History of Data Science

Data science is a field of study that involves analyzing large amounts of data to discover new insights and methods for problem-solving.

The history of data science is long, and there have been societies, such as ancient Egypt’s tax records and the Roman Empire’s census, that needed to deal with large amounts of data. Over time, knowledge from various fields, including statistics, computer science, machine learning, database technology, and visualization technology, has come together to develop data science.

Let’s take a closer look at the history of data science from around the 1960s.

1960s

In the 1960s, foundational technology for data processing was developed, and data processing became widely used in the business field. At that time, data owned by companies was mainly paper-based and manually processed.

However, this method was slow and error-prone. Computer-based data processing was introduced, making it possible to process data efficiently and accurately.

Additionally, data analysis techniques based on statistics and mathematics developed during this period and became useful in business fields such as business strategy and market analysis. These foundational technologies have greatly contributed to the development of data science.

1970s

The 1970s were an important time for the development of data science. During this period, advances in data processing technology made it possible to manage and process large amounts of data, and statistical analysis became widely used in business and economics fields. Specifically, the following events occurred during the 1970s:

Development of database technology

In the 1970s, relational database technology was developed, making it possible to manage and process large amounts of data. This allowed companies to handle vast amounts of data.

Introduction of statistical analysis software

In the 1970s, statistical analysis software such as SAS and SPSS was introduced, making data analysis more common. These software programs had various functions necessary for statistical analysis, contributing to the efficiency and accuracy of data analysis.

Introduction of data mining

In the late 1970s, the concept of data mining was introduced. This is a technology for discovering knowledge and information from large amounts of data. By using this technology, companies can utilize it to develop business strategies and product development.

1980s

In the 1980s, with the advancement of data processing technology, data analysis using computers became more common. The development of statistical analysis software, such as SAS, SPSS, and Stata, made them widely used as tools for data analysis.

Additionally, progress was seen in the field of machine learning, where algorithms such as neural networks, decision trees, and random forests were developed. These algorithms were able to discover patterns from large amounts of data, and their application was expected in business and scientific fields.

Furthermore, progress was made in data visualization technology. The development of graphics software made it possible to visually represent data distributions such as histograms, scatter plots, and box plots.

These technological advancements established data science as a more practical field and led to its increasing utilization in business and scientific fields.

1990s

In the 1990s, technologies and methodologies related to data science progressed even further, and new fields were developed. First, the spread of the internet made it possible to collect large amounts of data online. This led to the emergence of data mining, a technique for extracting useful information from large amounts of data, which was utilized in business and finance fields.

Furthermore, progress was made in the field of artificial intelligence. The technique of neural networks gained attention and was used in fields such as speech recognition and image recognition. Additionally, the concept of big data emerged in the 1990s, leading to the development of technology to process large amounts of data. As a result, large-scale data storage such as data warehouses and data marts became widespread, and data analysis became more precise.

Thus, the 1990s can be considered a time of significant progress in technologies and methodologies related to data science, with new fields being developed.

2000s

In the 2000s, the internet rapidly spread, and data volumes increased significantly. During this period, web services such as search engines and online advertising emerged, leading to an increasing need to analyze large amounts of data on the web. Additionally, the concept of big data was born, and big data processing technologies such as Hadoop and MapReduce were developed.

In the 2000s, progress was also made in the field of machine learning and artificial intelligence. Google developed the PageRank algorithm, significantly improving search engine accuracy. Furthermore, machine learning algorithms such as SVM and random forests were developed, leading to improvements in data analysis accuracy.

In the latter half of the 2000s, the application of artificial intelligence through big data analysis and machine learning became widespread, leading to the growth of the data science field.

2010s

With the spread of the internet in the 2010s, it became possible to process and analyze large-scale data called big data, and the importance of data science has been further enhanced. In addition, the development of artificial intelligence technologies such as machine learning and deep learning has improved the accuracy of data analysis and expanded new application fields.

In the 2010s, open-source data science platforms such as R and Python became widely popular and made a significant contribution to the spread and development of data science.

Furthermore, data science began to be applied in a wide range of fields, not only in business but also in medicine, biotechnology, natural disaster prediction, and many others. As big data processing technology and the application scope of data science expanded, privacy and ethical issues related to data began to emerge. Addressing these issues also became an important challenge.

Present Day

The advent of the big data era has led to the generation of enormous amounts of data worldwide. As a result, the importance of data science has become even greater. Data science can extract useful information from vast amounts of data and help solve business and social problems. For example, in the business field, analyzing customer data and sales data can help determine the direction of marketing strategies and product development.

Furthermore, the application of data science has advanced with the progress of AI technology. For instance, machine learning can automatically learn patterns from data. This has led to improvements in accuracy in fields such as image recognition, speech recognition, and natural language processing.

Additionally, advances in neural networks using deep learning have made it possible to analyze complex data. Thus, data science is becoming increasingly important in the age of big data and the advancement of AI technology, and its application is expected in various fields.