Programming Languages in Data Science: a Comparison from a Database Angle

2021 ◽  
Author(s):  
Xiantian Zhou ◽  
Carlos Ordonez
Keyword(s):  
Programming Languages ◽  
Data Science

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.

scholarly journals CONTEMPORARY DATA SCIENCE FOR FINANCE STUDENTS. ESSENTIAL FEATURES OF COMMONLY USED STATISTICAL SOFTWARE - A COMPARATIVE STUDY

2019 ◽  
Vol 9 (2) ◽  
pp. 14-20
Author(s):  
Mădălina Viorica ION (MANU) ◽  
◽  
Ilie VASILE ◽  
Keyword(s):  
Comparative Study ◽  
Programming Languages ◽  
Data Science ◽  
Relevant Literature ◽  
Global Scale ◽  
Statistical Software ◽  
Business Excellence ◽  
Romanian Literature ◽  
Basic Features ◽  
Python Programming

This paper inventories some of the essential traits of the software preferred by researchers, students and professors, such as R or RStudio, or Matlab and also their possible utilizations. In order to fill the gap in the Romanian literature and help finance students in choosing proper tools according to the research purpose, this comparative study aims at bringing a fresh, useful perspective in the relevant literature. In Romania, the use of R was the focus of several international conferences on official statistics held in Bucharest, and others having business excellence, innovation and sustainability as purpose. In this time, at global scale, R and Python programming languages are considered the lingua franca of data science, as common statistical software used both in corporations and academia. In this paper, I analyze basic features of such software, with the purpose of application in finance.

scholarly journals What more than a hundred project groups reveal about teaching visualization

2020 ◽  
Vol 23 (5) ◽  
pp. 895-911 ◽  
Author(s):  
Michael Burch ◽  
Elisabeth Melby
Keyword(s):  
Programming Languages ◽  
Information Visualization ◽  
Teaching Strategies ◽  
Data Science ◽  
Major Goal ◽  
Wide Range ◽  
Project Groups ◽  
Small Project ◽  
Programming Skills ◽  
The Given

Abstract The growing number of students can be a challenge for teaching visualization lectures, supervision, evaluation, and grading. Moreover, designing visualization courses by matching the different experiences and skills of the students is a major goal in order to find a common solvable task for all of them. Particularly, the given task is important to follow a common project goal, to collaborate in small project groups, but also to further experience, learn, or extend programming skills. In this article, we survey our experiences from teaching 116 student project groups of 6 bachelor courses on information visualization with varying topics. Moreover, two teaching strategies were tried: 2 courses were held without lectures and assignments but with weekly scrum sessions (further denoted by TS1) and 4 courses were guided by weekly lectures and assignments (further denoted by TS2). A total number of 687 students took part in all of these 6 courses. Managing the ever growing number of students in computer and data science is a big challenge in these days, i.e., the students typically apply a design-based active learning scenario while being supported by weekly lectures, assignments, or scrum sessions. As a major outcome, we identified a regular supervision either by lectures and assignments or by regular scrum sessions as important due to the fact that the students were relatively unexperienced bachelor students with a wide range of programming skills, but nearly no visualization background. In this article, we explain different subsequent stages to successfully handle the upcoming problems and describe how much supervision was involved in the development of the visualization project. The project task description is given in a way that it has a minimal number of requirements but can be extended in many directions while most of the decisions are up to the students like programming languages, visualization approaches, or interaction techniques. Finally, we discuss the benefits and drawbacks of both teaching strategies. Graphic abstract

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.

Comparative Analysis of Data Visualization Libraries Matplotlib and Seaborn in Python

2021 ◽  
Vol 10 (1) ◽  
pp. 277-281
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Programming Languages ◽  
Data Visualization ◽  
Data Science ◽  
Computational Modelling ◽  
And Mathematics ◽  
Python Programming ◽  
The Comparative Study

With the tremendous growth in the areas of computing, statistics, and mathematics has led to the rise of the emerging field of expertise, named ‘Data Science’. This paper focuses on the comparative study and evaluation of the data science libraries used in Python Programming Languages, named ‘Matplotlib’ and ‘Seaborn’. The sole purpose of this paper is to identify areas and evaluate the strengths and weaknesses of these libraries with the implementation of code and identify the classification of the univariate and multivariate plotting of data concerned with patterns of data visualization and computational modelling of data in the form of processed information using techniques of big data and data mining

Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language

2019 ◽  
Vol 44 (3) ◽  
pp. 348-361 ◽  
Author(s):  
Jiangang Hao ◽  
Tin Kam Ho
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Machine Learning Methods ◽  
Python Programming Language ◽  
And Behavior ◽  
Python Programming

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

scholarly journals Commonly used Algorithms in Data Science Along with Internal Logics and Implementations through R Programming

2020 ◽  
Vol 9 (3) ◽  
pp. 2649-2652
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Dimensionality Reduction ◽  
Programming Languages ◽  
Recommender Systems ◽  
Rural Areas ◽  
Data Science ◽  
Online Platforms ◽  
R Programming ◽  
Urban And Rural Areas

The terms machine learning, deep learning and data science are buzz words now a days. The usage of these techniques with some technologies like R and Python is most common in the industry and academics. The current work is dealing with the inherent logics existing in the algorithms like Classification, Dimensionality reduction and Recommender systems along with the suitable examples. Some of the applications mentioned here like Facebook, Twitter and LinkedIn to exploit the usage of these algorithms in their daily usage. The discussion about online platforms like Amazon, Flipkart are other areas where the recommender systems were most commonly used algorithms. The outcome of the work is the logical things hidden in the usage of the algorithms and the implementation wise which are packages and functions helpful for the implementation of the algorithms. The belief is the work will be helpful for the researchers and academicians in the context of algorithmic perspective and they can extend the work by contributing their thoughts and views on the same work. Unlike in the normal programming, R/Python simplifies the logic of algorithms so that the lines of code and understanding of the problem is bit simple when compared with general programming languages. The work explains the mail respondents related to the allocation of the house by the company as a response to their mail by considering Urban, semi-urban and rural areas of the customers, the income range of the customers also observed in the allocation of the house. The implementations are with R by using classification and the corresponding results were published with the explanation of the values found in the implementation.

scholarly journals COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics

2021 ◽  
Vol 3 ◽  
Author(s):  
Ahmed Al-Hindawi ◽  
Ahmed Abdulaal ◽  
Timothy M. Rawson ◽  
Saleh A. Alqahtani ◽  
Nabeela Mughal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Data Science ◽  
The Other ◽  
Machine Learning Techniques ◽  
Prognostic Models ◽  
Learning Techniques ◽  
Classical Statistics ◽  
Open Datasets ◽  
The Impact

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.

Data Science Programming Languages

2020 ◽  
pp. 153-196
Author(s):  
Usman Qamar ◽  
Muhammad Summair Raza
Keyword(s):  
Programming Languages ◽  
Data Science

scholarly journals Hack weeks as a model for data science education and collaboration

2018 ◽  
Vol 115 (36) ◽  
pp. 8872-8877 ◽  
Author(s):  
Daniela Huppenkothen ◽  
Anthony Arendt ◽  
David W. Hogg ◽  
Karthik Ram ◽  
Jacob T. VanderPlas ◽  
...  
Keyword(s):  
Programming Languages ◽  
Data Science ◽  
Low Cost ◽  
Self Report ◽  
Academic Disciplines ◽  
Project Work ◽  
Science Methods ◽  
Using Data ◽  
Modes Of Interaction ◽  
Impact Data

Across many scientific disciplines, methods for recording, storing, and analyzing data are rapidly increasing in complexity. Skillfully using data science tools that manage this complexity requires training in new programming languages and frameworks as well as immersion in new modes of interaction that foster data sharing, collaborative software development, and exchange across disciplines. Learning these skills from traditional university curricula can be challenging because most courses are not designed to evolve on time scales that can keep pace with rapidly shifting data science methods. Here, we present the concept of a hack week as an effective model offering opportunities for networking and community building, education in state-of-the-art data science methods, and immersion in collaborative project work. We find that hack weeks are successful at cultivating collaboration and facilitating the exchange of knowledge. Participants self-report that these events help them in both their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration, and cultivate best practices.

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