The popularity of data science has often been credited to the increased data availability growing from static to live data, more powerful computing, and an emphasis on analytics-driven decisions made in businesses. The two most popular programming languages for data science are Python and R which are excellent tools in their own right but are very often conceived as rivals in the technological era. There have been many reasons that led to this outlook ranging from the diverse user base to the choice of the programming language used by them. What this leads to is an introduction and premises to the data science language camps the R-camp and the Python camp, each camp supported by a multitude of users who are in a constant duel trying to prove which language is the best. Both R and Python are supreme languages in their own regard, of late, Python has found greater applicability over the years and why is it so?

We introduce agents that use object-oriented reasoning to consider alternate states of the world in order to more quickly find solutions to problems. Specifically, a hierarchical controller directs a low-level agent to behave as if objects in the scene were added, deleted, or modified. The actions taken by the controller are defined over a graph-based representation of the scene, with actions corresponding to adding, deleting, or editing the nodes of a graph. We present preliminary results on three environments, demonstrating that our approach can achieve similar levels of reward as non-hierarchical agents, but with better data efficiency.

Given that, I've written this series to help give guidance to those wanting to start learning more about data science, machine learning, and/or artificial intelligence, and need help choosing a language. This series is also intended for practitioners that wonder which language and packages work best in certain scenarios. Although we'll cover most considerations in this series, including fundamental computer science concepts, the short answer is that you should learn Python and R, and should definitely learn SQL too. If you're really feeling ambitious, give Java, C, and Scala a shot as well. While not specific to data science, the TIOBE Index is a great, up-to-date way to assess the popularity and relevance of different programming languages. When I say'learn', I mean learn fundamental programming concepts and control flow structures, which are applicable to any computer programming language.

This book contains proven steps and strategies on learning Python Programming quickly and easily. Python is a powerful and flexible programming language. It uses concise and easy-to-learn syntax which enables programmers to write more codes and develop more complex programs in a much shorter time. Python: The Ultimate Beginner's Guide provides all essential programming concepts and information you need to start developing your own Python program. The book provides a comprehensive walk-through of Python programming in a clear, straightforward manner that beginners will appreciate. Important concepts are introduced through a step-by-step discussion and reinforced by relevant examples and illustrations. You can use this book as a guide to help you explore, harness, and gain appreciation of the capabilities and features of Python. Thanks again for purchasing this book, I hope you enjoy it!

Become a true data scientist & machine learning expert with full industry knowledge Apply different predictive models and machine learning algorithms into use cases in different business areas Present analytical results to various users Master Text Mining & Natural Language Processing (NLP) using Python & Spark for sentimental analysis Work on Python with SQL on SQLite, Redshift, SAS, MongoDB, Spark and other data sources Become industry expert in banking, marketing, credit risk and product-user recommender system Collect and analyze Big Data in different systems Use AWS and Azure for Cloud Computing Master fundamental Python programming Apply generic Object Oriented Programming (OOP) Conduct real world capstone projects to build up career path Master useful data engineering knowledge and skills Convert homework and practices into your own knowledge and skills Use all famous graphics tools such as matplotlib, plotly, seaborn and ggplot into data visualization Any one should be able to use computer including being able to install software Desire to learn Python, Data Science and Cloud Computing Prior exposure to programming languages will be helpful Basic knowledge and skills of math In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing! This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python. Data science career is not just a traditional IT or pure technical game – this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data.

Machine reading comprehension(MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets. In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task). Our solution is a relation module that is adaptable to any MRC model. The relation module consists of both semantic extraction and relational information. We first extract high level semantics as objects from both question and context with multi-head self-attentive pooling. These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence. These scores are used to determine whether a question is non-answerable. We test the relation module on the SQuAD 2.0 dataset using both BiDAF and BERT models as baseline readers. We obtain 1.8% gain of F1 on top of the BiDAF reader, and 1.0% on top of the BERT base model. These results show the effectiveness of our relation module on MRC

Python GUI Programming Cookbook: Develop functional and responsive user interfaces with tkinter and PyQt5, 3rd Edition Books by Burkhard Meier Over 90 recipes to help you develop widgets, forms, layouts, charts, and much more using the latest features of Python 3 Key Features Use object-oriented programming to develop impressive GUIs in Python Create interesting charts to visually represent data using Matplotlib Develop GUIs with the latest versions of tkinter, PyQt5, and wxPython frameworks Book Description Python is a multi-domain, interpreted programming language that is easy to learn and implement. With its wide support for frameworks to develop GUIs, you can build interactive and beautiful GUI-based applications easily using Python. This third edition of Python GUI Programming Cookbook follows a task-based approach to help you create effective GUIs with the smallest amount of code. Every recipe in this book builds upon the last to create an entire, real-life GUI application. These recipes also help you solve problems that you might encounter while developing GUIs.

This post is not meant for seasoned software engineers. This is geared towards data scientists and machine learning (ML) practitioners, who like me, do not come from a software engineering background. We use Python a lot for our work. It is on the way to becoming the fastest growing major language for modern data-driven analytics and artificial intelligence (AI) apps. However, it is also used for simple scripting purpose, to automate stuff, to test a hypothesis, create interactive plots for brainstorming, to control lab instruments, etc.

JAVA PROGRAMMING, Sixth Edition provides the beginning programmer with a guide to developing applications using the Java programming language. Java is popular among professional programmers because it can be used to build visually interesting GUI and Web-based applications. Java also provides an excellent environment for the beginning programmer – students can quickly build useful programs while learning the basics of structured and object-oriented programming techniques.

Perkovic's Introduction to Programming Using Python is more than just an introduction to programming. It is an inclusive introduction to Computer Science that takes the pedagogical approach of "the right tool for the job at the right moment," and focuses on application development. The book's approach is hands-on and problem-oriented, with practice problems and solutions appearing throughout the text. The text is imperative-first, but does not shy away from discussing objects early where appropriate. Discussions of user-defined classes and Object-Oriented Programming appear later in the text, when students have more background and the concepts can be motivated.