scholarly journals Python: A Quintessential approach towards Data Science

2021 ◽  
Vol 9 (VI) ◽  
pp. 3018-3024
Author(s):  
Aniket M. Wazarkar
Keyword(s):  
Big Data ◽  
Programming Languages ◽  
Data Science ◽  
Object Oriented Programming ◽  
Software Applications ◽  
Efficient Data ◽  
Exploratory Data ◽  
Data Scientist ◽  
Analytical Tools ◽  
Oriented Programming Language

Python is an interpreted object-oriented programming language that is sustainably procuring vogue in the field of data science and analytics by fabricating complex software applications. Establishing a righteous nexus between developers and data scientists. Python has undoubtedly become paramount for data scientists mindful of cosmic and robust standard libraries which are used for analyzing and visualizing the data. Data scientists have to deal with the exceedingly large amount of data alias as big data. With elementary usage and a vast set of python libraries, Python has doubtlessly become an admired option to handle big data. Python has developed and evolved analytical tools which can help data scientist in developing machine learning models, web services, data mining, data classification, exploratory data analysis, etc. In this paper, we will scrutinize various tools which are used by python programmers for efficient data analytics, their scope with contrast to other programming languages.

scholarly journals Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2772 ◽  
Author(s):  
Aguinaldo Bezerra ◽  
Ivanovitch Silva ◽  
Luiz Affonso Guedes ◽  
Diego Silva ◽  
Gustavo Leitão ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Exchange ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Data Driven ◽  
Industrial Data ◽  
Industrial Big Data ◽  
Data Driven Approach ◽  
Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

scholarly journals Evaluating the GO Programming Language with Design Patterns

2021 ◽  
Author(s):  
◽  
Frank Schmager
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Design Patterns ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Application Framework ◽  
Evaluation Design ◽  
Design And Implementation ◽  
Oriented Programming Language

<p>GO is a new object-oriented programming language developed at Google by Rob Pike, Ken Thompson, and others. GO has the potential to become a major programming language. GO deserves an evaluation.  Design patterns document reoccurring problems and their solutions. The problems presented are programming language independent. Their solutions, however, are dependent on features programming languages provide. In this thesis we use design patterns to evaluate GO. We discuss GO features that help or hinder implementing design patterns, and present a pattern catalogue of all 23 Gang-of-Four design patterns with GO specific solutions. Furthermore, we present GoHotDraw, a GO port of the pattern dense drawing application framework JHotDraw. We discuss design and implementation differences between the two frameworks with regards to GO.</p>

scholarly journals Cooking Up Knowledge From Big Data Using Data Science

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrea Rau
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Programming Language ◽  
Data Science ◽  
Weather Forecasting ◽  
Original Question ◽  
Data Scientist ◽  
Using Data ◽  
The Way

Data collected in very large quantities are called big data, and big data has changed the way we think about and answer questions in many different fields, like weather forecasting and biology. With all this information available, we need computers to help us store, process, analyze, and understand it. Data science combines tools from fields like statistics, mathematics, and computer science to find interesting patterns in big data. Data scientists write step-by-step instructions called algorithms to teach computers how to learn from data. To help computers understand these instructions, algorithms must be translated from the original question asked by a data scientist into a programming language—and the results must be translated back, so that humans can understand them. That means that data scientists are data detectives, programmers, and translators all in one!

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

A paradigmatic object-oriented programming language: Design, static typing and semantics

1994 ◽  
Vol 4 (2) ◽  
pp. 127-206 ◽  
Author(s):  
Kim B. Bruce
Keyword(s):  
Fixed Points ◽  
Programming Languages ◽  
Programming Language ◽  
Type System ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Language Design ◽  
Type Checking ◽  
Basic Features ◽  
Oriented Programming Language

AbstractTo illuminate the fundamental concepts involved in object-oriented programming languages, we describe the design of TOOPL, a paradigmatic, statically-typed, functional, object-oriented programming language which supports classes, objects, methods, hidden instance variables, subtypes and inheritance.It has proven to be quite difficult to design such a language which has a secure type system. A particular problem with statically type checking object-oriented languages is designing typechecking rules which ensure that methods provided in a superclass will continue to be type correct when inherited in a subclass. The type-checking rules for TOOPL have this feature, enabling library suppliers to provide only the interfaces of classes with actual executable code, while still allowing users to safely create subclasses. To achieve greater expressibility while retaining type-safety, we choose to separate the inheritance and subtyping hierarchy in the language.The design of TOOPL has been guided by an analysis of the semantics of the language, which is given in terms of a model of the F-bounded second-order lambda calculus with fixed points at both the element and type level. This semantics supports the language design by providing a means to prove that the type-checking rules are sound, thus guaranteeing that the language is type-safe.While the semantics of our language is rather complex, involving fixed points at both the element and type level, we believe that this reflects the inherent complexity of the basic features of object-oriented programming languages. Particularly complex features include the implicit recursion inherent in the use of the keyword, self, to refer to the current object, and its corresponding type, MyType. The notions of subclass and inheritance introduce the greatest semantic complexities, whereas the notion of subtype is more straightforward to deal with. Our semantic investigations lead us to recommend caution in the use of inheritance, since small changes to method definitions in subclasses can result in major changes to the meanings of the other methods of the class.

