The term data science is not new and comes from the beginnings of data management and data analysis, which date back to the 1960s. With the trend topic ” Big Data “, the science of data has once again become the focus of the company and the public. The focus of data science is – contrary to many
opinions – not in the data (ie “Data”) itself, but in the way how the data is processed, prepared and analyzed are. At Data Science, there is a purpose-oriented data analysis and the systematic generation of decision aids and bases in order to achieve economic competitive advantages. It is important to consider that a data analysis can only be successful if it relates to a specific question.
Data Science – History and Outlook
In the early days of data analysis , the focus of the analysis was on the evaluation of statistics. Data analysis evolved into a new scientific discipline through the advent of computer-aided data analysis. John W. Tukey wrote down his thoughts in his book The Future of Data Analysis (1962).In the 1970s, the term data science was for the first time defined more closely and publicly discussed via (new) methods of data processing. Since 1989, the topic of data science and data analysis has been taken up again and again. In doing so, approaches and the enormous potential of systematic data analysis for marketing and corporate strategies were demonstrated. The term Date Science moved temporarily into the background and lives only since Big Data again. Reason for the renewed interest in this topic are new technological developments in the area of analytical databases and analysis tools, which make a data evaluation economically viable. For a long time, data analysis was the subject of research and of large companies and corporations, as the cost and effort was enormous. Small and medium-sized companies could not exhaust these possibilities. They continued to rely on manual analysis and limited spreadsheet capabilities. Through the cloud computing , software-as-a-Service and the increasing development of open source solutions for databases and analysis software, there was a new momentum for the data science.
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Data Science – structuring and evaluation of company data
The approaches to the application of data science are diverse. It plays a major role in the exploratory search for correlating data properties . Data mining and text mining have become very popular. In these an automated analysis of databases takes place, which generates answers to a specific question. The discovery of new and yet unknown relationships is in the foreground. Another, ever more important approach is the use of data analysis in the production of forecasts, keyword predictive analytics, certain corporate and environmental indicators (eg: sales forecast, consumer behavior). Here it is the task of the data scientist to recognize the developments in the company environment at an early stage by extrapolation of known data .
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Data Science Basics :
The topic of data science is, in addition to data analytics and data mining, in
Business Intelligence today more important. There is a significant and growing demand for business, government and nonprofit professionals who can effectively work with large scale data.
“What’s behind the term Data Science?”
At the core of data science is the creation and implementation of automated methods to analyze large amounts of data and extract knowledge from them. The spectrum of data science ranges from genome to high energy physics. In doing so, the data scientist can uncover and create new branches of science and influence social sciences and humanities. The trend is likely to shift in the coming years in the field of mobile sensors . In academic research we will see an increasing number of traditional disciplines and new sub-disciplines.
Data Science Basics – short and sweet
The following points should always be kept in mind under the aspect of Data Science Basics , when it comes to the topic:
Mathematics and Statistics Skills :
A good data scientist must be able to understand what the data is saying, must have a solid grounding in linear algebra and an understanding of algorithms and statistics skills.
Machine Learning Concept :
Machine learning is the buzzword, but it’s inextricably linked to big data. Machine learning uses artificial intelligence algorithms to transform and learn data into meaningful values without being explicitly programmed.
Understanding Databases, Data Lakes, and Distributed Storage:
Data is stored in databases, data lakes, or distributed networks. The structure of the data repositories can always be different. What is important is to see big picture or think ahead when building up upcoming architectures.
Basics of Good Data Visualization and Reporting:
A data scientist does not have to become a graphic designer, but he also needs to understand how to create the data so that a layperson – like a manager or CEO – understands it.
Practice, practice and practice:
A data scientist must always stay in practice. He likes to develop favorite projects with open source data, participate in contests, collaborate with a network of scientists, and participate in boot camps. Do You Want to join Become Data Scientist Join Our Classroom Training “Data Science Training in Pune” and get Placed in MNC.
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