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  • Ali Hasany

Data Science and its 20 key terms which you should know.

Updated: Mar 2

It is not surprising that so many people are interested in a career in data science given how quickly the field is developing.


In actuality, one of the most in-demand careers is that of a data scientist.


Having a basic awareness of the terminology used in data science is crucial if you want to work in the field.


You need not fear, though, since you have arrived at the appropriate location.


TOP 20 KEY TERMS OF DATA SCIENCE

The top 20 words in data science that beginners should be familiar with are explained in this essay.


Check that out!


Data Science Mind Map
Data Science Mind Map

1. Business Intelligence

Data comes in many forms, each of which necessitates a distinct approach of analysis.


Business intelligence is one such method for analyzing highly organized and static data.


In order to find answers, both recent and historical data are emphasized in this situation.


Organizations may develop and be successful by recognizing market trends with the help of business intelligence.


2. Visualization

Data that summarizes ideas and results must be shown visually, according to data scientists.


Visualization is all about doing this.


With visualization in place, data can be presented in a form that is more powerful, perceptive, and simple to grasp.


All of this clarifies why becoming a proficient data scientist requires understanding how to utilize visualization.


3. Big Data

Well, the term itself explains what it signifies in reality.


It is only a compilation of substantial and intricate info. Data sets eventually become "big data" as they continue to expand and become enormous.


Additionally, a few machine learning algorithms are used to look for trends in the data and provide the data scientists the ability to anticipate the future.


4. Data Mining

Finding anomalies, trends, and correlations within huge data sets in order to forecast outcomes is known as data mining.


You may use this information to lower risks, improve customer connections, raise profits, and more by employing a variety of strategies.

5. Data Wrangling

It is impossible to manipulate data in order to produce the desired outcomes because it is rather random and raw.


This is the justification behind the requirement to manage the raw data till it performs better in a more extensive process or project. If you're wondering what taming is, it's just aligning numbers with a broader data collection.


That's not all, though. Additionally, it entails changing or deleting values that might subsequently influence analysis or performance, etc.


6. Algorithm

Algorithms are designed by humans to contribute to informed decision-making that creates the intended business value.


7. Linear Regression

As a prospective data scientist, you must have come across this phrase frequently. Well, linear regression is a statistical technique used in the construction of regression models and predictive analysis.


Finding a straight line between a known independent and dependent output variable is the main usage of this technique.


Finding the strength in the association between the variables is the straightforward goal behind this.


8. Classification

Classification algorithms are used to classify or categorize data. It can be performed on both structured and unstructured data.


Classification can be of three types: binary classification, multiclass classification, and multilabel classification.


9. Bayesian Network

The amount of data is enormous and random. Therefore, it is crucial to have a graphical model that depicts the relationships between these random variables.


The Bayesian network is useful in this situation.


This tool plays a crucial part in data science since it may help you comprehend future events by using historical data and assigning probabilities to produce predictions for future outcomes.


10. Machine Learning

Another phrase that is frequently used in the context of data is machine learning. It is a method where a computer applies an algorithm to fairly comprehend a set of data.


It bases its projections on that.


Organizations are in a position to make the best decisions possible with machine learning in place.


11. Neural Networks

Neural networks are just algorithms that mimic how the human brain functions. These algorithms' primary goal is to label and classify datasets.


Neural networks have an input layer, a hidden layer, and an outer layer, which is a crucial distinction to make.

12. Generative Pre-trained model

It is clear from the name alone what function it is meant to serve.


When developing new models, such a model aids in resolving a problem that has previously been of a similar nature.


The ability to fine-tune pre-trained models to change variables and parameters is one of their strongest features. With these models in place, data scientists may work to reduce expenses, save time, and produce better, more precise outcomes.

13. Cluster Analysis

Cluster analysis is nothing more than using unsupervised learning algorithms to group data points based on similarities in the absence of an output variable.


This form of analysis is critical in the field of data because it reveals patterns inside clusters, between clusters, and the cluster groups that are most similar to one another.


14. Deep Learning

These methods of classifying and predicting have driven the AI revolution of the last decade.


Deep neural networks have produced cutting-edge findings in imaging, natural language processing, and anomaly detection.


The conversational bots that are helping people navigate customer service on a website come from this AI technology.


Simple automation can be applied more widely, such as voice-to-text on a cell phone, or it can be used to recognize and translate handwriting, utilizing the data to aid in the effort.


15. Reinforcement Learning

This model examines an environment and develops the ability to make a sequence of decisions that aims to find the best positive path forward.


Such models can learn to win Chess and Go tournaments against human grandmasters. Practical applications include route optimization, factory optimization, and cyber vulnerability testing.


16. Exploratory Data Analysis

Exploratory Data Analysis is the crucial process of doing preliminary investigations on data in order to uncover patterns, spot anomalies, test hypotheses, and validate assumptions using summary statistics and graphical representations.


17. Large Language Models

A large language model (LLM) is a language model that consists of a neural network with many parameters (usually billions of weights or more) that has been self-trained on vast amounts of unlabelled text.


LLMs first appeared in 2018 and excel in a wide range of tasks.


Natural language processing research has transitioned away from the prior paradigm of developing specialized supervised models for specific tasks.


18. Predictive Analytics

Predictive analytics is a subset of advanced analytics that uses previous data to create predictions about future events using statistical modeling, data mining techniques, and machine learning.


Companies use predictive analytics to identify risks and opportunities by finding trends in data.


19. Data Lake

A data lake is a centralized repository that can hold all of your structured and unstructured data at any scale.


You may save your data without first structuring it and then run various sorts of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning—to help you make better decisions.


20. Python

Python is an object-oriented, high-level programming language with dynamic semantics that is interpreted.


Its high-level built-in data structures, together with dynamic typing and dynamic binding, make it particularly appealing for usage as a scripting or glue language to connect existing components together.


Python's concise, easy-to-learn syntax prioritizes readability, lowering software maintenance costs.


Python has support for modules and packages, which promotes program modularity and code reuse. The Python interpreter and substantial standard library are free to use and distribute in source or binary form for all major platforms.


Data Science Overview
Data Science Overview

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