Among thousands, 10 programming languages stand out for their job marketability and wide use. Anyone can learn it from his/her initial stage in the field of software development. A free alternative to pricey statistical software such as Matlab or SAS, over the last few years R has become the golden child of data science. Why You Should Learn Python Python is one of the top programming languages requested by companies in 2017 / 2018.

Get Robotics Certification taking the Online Robotics Degree Programs. However, you can get an online Robotics Degree from a lot of places like Coursera, Udemy, EDx, Futurelearn and so on. Description: One of the robotics tutorial for beginners to advanced where over 1000 students enrolled! So, learn Robotics online to open career opportunities and have fun to learn electronics focused on building robots automation! Description: An autonomous light-seeking an obstacle avoiding robot for Arduino Makers that want to learn the hard way.

HOy traemos a este espacio el último número el Vol 13, No 10 (2018) del International Journal of Emerging Technologies in Learning (iJET) This interdisciplinary journal aims to focus on the exchange of relevant trends and research results as well as the presentation of practical experiences gained while developing and testing elements of technology enhanced learning. So it aims to bridge the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Readers don't have to pay any fee. Vol 13, No 10 (2018) Table of Contents Papers Innovative English Classroom Teaching Based on Online Computer Technology in Rural Middle and Primary Schools Application of Brain Neural Network in Personalized English Education System Songlin Yang, Min Zhang A Generic Tool for Generating and Assessing Problems Automatically using Spreadsheets Maria Assumpció Rafart Serra, Andrea Bikfalvi, Josep Soler Masó, Jordi Poch Garcia A High Security Distance Education Platform Infrastructure Based on Private Cloud Jingtai Ran, Kepeng Hou, Kegang Li, Niya Dai Student Performance Prediction Model Based on Discriminative Feature Selection Haixia Lu, Jinsong Yuan An Eight-Layer Model for Mathematical Cognition Marios A. Pappas, Athanasios S. Drigas, Fotini Polychroni Design and Implementation of University Art Education Management System Based on JAVA Technology The Design and Application of Flip Classroom Teaching Based on Computer Technology Jia Li, Xiaoxia Zhang, Zijun Hu Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining Intelligent System for College English Listening and Writing Training Development of an Accounting Skills Simulation Practice System Based on the B/S Architecture Jianmei Liu, Rong Fu Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm Sujuan Jia, Yajing Pang Blended Learning Innovation Model among College Students Based on Internet Score Prediction Model of MOOCs Learners Based on Neural Network Yuan Zhang, Wenbo Jiang Design and Implementation of the Online Computer-Assisted Instruction System Based on Object-Oriented Analysis Technology Wenbo Zhou, Lei Shi, Jian Chen Matlab-Realized Visual A* Path Planning Teaching Platform Communication Jigsaw: A Teaching Method that Promotes Scholarly Communication A Tablet-Computer-Based Tool to Facilitate Accurate Self-Assessments in Third- and Fourth-Graders Denise Villanyi, Romain Martin, Philipp Sonnleitner, Christina Siry, Antoine Fischbach Short Papers Application of Blockchain Technology in Online Education Han Sun, Xiaoyue Wang, Xinge Wang The Grading Multiple Choice Tests System via Mobile Phone using Image Processing Technique Worawut Yimyam, Mahasak Ketcham Offline Support Model for Low Bandwidth Users to Survive in MOOCs International Journal of Emerging Technologies in Learning.

The effectiveness of learning in massive open online courses (MOOCs) can be significantly enhanced by introducing personalized intervention schemes which rely on building predictive models of student learning behaviors such as some engagement or performance indicators. A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand. In this paper, we make the first attempt to solve the feature learning problem by taking the unsupervised learning approach to learn a compact representation of the raw features with a large degree of redundancy. Specifically, in order to capture the underlying learning patterns in the content domain and the temporal nature of the clickstream data, we train a modified auto-encoder (AE) combined with the long short-term memory (LSTM) network to obtain a fixed-length embedding for each input sequence. When compared with the original features, the new features that correspond to the embedding obtained by the modified LSTM-AE are not only more parsimonious but also more discriminative for our prediction task. Using simple supervised learning models, the learned features can improve the prediction accuracy by up to 17% compared with the supervised neural networks and reduce overfitting to the dominant low-performing group of students, specifically in the task of predicting students' performance. Our approach is generic in the sense that it is not restricted to a specific supervised learning model nor a specific prediction task for MOOC learning analytics.