scholarly journals A New Modern Object-Oriented Programming Languages

2021 ◽  
pp. 683-685
Author(s):  
Prof. Shilpa Shitole ◽  
Rohit Maurya ◽  
Tanaya Pawar ◽  
Siya Randhe
Keyword(s):  
Open Source ◽  
Programming Languages ◽  
Programming Language ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Logical Programming ◽  
Ways Of Thinking ◽  
Way Of Thinking ◽  
Programming Paradigms ◽  
Oriented Programming Language

Industries evolve. Our thinking changes as well. Programming languages need evolvement too. “The thing is that ideas for new features with its ways of thinking will be flourished, and so perfectly designed those languages won’t be perfect anymore.” Where did logical programming go? “Notice that you can use this paradigm and just provide a set of constraints for a website and expect the website to develop automatically based on them.” It is possible to implement that. Likewise, new paradigms will sooner or later be born. It can’t be that we’ve explored everything. “Technologies are born likewise the old way of thinking, which represents by the previous programming languages might not be adequate. This project is an open-source modern object-oriented programming language that aims to bridge the gap between modern expressive programming paradigms like python and strictly typed rigid languages like Java and C#. Our goal is to provide the usefulness of an object-oriented programming language while holding the simplicity of an expressive programming language without having to sacrifice performance.

scholarly journals Python the game changer in the field of Machine Learning, Data Science and IoT: A Review

2021 ◽  
Vol 9 (8) ◽  
pp. 1827-1837
Author(s):  
Prithwish Parial
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Experience ◽  
Object Oriented Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
High Level ◽  
Learning Data ◽  
Game Changer ◽  
Oriented Programming Language

Abstract: Python is the finest, easily adoptable object-oriented programming language developed by Guido van Rossum, and first released on February 20, 1991 It is a powerful high-level language in the recent software world. In this paper, our discussion will be an introduction to the various Python tools applicable for Machine learning techniques, Data Science and IoT. Then describe the packages that are in demand of Data science and Machine learning communities, for example- Pandas, SciPy, TensorFlow, Theano, Matplotlib, etc. After that, we will move to show the significance of python for building IoT applications. We will share different codes throughout an example. To assistance, the learning experience, execute the following examples contained in this paper interactively using the Jupiter notebooks. Keywords: Machine learning, Real world programming, Data Science, IOT, Tools, Different packages, Languages- Python.

scholarly journals Evaluating the GO Programming Language with Design Patterns

2021 ◽  
Author(s):  
◽  
Frank Schmager
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Design Patterns ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Application Framework ◽  
Evaluation Design ◽  
Design And Implementation ◽  
Oriented Programming Language

<p>GO is a new object-oriented programming language developed at Google by Rob Pike, Ken Thompson, and others. GO has the potential to become a major programming language. GO deserves an evaluation.  Design patterns document reoccurring problems and their solutions. The problems presented are programming language independent. Their solutions, however, are dependent on features programming languages provide. In this thesis we use design patterns to evaluate GO. We discuss GO features that help or hinder implementing design patterns, and present a pattern catalogue of all 23 Gang-of-Four design patterns with GO specific solutions. Furthermore, we present GoHotDraw, a GO port of the pattern dense drawing application framework JHotDraw. We discuss design and implementation differences between the two frameworks with regards to GO.</p>

scholarly journals Big Data Science and EXASOL as Big Data Analytics tool

2019 ◽  
Vol 8 (9S) ◽  
pp. 933-937
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
System Architectures ◽  
Huge Data ◽  
Exploratory Approach ◽  
Data Scientist ◽  
Using Data

Big data and Data science are the two top trends of recent years. Both can be combined together as big data science. This leads to the demand for new system architectures which facilitates the development of processes which can handle huge data volumes without deterring the agility, flexibility and the interactive feel which suits the exploratory approach of a data scientist. Businesses today have found ways of using data as the principal factor for value generation. These data-driven businesses apply a variety of data tools as data analysis is one of the chief elements in this process. In order to raise data science to the new computational level that is required to meet the challenges of big data and interactive advanced analytics, EXASOL has introduced a new technological approach. This tool enables us more effective and easy data analysis.

Data Science is Here

2020 ◽  
pp. 108-127
Author(s):  
Dimitar Grozdanov Christozov ◽  
Katia Rasheva-Yordanova ◽  
Stefka Toleva-Stoimenova
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Science ◽  
Informed Decision Making ◽  
Knowledge And Skills ◽  
Analytical Skills ◽  
Analytical Thinking ◽  
Data Scientist ◽  
Primary Focus ◽  
Science Training

With the advent of big data, the search for respective data experts has become more intensive. This study aims to discuss data scientist skills and some topical issues that are related to data specialist profiles. A complex competence model has been deployed, dividing the skills into three groups: hard, soft, and analytical skills. The primary focus is on analytical thinking as one of the key competences of the successful data scientist taking into account the trans-discipline nature of data science. The chapter considers a new digital divide between the society and this small group of people that make sense out of the vast data and help the organization in informed decision making. As data science training needs to be business-oriented, the curricula of the Master's degree in Data Science is compared with the required knowledge and skills for recruitment.

Sign in / Sign up

Export Citation Format

Share Document