Keeping the human in the data scientist: Shaping human‐centered data science education

2019 ◽  
Vol 56 (1) ◽  
pp. 601-603
Author(s):  
Theresa Dirndorfer Anderson ◽  
Nicola Parker
Keyword(s):  
Science Education ◽  
Data Science ◽  
Data Scientist

scholarly journals Data Wrangling using Python

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3491-3495
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Research Work ◽  
Research Area ◽  
Mapping Data ◽  
Data Format ◽  
Data Analyst ◽  
Data Engineering ◽  
Data Scientist ◽  
Term Data

The term Data Engineering did not get much popularity as the terminologies like Data Science or Data Analytics, mainly because the importance of this technique or concept is normally observed or experienced only during working with data or handling data or playing with data as a Data Scientist or Data Analyst. Though neither of these two, but as an academician and the urge to learn, while working with Python, this topic ‘Data engineering’ and one of its major sub topic or concept ‘Data Wrangling’ has drawn attention and this paper is a small step to explain the experience of handling data which uses Wrangling concept, using Python. So Data Wrangling, earlier referred to as Data Munging (when done by hand or manually), is the method of transforming and mapping data from one available data format into another format with the idea of making it more appropriate and important for a variety of relatedm purposes such as analytics. Data wrangling is the modern name used for data pre-processing rather Munging. The Python Library used for the research work shown here is called Pandas. Though the major Research Area is ‘Application of Data Analytics on Academic Data using Python’, this paper focuses on a small preliminary topic of the mentioned research work named Data wrangling using Python (Pandas Library).

How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

scholarly journals An overview of two open interactive computing environments useful for data science education

JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 159-165
Author(s):  
Robert Hoyt ◽  
Victoria Wangia-Anderson
Keyword(s):  
Science Education ◽  
Programming Languages ◽  
Data Science ◽  
Predictive Analytics ◽  
Data Exploration ◽  
Online Data ◽  
Education Benefits ◽  
Interactive Computing ◽  
Data Analyses ◽  
Computing Environments

Abstract Objective To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education. Methods and Materials The features of two online data science platforms are outlined. Both are useful for new data projects and both are integrated with common programming languages used for data analysis. One platform focuses more on data exploration and the other focuses on containerizing, visualization, and sharing code repositories. Results Both data science platforms are open, free, and allow for collaboration. Both are capable of visual, descriptive, and predictive analytics Discussion Data science education benefits by having affordable open and collaborative platforms to conduct a variety of data analyses. Conclusion Open collaborative data science platforms are particularly useful for teaching data science skills to clinical and nonclinical informatics students. Commercial data science platforms exist but are cost-prohibitive and generally limited to specific programming languages.

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