Artificial intelligence is slowly but steadily embedding itself into the core processes of multiple industries and changing the industrial landscape in so many ways -- be it deep learning-powered autonomous cars or bot-powered medical diagnostic processes. The industrial and energy sectors are not immune to the disruption that comes with embracing AI. As upstream and downstream companies gear up for AI, there is one important lesson I want to share that might seem counterintuitive. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications.

This is the SECOND in a series of posts on applying Tim Ferriss' accelerated learning framework to Data Science. My goal is to become a world-class (top 5%) Data Scientist in 6 months, while open-sourcing everything I find and learn along the way. And if you stick around until the end, you're in for a special treat. A simple Google search of "how to learn Data Science" returns thousands of learning plans, degree programs, tutorials, and bootcamps. It's never been more difficult for a beginner to find signal in the noise. Everyone seems to have a different opinion, and the only common approach appears to be dumping a long list of courses to take and books to read, all the while providing little to no context into how these concepts fit into the bigger picture.

Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning.

Although compelling assessments have been examined in recent years, more studies are required to yield a better understanding of the several methods where assessment techniques significantly affect student learning process. Most of the educational research in this area does not consider demographics data, differing methodologies, and notable sample size. To address these drawbacks, the objective of our study is to analyse student learning outcomes of multiple assessment formats for a web-facilitated in-class section with an asynchronous online class of a core data communications course in the Undergraduate IT program of the Information Sciences and Technology (IST) Department at George Mason University (GMU). In this study, students were evaluated based on course assessments such as home and lab assignments, skill-based assessments, and traditional midterm and final exams across all four sections of the course. All sections have equivalent content, assessments, and teaching methodologies. Student demographics such as exam type and location preferences are considered in our study to determine whether they have any impact on their learning approach. Large amount of data from the learning management system (LMS), Blackboard (BB) Learn, had to be examined to compare the results of several assessment outcomes for all students within their respective section and amongst students of other sections. To investigate the effect of dissimilar assessment formats on student performance, we had to correlate individual question formats with the overall course grade. The results show that collective assessment formats allow students to be effective in demonstrating their knowledge.

This hands-on training is intended to familiarize researchers and data scientists with the services Azure offers to aid them in their research, especially with regard to high-performance computing, big-data analysis, and analyzing data streaming from Internet-of-Things (IoT) devices.

This repo contains an incremental sequence of notebooks designed to teach deep learning, Apache MXNet (incubating), and the gluon interface. Our goal is to leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place. If we're successful, the result will be a resource that could be simultaneously a book, course material, a prop for live tutorials, and a resource for plagiarising (with our blessing) useful code. To our knowledge there's no source out there that teaches either (1) the full breadth of concepts in modern deep learning or (2) interleaves an engaging textbook with runnable code. We'll find out by the end of this venture whether or not that void exists for a good reason.

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people manage and analyze vast amount of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans for humans. First, since text data are generated by people, they are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. Second, since text is written for consumption by humans, humans play a critical role in any text data application system, and a text management and analysis system must involve them in the loop of text analysis.

In this tutorial, we will demonstrate how to create scalable, end-to-end data analysis processes in R on single machines as well as in-database in SQL Server and on Hadoop clusters running Spark. We will provide hands-on exercises as well as code in a public GitHub repository for attendees to adopt in their data science practice. In particular, the attendees will see how to build, persist, and consume machine learning models using distributed machine learning functions in R. R is one of the most used languages in the data science, statistical and machine learning (ML) community. Although open-source R (CRAN library) now has in excess of 10,000 packages and functions for statics and ML, when it comes to scalable analysis using R, or deployment of trained models into production, many data scientists are blocked or hindered by (a) its limitations of available functions to handle large datasets efficiently, and (b) knowledge about the appropriate computing environments to scale R scripts from desktop analysis to elastic and distributed cloud services. In this tutorial, we will discuss how to create end-to-end data science solutions that utilize distributed compute resources.

For students running the latest version of Mac OS X (10.8), the easiest way to obtain all the packages is to install the Scipy Superpack which works with Python 2.7.2 that ships with OS X. Otherwise, another easy way to install all the necessary packages is to use Continuum Analytics' Anaconda.

To find out more information about our BSIS and MSDA programs visit: http://sps.cuny.edu