Data Science Technical Dictionary
Data Science Technical Dictionary consists of various terms which are very useful when we start learning Data Science. In this article, Let’s understand some keywords or topics at the very basic level that often comes in data science.
Algorithms
Algorithms are a series of steps that can be repeated for specific kinds of tasks with data. With the help of an algorithm, we tell the instructions or a series of instructions to the computer on how to perform a task.
Angular JS
Angular JS is an open-source library of JavaScript which is regularly maintained by Google and the Angular JS community. It provides a platform to build web applications for mobile and desktop. AngularJS is very useful for data scientists to provide the interface to show the analysis results.
Artificial Intelligence (AI)
Artificial Intelligence is the intelligence of machines to act and think like humans. It is the development of computers and machines that let them perform tasks that generate the insights of the data to grow the business. Business Intelligence increases the opportunities to expand the business. Various tools are used in Business Intelligence such as data warehouses, data discovery tools, data mining tools, services of cloud data, and dashboard reports.
Big Data
Big Data means a large volume of both structured and unstructured data. The amount of data is not important for any organization, instead, the useful data from a large amount of data or big data is important for the organization. And companies use various tools to get insights from this data to derive effective strategies for the growth of the business.
Clustering
Clustering is used for the discovery of inherent groupings of the data. It is a method of unsupervised learning model. Clustering segments the data based on multiple factors, for example, the clustering of customers from a large amount of data based on the same interests of customers, their purchases and more.
Computational Linguistics
Computational Linguistics is concerned with the understanding of written and spoken language from the perspective of computation. It is used for the processing and production of languages for computation. Since languages are the most natural and most important means of communication for us, computational linguistics increases our interaction with machines.
Correlation
It is used to describe the degree of two variables that move in coordination with one another. The variables that move in the same direction are known as positive correlations, and the variables that move in opposite directions are known as negative correlations. Correlation is also known as the ratio of two variables to a product of variance.
Database
The database is a collection of Structured Data. It is a storage space of Data. The database is mostly used with Database Management System such as (DBMS), MySQL, or other query languages which helps in retrieving useful data from the complete set of data.
Data Analysis
It is the analysis of the data to process the requests of extraction of data of present and past. In data analysis, the statistics used are less complex as compared to data science and it is used to identify the patterns to improve the organization’s growth.
Data Engineering
Data Engineering is done at the backend. Data Engineers are the people who develop a system for data scientists to make the process of data analysis easier. It mainly focuses on the practical application of data analysis and its collection.
Data Journalism
The data used in Data Journalism is mostly of a numerical type of data. Numerical data is very useful in the production and distribution of knowledge in this digital world. In Data Journalism, the data is analysed to find useful information.
Data Science
Data Science is a combination of algorithms, data analysis, methods, processes, and systems for the extraction of knowledge and useful insights from both structured and unstructured data. It encompasses the preparation of data for the analysis which includes multiple steps such as cleansing of the data, aggregation, and manipulation of the data.
Data Visualization
Data Visualization is the process of converting large sets of data into visual forms such as graphs and charts which are very useful in understanding the data insights and it makes it easier to identify the real-time trends in the data.
Data Exploration
It is the first step before analysing the data and is used to explore the data to find more insights into the data. The tools that come under data exploration makes the process of data analysis easier and some of these tools include Microsoft Power BI, Tableau, and Qlik.
Data Mining
Data Mining is the extraction of useful information from structured and unstructured data. We extract useful insights from the set of data and the use of those insights can be profitable for organizations. The mathematical analysis is used in Data mining to find the patterns of the data and the trends in the insights of data.
Data Pipelines
It is a collection of actions that are used to process the data in a sequence. Data pipeline means the output that is taken from one segment becomes the input for the next segment. This process continues until all the data is appropriately cleaned for further use by data scientists.
Data Wrangling (Munging)
Data Munging is a process in which the mapping of data is done to transform it from a “raw” form to another useful format that is valuable for multiple purposes.
Deep Learning
Deep Learning is a multi-level algorithm that uses multiple layers to extract higher levels of features from the raw input data. It is a type of machine learning and artificial intelligence. Deep learning is used in almost every kind of industry. As it is a multi-level algorithm, the first level will find certain lines of data, the next line will find combinations of lines as shapes, and then further levels will do the same identification with more information.
Early Stopping
It is a technique in Machine Learning to avoid overfitting when we are training a machine learning model. The early stopping technique stops the training of machines after a certain set of training sessions.
Feature Engineering
Feature Engineering is a process of iteration and more effort which is required to obtain a good model. In feature engineering, domain knowledge is used to extract features from the raw data. It is useful to improve the performance of machine learning algorithms to make the training models efficient and effective.
GATE
GATE is an abbreviation used for ‘General Architecture for Text Engineering. It is an open-source, java-based framework for tasks related to language processing. It is used by a wide community of teachers, scientists, and students for language processing tasks, extraction of information and more.
Hadoop
It is an open-source distributed software framework that is used to deal with enormous data. Hadoop provides big storage for every kind of data and can be used for parallel processing to handle big data.
Iteration
Iteration is the repetition of an algorithm’s parameters when we are training a machine learning model on the dataset. Each iteration takes a certain number of data for the machine learning process.
Labelled Data
As the name suggests Labeled Data, the ‘label’ helps to understand the meaningful data in the records. Obtaining labelled data is more expensive than obtaining the raw data as it involves the manual labelling of every piece of data.
Machine Learning
Machine Learning is the training of machines with the help of data and the algorithms applied to the data. Machine learning is a type of AI (Artificial Intelligence), which is very useful when we are creating a machine learning model without explicitly programming the machine.
SQL
SQL is an abbreviation used for Structured Query Language and it is used to extract useful information or a piece of information from the databases with the help of SQL queries. It is very useful when we need to find the data of any specific person or a category from the database.
Web Scraping
It is a process of gathering data from various sources such as websites, databases, and other resources. In web scraping, a few keywords or scripts are written to find the relevant data, then the data is scraped and pulled into a new file for later analysis of the data.
