Data Science (The MIT Press Essential Knowledge) PDF 2023.

 Data Science (The MIT Press Essential Knowledge) PDF 2023.

What is a Data Scientist?

The data scientist profession requires a range of technical and domain-related skills to manage and analyze data to solve business problems.

 The data scientist is partly a mathematician, partly a business analyst and partly a computer scientist. A good data scientist is able to detect trends and patterns in the data and knows how to use them for useful and actionable results. 

Data scientists are at the forefront of modern business, transforming the way we work. Data Scientist Skills Diagram: History of Data Scientists: In 2001, a computer scientist, William S. Cleveland, wrote an article entitled “Data Science: An Action Plan for Expanding the Technical Area of Statistics”. 

This article presented Data Science as a discipline of applied statisticians. That was only 20 years ago, and the world of technology and business has evolved rapidly since then. As this is a relatively new career path, current data scientists come from a wide variety of backgrounds and backgrounds. Many start their careers as statisticians, mathematicians or data scientists. 

But as access to computers, artificial intelligence (AI) and data learning tools has become commonplace, the role has evolved. 

A Data Scientist is no longer confined to the IT department; it is now an integral part of the entire company. Due to its expansion and crucial influence on the company, the role of the Data Scientist requires a logical and innovative person, able to translate data information into business strategy.

 What is qualifications to become in Data Scientist? In the last ten years, higher education institutions have developed specific courses for data scientists. Those interested in working in this field can obtain a bachelor’s or master’s degree in data science from a large number of universities. 

 The courses taken by Data Scientists generally cover statistical modeling, data management, data visualization, machine learning, software engineering, data ethics, research design and user experience. They can learn SQL, Python, Perl and a series of other programming languages such as R. 

They will get familiar with Hadoop, Pig, Spark, Hive and MapReduce. However, with the availability of more open-source software and commercialized data science tools, what people learn today could soon become obsolete. Therefore, data scientists need to be agile and continue to develop new skills and techniques within the sector.

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