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.

Exploring Data with CODAP

2019 ◽  
Vol 112 (6) ◽  
pp. 473-476 ◽  
Author(s):  
Gemma F. Mojica ◽  
Christina N. Azmy ◽  
Hollylynne S. Lee
Keyword(s):  
Science Education ◽  
Data Science ◽  
Statistics Education ◽  
The Internet ◽  
Online Data ◽  
Web Browser ◽  
Web Based ◽  
Teachers And Students ◽  
And Mathematics ◽  
Analysis Platform

Concord Consortium's Common Online Data Analysis Platform (CODAP), a free Web-based data tool designed for students in grades 6-12 and higher, is continuously being updated and developed for diverse projects in data science, science education, and mathematics/statistics education (https://codap.concord.org/). Teachers and students can access CODAP without downloading software or registering for accounts. Although some Web-based technology tools provide certain features for free and require users to pay a fee to use additional features, CODAP has no hidden costs. Devices need only be connected to the Internet using an updated Web browser (Chrome is preferred). CODAP is not optimized (yet) for use on such touchscreen devices as tablets or iPads®.

scholarly journals SciPy and OpenCV as an interactive computing environment for computer vision

2015 ◽  
Vol 22 (1) ◽  
pp. 154
Author(s):  
Thiago Teixeira Santos
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Vision Research ◽  
Data Exploration ◽  
Computing Environment ◽  
Interactive Computing ◽  
Python Programming Language ◽  
Computing Environments ◽  
Python Programming

In research and development (R&D), interactive computing environments are a frequently employed alternative for data exploration, algorithm development and prototyping. In the last twelve years, a popular scientific computing environment flourished around the Python programming language. Most of this environment is part of (or built over) a software stack named SciPy Stack. Combined with OpenCV’s Python interface, this environment becomes an alternative for current computer vision R&D. This tutorial introduces such an environment and shows how it can address different steps of computer vision research, from initial data exploration to parallel computing implementations. Several code examples are presented. They deal with problems from simple image processing to inference by machine learning. All examples are also available as IPython notebooks.

scholarly journals The democratization of data science education

2017 ◽  
Author(s):  
Sean Kross ◽  
Roger D Peng ◽  
Brian S Caffo ◽  
Ira Gooding ◽  
Jeffrey T Leek
Keyword(s):  
Machine Learning ◽  
Science Education ◽  
Data Analysis ◽  
Data Science ◽  
Online Data ◽  
The Past ◽  
The Us ◽  
Science Curricula ◽  
The Impact ◽  
And Training

Over the last three decades data has become ubiquitous and cheap. This transition has accelerated over the last five years and training in statistics, machine learning, and data analysis have struggled to keep up. In April 2014 we launched a program of nine courses, the Johns Hopkins Data Science Specialization, which has now had more than 4 million enrollments over the past three years. Here the program is described and compared to both standard and more recently developed data science curricula. We show that novel pedagogical and administrative decisions introduced in our program are now standard in online data science programs. The impact of the Data Science Specialization on data science education in the US is also discussed. Finally we conclude with some thoughts about the future of data science education in a data democratized world.

scholarly journals Success Factors for Using Case Method in Teaching Applied Data Science Education

2021 ◽  
Vol 4 (1) ◽  
pp. 76
Author(s):  
Valentina Chkoniya
Keyword(s):  
Science Education ◽  
Data Science ◽  
Success Factors ◽  
Predictive Analytics ◽  
Teaching Method ◽  
Mining Machine ◽  
Case Method ◽  
Scientific Methods ◽  
Close Analysis ◽  
Science Educators

In a world where everything involves data, an application of it became essential to the decision-making process. The Case Method approach is necessary for Data Science education to expose students to real scenarios that challenge them to develop the appropriate skills to deal with practical problems by providing solutions for different activities. Data science combines multiple fields like statistics, scientific methods, and data analysis to extract value from data, being an umbrella term used for multiple industries, such as data analytics, data mining, machine learning, big data, business intelligence, and predictive analytics. This paper gives an overview of success factors for using the Case Method in teaching Applied Data Science education. Showing that close analysis provides a deeper understanding of implications, connects theory to practice, and classes unfold without a detailed script when successful instructors simultaneously manage content and process. This synthesis of current research can be used by Applied Data Science educators to more effectively plan the use of the Case Method as one possible teaching method.

scholarly journals The democratization of data science education

2017 ◽  
Author(s):  
Sean Kross ◽  
Roger D Peng ◽  
Brian S Caffo ◽  
Ira Gooding ◽  
Jeffrey T Leek
Keyword(s):  
Machine Learning ◽  
Science Education ◽  
Data Analysis ◽  
Data Science ◽  
Online Data ◽  
The Past ◽  
The Us ◽  
Science Curricula ◽  
The Impact ◽  
And Training

Over the last three decades data has become ubiquitous and cheap. This transition has accelerated over the last five years and training in statistics, machine learning, and data analysis have struggled to keep up. In April 2014 we launched a program of nine courses, the Johns Hopkins Data Science Specialization, which has now had more than 4 million enrollments over the past three years. Here the program is described and compared to both standard and more recently developed data science curricula. We show that novel pedagogical and administrative decisions introduced in our program are now standard in online data science programs. The impact of the Data Science Specialization on data science education in the US is also discussed. Finally we conclude with some thoughts about the future of data science education in a data democratized world.

scholarly journals Shaping the foundations of programming languages

2021 ◽  
Vol 64 (6) ◽  
pp. 120
Author(s):  
Leah Hoffmann
Keyword(s):  
Science Education ◽  
Computer Science ◽  
Programming Languages ◽  
Computer Science Education ◽  
The Future ◽  
Turing Award

ACM A.M. Turing Award recipients Alfred Aho and Jeffrey Ullman discuss their early work, the 'Dragon Book,' and the future of 'live' computer science education.

Implementation of Selection Sort Algorithm in Various Programming Languages

2021 ◽  
Vol 10 (4) ◽  
pp. 2249-2255
Keyword(s):  
Programming Languages ◽  
Data Science ◽  
Efficient Algorithms ◽  
Huge Number ◽  
Running Time ◽  
Fast Running ◽  
Python Language ◽  
Sort Algorithm

Sorting algorithmdeals with the arrangement of alphanumeric data in static order.It plays an important roleinthe field of data science. Selection sort is one ofthe simplest and efficient algorithms which can be applied for the huge number of elements it works likeby giving list of unsorted information, the calculation which breaksintotwo partitions. One section has all the sorted information and another sectionhas all thestaying unsorted information. The calculation rehashes itself, by finding the smallestcomponentinside the rundown of unsorted information and swappingitwith the furthest left component, in the end setting everything straight information.This researchpresents the implementationof selection sort usingC/C++, Python, and Rust and measuredthetime complexity. After experiment,we have collectedtheresults in terms of running time, andanalyzed the outcomes.It was observed that python language hasvery smallamount of line of code, and it also consumesless storage and fast running time then other two languages.

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