Building an Effective Data Science Practice

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future.

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From AI ethics principles to data science practice: a reflection and a gap analysis based on recent frameworks and practical experience

AI and Ethics ◽

10.1007/s43681-021-00127-3 ◽

2022 ◽

Author(s):

Ilina Georgieva ◽

Claudio Lazo ◽

Tjerk Timan ◽

Anne Fleur van Veenstra

Keyword(s):

Data Science ◽

Gap Analysis ◽

Practical Experience ◽

Science Practice ◽

Ai Ethics

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Introduction to Data Science

Advances in Data Mining and Database Management - Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics ◽

10.4018/978-1-7998-3053-5.ch001 ◽

2021 ◽

pp. 1-18

Author(s):

M. Govindarajan

Keyword(s):

Data Analysis ◽

Predictive Modeling ◽

Data Science ◽

Science Data ◽

Science Practice ◽

Scientific Methods ◽

Exploratory Data ◽

Product Creation ◽

Modeling Data ◽

Term Data

This chapter focuses on introduction to the field of data science. Data science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. The term data science has emerged because of the evolution of mathematical statistics, data analysis, and big data. Data science helps to discover hidden patterns from the raw data. It enables to translate a business problem into a research project and then translate it back into a practical solution. The purpose of this chapter is to provide emphasis on integration and synthesis of concepts, techniques, applications, and tools to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.

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