Author: Usama Amin

Introduction

Entrepreneurial ideas take advantage of the range of opportunities this field opens up, thanks to what is engineered by scientific profiles such as mathematicians or programmers.

1. ALGORITHM.  In Computer Science, an algorithm is a set of steps to perform a task. In other words, a logical sequence and instructions form a mathematical or statistical formula to perform data analysis.
2. SENTIMENT ANALYSIS.  Sentiment analysis refers to the different methods of computational linguistics that help to identify and extract subjective information from existing content in the digital world. Thanks to sentiment analysis, we can be able to extract a tangible and direct value, such as determining if a text extracted from the Internet contains positive or negative connotations.
3. PREDICTIVE ANALYSIS. Predictive analysis belongs to the area of Business Analytics. It is about using data to determine what can happen in the future. The AP makes it possible to determine the probability associated with future events from the analysis of the available information (present and past). It also allows the discovery of relationships between the data that are normally not detected with less sophisticated analysis. Techniques such as data mining and predictive models are used.
4. BUSINESS ANALYTICS. Business Analytics encompasses the methods and techniques used to collect, analyze, and investigate an organization's data set, generating insights that are transformed into business opportunities and improving business strategy. AE allows an improvement in decision-making since these are based on obtaining real data and real-time and allows business objectives to be achieved from the analysis of this data.
5. BIG DATA.  We are currently in an environment where trillions of bytes of information are generated every day. We call this enormous amount of data produced every day Big Data. The growth of data caused by the Internet and other areas (e.g., genomics) makes new techniques necessary to access and use this data. At the same time, these large volumes of data offer new knowledge possibilities and new business models. In particular, on the Internet, this growth begins with the multiplication in the number of websites, beginning search engines (e.g., Google) to find new ways to store and access these large volumes of data. This trend (blogs, social networks, IoT…) is causing the appearance of new Big Data tools and the generalization of their use.
6. BUSINESS ANALYTICS (Business Analytics). Business Analytics or Business Analytics allows you to achieve business objectives based on data analysis. Basically, it allows us to detect trends and make forecasts from predictive models and use these models to optimize business processes.
7. BUSINESS INTELLIGENCE Another concept related to EA is Business Intelligence (IE) focused on the use of a company's data to also facilitate decision-making and anticipate business actions. The difference with EA is that EI is a broader concept, it is not only focused on data analysis, but this is an area within EI. In other words, EI is a set of strategies, applications, data, technology, and technical architecture, among which is EA, and all this focus on the creation of new knowledge through the company's existing data.
8. DATA MINING or data mining.  Data Mining is also known as Knowledge Discovery in Database (KDD). It is commonly defined as the process of discovering useful patterns or knowledge from data sources such as databases, texts, images, the web, etc. Patterns must be valid, potentially useful, and understandable. Data mining is a multidisciplinary field that includes machine learning, statistics, database systems, artificial intelligence, Information Retrieval, and information visualization, ... The general objective of the data mining process is to extract information from set data and transform it into an understandable structure for later use.
9. DATA SCIENCE.  The opportunity that data offers to generate new knowledge requires sophisticated techniques for preparing this data (structuring) and analyzing it. Thus, on the Internet, recommendation systems, machine translation, and other Artificial Intelligence systems are based on Data Science techniques.
10. DATA SCIENTIST.  The data scientist, as his name indicates, is an expert in Data Science (Data Science). His work focuses on extracting knowledge from large volumes of data (Big Data) extracted from various sources and multiple formats to answer the questions that arise.
11. DEEP LEARNING is a technique within machine learning based on neural architectures. A deep learning-based model can learn to perform classification tasks directly from images, text, sound, etc. Without the need for human intervention for feature selection, this can be considered the main feature and advantage of deep learning, called “feature discovery.” They can also have a precision that surpasses the human being.
12. GEO MARKETING. The joint analysis of demographic, economic, and geographic data enables market studies to make marketing strategies profitable. The analysis of this type of data can be carried out through Geo marketing. As its name indicates, Geo marketing is a confluence between geography and marketing. It is an integrated information system -data of various kinds-, statistical methods, and graphic representations aimed at providing answers to marketing questions quickly and easily.
13. ARTIFICIAL INTELLIGENCE.  In computing, these are programs or bots designed to perform certain operations that are considered typical of human intelligence. It is about making them as intelligent as humans. The idea is that they perceive their environment and act based on it, focused on self-learning, and being able to react to new situations.
14. ELECTION INTELLIGENCE.  This new term, "Electoral Intelligence (IE)," is the adaptation of mathematical models and Artificial Intelligence to the peculiarities of an electoral campaign. The objective of this intelligence is to obtain a competitive advantage in electoral processes.  Do you know how it works?
15. INTERNET OF THINGS (IoT).  This concept, the Internet of Things, was created by Kevin Ashton and refers to the ecosystem in which everyday objects are interconnected through the Internet.
16. MACHINE LEARNING (Machine Learning).  This term refers to the creation of systems through Artificial Intelligence, where what really learns is an algorithm, which monitors the data with the intention of being able to predict future behavior.
17. WEB MINING.  Web mining aims to discover useful information or knowledge (KNOWLEDGE) from the web hyperlink structure, page content, and user data. Although Web mining uses many data mining techniques, it is not merely an application of traditional data mining techniques due to the heterogeneity and semi-structured or unstructured nature of web data. Web mining or web mining comprises a series of techniques aimed at obtaining intelligence from data from the web. Although the techniques used have their roots in data mining or data mining techniques, they present their own characteristics due to the particularities that web pages present.
18. OPEN DATA. Open Data is a practice that intends to have some types of data freely available to everyone, without restrictions of copyright, patents, or other mechanisms. Its objective is that this data can be freely consulted, redistributed, and reused by anyone, always respecting the privacy and security of the information.
19. NATURAL LANGUAGE PROCESSING (NLP).  From the joint processing of computational science and applied linguistics, Natural Language Processing  (PLN or NLP in English) is born, whose objective is none other than to make possible the compression and processing aided by a computer of information expressed in human language, or what is the same, make communication between people and machines possible.
20. PRODUCT MATCHING. Product Matching is an area belonging to Data Matching or Record Linkage in charge of automatically identifying those offers, products, or entities in general that appear on the web from various sources, apparently in a different and independent way, but that refers to the same actual entity. In other words, the Product Matching process consists of relating to different sources those products that are the same.

Conclusion

Today there are numerous data science and AI tools to process massive amounts of data. And this offers many opportunities: performing predictive and advanced maintenance, product development, machine learning, data mining, and improving operational efficiency and customer experience.