In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoing courses, when the student outcomes are yet unknown, must rely on models trained from the historical data of previously offered courses. It is possible to transfer models, but they often have poor prediction performance. One reason is features that inadequately represent predictive attributes common to both courses. We present an automated transductive transfer learning approach that addresses this issue. It relies on problem-agnostic, temporal organization of the MOOC clickstream data, where, for each student, for multiple courses, a set of specific MOOC event types is expressed for each time unit. It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term. With these methods, we investigate the transferability of dropout prediction across similar and dissimilar MOOCs and compare with known methods. Results show improved model transferability and suggest that the methods are capable of automatically learning a feature representation that expresses common predictive characteristics of MOOCs.

We've all heard the dire predictions about robots coming to steal our jobs. As technologies such as machine learning, AI and automation advance by the day, workplaces everywhere are being transformed; naturally, some people fear that they will become redundant -- depending on their job, some may be right. In this age of AI and ML, ambivalence is ripe; but something ironic has emerged: when it comes to advancing these cutting-edge technologies, the lack of human skills and knowledge is slowing innovation down. In a new survey by Cloudera, the software firm, exploring the benefits and roadblocks of ML adoption across Europe, 51% of business leaders said that the skills shortage was holding them back from implementation. According to Cloudera, companies are eager to use ML -- it's second only to analytics as the key investment priority for businesses; ahead of other disciplines like IoT, artificial intelligence and data science.

There is no shortage of articles attempting to lay out a step-by-step process of how to become a data scientist. Are you a recent graduate? Do this… Are you changing careers? Do that… And make sure you're focusing on the top skills: coding, statistics, machine learning, storytelling, databases, big data… Need resources? Check out Andrew Ng's Coursera ML course, …". Although these are important things to consider once you have made up your mind to pursue a career in data science, I hope to answer the question that should come before all of this. It's the question that should be on every aspiring data scientist's mind: "should I become a data scientist?" This question addresses the why before you try to answer the how. What is it about the field that draws you in and will keep you in it and excited for years to come? In order to answer this question, it's important to understand how we got here and where we are headed. Because by having a full picture of the data science landscape, you can determine whether data science makes sense for you. Before the convergence of computer science, data technology, visualization, mathematics, and statistics into what we call data science today, these fields existed in siloes -- independently laying the groundwork for the tools and products we are now able to develop, things like: Oculus, Google Home, Amazon Alexa, self-driving cars, recommendation engines, etc. The foundational ideas have been around for decades... early scientists dating back to the pre-1800s, coming from wide range of backgrounds, worked on developing our first computers, calculus, probability theory, and algorithms like: CNNs, reinforcement learning, least squares regression. With the explosion in data and computational power, we are able to resurrect these decade old ideas and apply them to real-world problems. In 2009 and 2012, articles were published by McKinsey and the Harvard Business Review, hyping up the role of the data scientist, showing how they were revolutionizing the way businesses are operating and how they would be critical to future business success. They not only saw the advantage of a data-driven approach, but also the importance of utilizing predictive analytics into the future in order to remain competitive and relevant. Around the same time in 2011, Andrew Ng came out with a free online course on machine learning, and the curse of AI FOMO (fear of missing out) kicked in. Companies began the search for highly skilled individuals to help them collect, store, visualize and make sense of all their data. "You want the title and the high pay?

There are no hard and fast rules for learning such a complex topic. The beauty of online learning is that you get to choose what you lack and what excite you. For this part 1 of the series, I will review the maths and python fundamental courses that I had taken. Please note that these are my personal opinion which might or might not resonate with you. I like to give special mention to Data Science A-Z by Kirill Eremenko and the SuperDataScience Team.

The educational system is broken, and unfair. For decades, if not centuries, learning was limited by geography and having the means to continue with higher education. Online learning and massive open online courses (MOOCs) promised to address the inequities in education while extending its reach across all geographies. However, the online model simply paved over the older methods with technology, and perhaps even making things worse -- pushing course material to students, with no effective way to track how much they're learning, or even if they're paying attention. Now, artificial intelligence (AI) may have an answer for that, bringing learning and feedback in a very personal way to students.

Ricardo Diaz is a machine learning engineer. He works for a great company in Peru, and he's a graduate of no less than four Nanodegree programs! But just two years ago, it was a different story. He was still in Venezuela, struggling to learn new skills. He was short of money, and his prospects for making a full-time salary weren't great.