group of raw data. It indicates studying data patterns in large batches of data with the help of various software. Data Mining primarily relies upon active data collection and warehousing along with computer processing.
It is a versatile combination of data interpretation, algorithm expansion, and the technology to resolve logically complex problems. It is all about discovering answers from data. Data Science is also about dwelling deep to the coarse level to mine and comprehend complex patterns, drifts, and inferences. This process of surfacing unseen intuition enables companies to make informed business decisions.
Data Visualization is a demonstration of quantifiable data through graphical representation. In other words, data visualizations convert large and small datasets into illustrations which is much easier for human interpretation. To craft decent data visualizations, one needs to begin with thorough data cleaning which is properly obtained and complete.
Econometrics is applying statistical and mathematical concepts in Economics for examining theories and estimating future trends. Generally, statistical methods examine economic models, later comparison is done between the results obtained with real-time samples.
Feature Engineering is a process to create supplementary pertinent structures from the existing raw features in the data and to raise the analytical supremacy over the learning algorithm. This form of engineering is generally to generate additional features.
Quantitative Analysis discusses financial and business analysis to comprehend and forecast performance of events. This is done by mathematical dimensions and calculations, statistical demonstration and exploration. Quantitative Analysts attempt to represent a given reality through numerical values.
Stata is an influential statistical software that empowers users to evaluate, manage, and create graphical visualizations of data. The software houses both command line as well as the graphical user interfaces to make the software user intuitive.
Supervised Learning is a method to aid machines to categorize substances, snags, and circumstances based on relative data fed into them. Usually, machines are fed with data like features, designs, dimensions, hues, and height of items. The data could also include people or situations continually until the machines gain the ability to achieve accurate categorization.
The word “semantic” refers to connotation in language. Semantic technology influences Artificial Intelligence to show how people comprehend language and process information. In other words, Semantic technologies encrypt meaning into content and information to permit the computer system to have human-like understanding and intellect.
Unstructured information management (UIM) applications are software systems to analyze amorphous information which include text, audiovisual, pictures, and more. UIMA determines, establishes, and distributes appropriate knowledge to the user about the above types of information. While evaluating unstructured information, UIM applications use various investigation skills, like statistical and rule-based Natural Language Processing (NLP), Information Retrieval (IR), Machine Learning, and ontologies.
To Be Continued…
I hope the listicle about “Sectors Involving Data Science” will be handy and serve as a cheat sheet whenever you’re in need of it. In my next article, I will discuss the next set of Data Science terminologies under the heading “Statistical Tools and Terminologies”. To learn more about Data Science and related courses visit Acadgild.