Data Science Types, Importance, History, and More

Data science is a discipline that studies where a particular base of information comes from. Likewise, it analyzes how these resources can be interpreted and represented in order to put them to productive use.

In other words, data science is related to the management of databases stored in digital files, from which a lot of helpful information can be extracted, such as statistical indicators. These can help, for example, a company to make business decisions.

Likewise, data science provides tools that allow not only to interpret but also to represent, for example, in images, the available data. Thus, we have the histogram, the bar chart, the pie chart, among others.

As can be deduced, this science is interdisciplinary as it covers knowledge of mathematics, statistics, and computer science.

Data Science and Data Types

It Should Also Be Note That Data Science Can Work With Two Types Of Data:

Structured:  They are organize, such as those tables with different columns. Each with another category such as name, surname, age, identity document number, etc.

Unstructured:  Those that do not correspond to a specific format, such as a free written text. In that case, it is necessary to interpret the content and extract data that can be manage.

Taking everything explained into account, professionals specializing in data science must have analytical skills and be able to communicate the content of the information they have processed.

Importance of Data Science

It is essential for companies or institutions that work with extensive data. Thus, these can become valuable information.

We can relate data science to Big Data, which consists of developing mechanisms capable of processing and managing massive data from various sources. The objective is to convert them into information capable of being interprete by the human being, which helps him make decisions.

This data to process can come from transactions between individuals and organizations (such as banking operations), daily actions of people (such as Internet searches), machines (such as the GPS of the cell phone that records where the user has been), or information biometrics (such as fingerprints).

History of Data Science

It can be said that the American statistician John Wilder Tukey was a forerunner of [data science] in the sixties. Emphasizing the importance of analyzing data instead of statistical testing models.

However, it was not until 1996 that the term data science was first used in a conference title. In the talk called: “Data Science, Classification, and Related Methods.” This is within the framework of meeting members of the ‘International Federation of Classification Societies (IFCS) held in Kobe, Japan.

Another significant milestone occurred in 2005 when “Long-Lived Digital Data Collections Enabling Research and Education in the 21st Century”. That document defines data scientists as computer experts, database and software programmers, and professionals from other disciplines (such as librarians and archivists) who are crucial to managing a digital data collection.

Data Science Life Cycle and Process

Data science projects involve a sequence of data collection and analysis stages. In an article outlining the data science process, Donald Farmer, director of analytics consultancy TreeHive Strategy, outlined these six main steps:

• Identify a business-related hypothesis to test.
• Gather data and prepare it for analysis.
• Experiment with different analytical models.
• Please choose the best model and run it on the data.
• Present the results to company executives.
• Deploy the model for continued use with new data.

Farmer said the process makes  a scientific endeavor. However, he wrote that incorporating companies, data science work “will always be most usefully focus on direct business realities” that can benefit the business. As a result, he added, data scientists must collaborate with business stakeholders on projects throughout the analytics lifecycle.

Challenges in Data Science

It is inherently challenging due to the advanced nature of its analytics. A large amount of data typically analyzed adds to the complexity and increases the time it takes to complete projects. Additionally, data scientists frequently work with big data pools that can contain various structured, unstructured, and semi-structured data, further complicating the analysis process.

One of the significant challenges is eliminating bias in datasets and analysis applications. That includes problems with the underlying data itself and those that data scientists unconsciously build into algorithms and predictive models. Such tendencies can skew analytics results if not identified and addressed, leading to flawed findings that lead to poor decisions. Worse yet, they can have a damaging impact on groups of people—for example. In the case of racial bias in artificial intelligence systems.

Conclusion

Data Science is an interdisciplinary field that involves scientific methods, processes, and systems to extract knowledge or better understand data in its different forms, whether structured or unstructured. It continues some data analytics fields such as statistics, data mining, machine learning, and predictive analytics.