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.

Data Analysis on Global Stratification

2020 ◽  
pp. 355-378
Author(s):  
Kirti Raj Bhatele ◽  
Stuti Singhal ◽  
Muktasha R. Mithora ◽  
Sneha Sharma
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Graphical Representation ◽  
Heat Maps ◽  
Python Language ◽  
Graphical Data ◽  
Sentence Form ◽  
The World ◽  
Table Data ◽  
Numeric Data

This chapter will guide you through the modeling, uses, and trends in data analysis and data science. The authors focus on the importance of pictorial data in replacement of numeric data. In most situations, graphical representation of data can present the information more distinctly, informative, and in less space than the same information requires in sentence form. This chapter provides a brief knowledge about representing data to more understandable form such that any person whether layman or not can understand it without any difficulty. This chapter also deals with the software Tableau which we use to convert the table data into graphical data. This Chapter contains 11 heat maps related to the world economies and their detailed study on several different topics. It will also give light on the basics of Python Language and its various algorithm studies to compare all the world economies based on their development.

TRIANGULATING DISJOINT JORDAN CHAINS

1994 ◽  
Vol 04 (04) ◽  
pp. 475-481 ◽  
Author(s):  
REUVEN BAR-YEHUDA ◽  
BERNARD CHAZELLE
Keyword(s):  
Lower Bound ◽  
Efficient Method ◽  
Simple Polygon ◽  
Efficient Algorithms ◽  
Log P ◽  
Point Location ◽  
Running Time ◽  
Geometric Characteristics ◽  
Interval Trees ◽  
Polygonal Chains

Recent advances on polygon triangulation have yielded efficient algorithms for a large number of problems dealing with a single simple polygon. If the input consists of several disjoint polygons, however, it is often desirable to merge them in preprocessing so as to produce a single polygon that retains the geometric characteristics of its individual components. We give an efficient method for doing so, which combines a generalized form of Jordan sorting with the efficient use of point location and interval trees. As a corollary, we are able to triangulate a collection of p disjoint Jordan polygonal chains in time O (n + p ( log p)1+ε), for any fixed ε > 0, where n is the total number of vertices. A variant of the algorithm gives a running time of O ((n + p log p) log log p). The performance of these solutions approaches the lower bound of Ω (n + p log p).

scholarly journals Research on Interactive Online Teaching of Python Language Foundation Course

2021 ◽  
Vol 4 (1) ◽  
pp. 36
Author(s):  
Zhiwen Zhu
Keyword(s):  
Information Technology ◽  
Programming Languages ◽  
Teaching Methods ◽  
Online Teaching ◽  
Learning Effect ◽  
Colleges And Universities ◽  
Traditional Teaching ◽  
Python Language ◽  
Teaching Efficiency ◽  
Technology Course

Python language, as one of the most popular programming languages, has become the preferred programming course in Colleges and universities. However, in traditional teaching, the dull and monotonous teaching of Python course leads to the low teaching efficiency of Python course and the unsatisfactory learning effect of students. Therefore, there is an urgent need for new teaching methods to improve classroom efficiency. Adopting Python interactive online teaching can not only improve the teaching efficiency of Python course, but also promote the reform of information technology course.

Big Data Optimization for Customer Discounts in Cloud Computing Environment

2017 ◽  
pp. 195-224
Author(s):  
Raghvendra Kumar ◽  
Prasant Kumar Pattnaik ◽  
Priyanka Pandey
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Efficient Algorithms ◽  
Proof Of Concept ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Running Time ◽  
Simulation Process ◽  
Current Implementation ◽  
Simulation Systems

Large companies have different methods of doing this, one of which is to run sales simulations. Such simulation systems often need to perform complex calculations over large amounts of data, which in turn requires efficient models and algorithms. This chapter intends to evaluate whether it is possible to optimize and extend an existing sales system called PCT, which is currently suffering from unacceptably high running times in its simulation process. This is done through analysis of the current implementation, followed by optimization of its models and development of efficient algorithms. The performances of these optimized and extended models are compared to the existing one in order to evaluate their improvement. The conclusion of this chapter is that the simulation process in PCT can indeed be optimized and extended. The optimized models serve as a proof of concept, which shows that results identical to the original system's can be calculated within < 1% of the original running time for the largest customers.

Introduction to Machine Learning for Chemists: An Undergraduate Course Using Python Notebooks for Visualization, Data Processing, Data Analysis, and Data Modeling

2021 ◽  
Author(s):  
Deborah Lafuente ◽  
Brenda Cohen ◽  
Guillermo Fiorini ◽  
Agustín García ◽  
Mauro Bringas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Real Data ◽  
Free Software ◽  
Chemistry Student ◽  
Chemistry Students ◽  
Pervasive Technology ◽  
Python Language ◽  
Processing Data ◽  
Depth And Complexity

Machine Learning, a subdomain of Artificial intelligence, is a pervasive technology that would mold how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a course that introduces machine learning for chemistry students based on a set of Python Notebooks and assignments. Python language, one of the most popular programming languages, allows for free software and resources, which ensures availability. The course is constructed for students without previous experience in programming, leading to an incremental progression in depth and complexity that covers both programming and machine learning concepts. The examples used are related to real data from physicochemical characterizations of wines, producing an attractive material that captures the interest of students. Topics included are Introduction to Python, Basic Statistics, Data Visualization and Dimension Reduction, Classification, and Regression.

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