Ah yes, the debate about which programming language, Python or R, is better for data science. In this series, I am considering machine learning and artificial intelligence as included in the term data science. This is almost the data science equivalent of tabs vs spaces for software engineers, at least at the time of this writing. This series is intended to be a somewhat definitive guide on this topic, including recommendations for languages and packages (aka libraries) applicable to different use cases, including data science in production and big data scenarios. This series is not intended to give side-by-side code comparisons, as there are plenty of other articles covering that. From my experience, which language to use is one of, if not the first question that someone interested in learning data science wants answered.

Refactoring6 is something software developers like to do. But do they refactor as much as they would like? Are there barriers that prevent them from doing so? Refactoring is an important tool for improving quality. Many development methodologies rely on refactoring, especially for agile methodologies but also in more plan-driven organizations. If barriers exist, they would undermine the effectiveness of many product-development organizations. We conducted a large-scale survey in 2009 of 3,785 practitioners' use of object-oriented concepts,7 including questions as to whether they would refactor to deal with certain design problems. We expected either that practitioners would tell us our choice of design principles was inappropriate for basing a refactoring decision or that refactoring is the right decision to take when designs were believed to have quality problems. However, we were told the decision of whether or not to refactor was due to non-design considerations. It is now eight years since the survey, but little has changed in integrated development environment (IDE) support for refactoring, and what has changed has done little to address the barriers we identified.

This paper contains analysis of concept of a class within different object-oriented knowledge representation models. The main attention is paid to structure of the class and its efficiency in the context of data storage, using object-relational mapping. The main achievement of the paper is extension of concept of homogeneous class of objects by introducing concepts of single-core and multi-core inhomogeneous classes of objects, which allow simultaneous defining of a few different types within one class of objects, avoiding duplication of properties and methods in representation of types, decreasing sizes of program codes and providing more efficient information storage in the databases. In addition, the paper contains results of experiment, which show that data storage in relational database, using proposed extensions of the class, in some cases is more efficient in contrast to usage of homogeneous classes of objects.

Java is arguably the single most important technology out there. Core Java Programming is an excellent introduction in to the world of Java programming. The instructor will take you through the basics of Java syntax and the complexities of Object Oriented Programming. This course is a stand-alone course, however it would be a huge aid to the online student who is taking a self-directed course, an individual who is trying to learn how to program. At the end of this course, you will be well versed with how to program in Java from the very basic level to an intermediate level of programming.

However, it remains elusive whether and how object percepts alone, or concomitantly a nonphysical attribute of the objects ("learned"), are decoded from perirhinal activities. By combining monkey psychophysics with optogenetic and electrical stimulations, we found a focal spot of memory neurons where both stimulations led monkeys to preferentially judge presented objects as "already seen." In an adjacent fringe area, where neurons did not exhibit selective responses to the learned objects, electrical stimulation induced the opposite behavioral bias toward "never seen before," whereas optogenetic stimulation still induced bias toward "already seen." These results suggest that mnemonic judgment of objects emerges via the decoding of their nonphysical attributes encoded by perirhinal neurons.

About this course: Welcome to our course on Object Oriented Programming in Java using data visualization. People come to this course with many different goals -- and we are really excited to work with all of you! Some of you want to be professional software developers, others want to improve your programming skills to implement that cool personal project that you've been thinking about, while others of you might not yet know why you're here and are trying to figure out what this course is all about. This is an intermediate Java course. We recommend this course to learners who have previous experience in software development or a background in computer science.

About this course: This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization's mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.

This is a real mega-work on advanced topics and implementations in Python. My only reservation is one I have about all his books, that language gets very contorted and unclear in the middle of things that need elucidation. Sometimes I'm unsure I've read something more than gibberish. Often he could explain things in a far simpler way. His drive to appease different computer-language religions and Python versions generates a lot of clutter in the learning process.

Unified modeling language (UML)6 is the de facto standard for representing object-oriented designs. It does a fine job of recording designs, but it has a severe problem: its diagrams don't convey what humans need to know, making the diagrams difficult to understand. This is why most software developers use UML only when forced to.1 For example, the UML diagrams in Figures 1 and 2 portray the embedded software in a fax machine. While these diagrams are attractive, they do not even tell you which objects control which others. Which object is the topmost controller over this fax machine? Which object(s) control the Modem object?

The ideal candidate should be passionate about working on cutting edge research and analytical services for Fortune 500 companies, global pharma/biotech firms and leaders in industries such as finance, energy and life sciences. The Data Scientist will be a contributing member to client engagements and have the opportunity to work with our network of world-class experts and thought leaders. Job Functions and Responsibilities The candidate Data Scientist will help develop, maintain and teach new tools and methodologies related to data science and high performance computing. This position will also help Analysis Group in maintaining our leadership position in terms of advancing methodology and data computations. Key responsibilities for this position will include: Working with project teams to address data science/computing challenges Identifying opportunities for technology to enhance service offerings Acting as a resource and participating in client engagements and research as part of the project team Maintaining up-to-date knowledge of computing tools, providing technical training and helping to grow the in-house knowledge base Presenting research at selected conferences; serving as an expert More specifically, Data Scientist will also be responsible for: Optimizing procedures for managing and accessing large databases (e.g., insurance claims, electronic health records, financial transactions) Creating interactive analytics portals and data visualizations (e.g., using R/Shiny, Python/Flask, D3) Building and maintaining high performance computing (HPC) tools on grid and cloud computing environments Developing and reviewing macros and packages in SAS, R, Python and other Object Oriented Languages Establishing optimized procedures for repetitive or computationally intensive tasks (C, C, Cuda-C) Using natural language processing methodologies to work with EMR data, social media data and other unstructured data Qualifications Experience applying multiple software tools and languages to provide data-driven analytical solutions to decision makers or research teams Strong credentials and experience in database management and data visualization Background in Statistics/Econometrics or Biostatistics Ideally PhD in Computer Science, Mathematics, Statistics or Economics with relevant experience.