R is on the rise and showing itself as a powerful option in many software development domains. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis, creating high-level graphics, and machine learning. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way. The course is structured in simple lessons so that the learning process feels like a step-by-step guide to plotting. We start by importing data in R from popular formats such as CSV and Excel tables.

As advanced machines and computers become more and more proficient at picking investments, diagnosing disease symptoms, and conversing in natural English, it is difficult not to wonder what the limits to their capabilities are. This is why many observers believe in technology's potential to disrupt our economy--and our civilization--is unprecedented. Over the past few years, my conversations with students entering the workforce and the business leaders who hire them have revealed something important: to stay relevant in this new economic reality, higher education needs a dramatic realignment. Instead of educating college students for jobs that are about to disappear under the rising tide of technology, twenty-first-century universities should liberate them from outdated career models and give them ownership of their own futures. They should equip them with the literacies and skills they need to thrive in this new economy defined by technology, as well as continue providing them with access to the learning they need to face the challenges of life in a diverse, global environment. Higher education needs a new model and a new orientation away from its dual focus on undergraduate and graduate students.

Every year I give my forecast for the top 10 workplace trends for the upcoming year. The purpose is to help prepare organizations for the future by collecting, assessing and reporting the trends that will most impact them. You can read my predictions from 2013, 2014, 2015, 2016 and 2017. These trends are based on hundreds of conversations with executives and workers, a series of national and global online surveys and secondary research from more than 450 different research sources, including colleges, consulting firms, non-profits, the government and trade associations. All economic indicators show a positive view of the U.S. economy in 2018.

Machine Learning or ML is fast becoming the buzzword of our time, but why are so many developers falling short when it comes to getting their heads round this essential skill? Here's why developers must tackle ML to get ahead and what's standing in their way. Let's face it, when it comes to AI, the future has very much arrived. This application of ML is everywhere right now, whether you're looking at self-driving cars or self-tuning database systems – it's impacting almost every industry on the market. Acquiring ML skills is a no-brainer for the ambitious developer, and the number of self-led courses and MOOCs doubled last year.

We will begin this course with a tutorial on the standard camera models used in computer vision. These models allow us to understand, in a geometric fashion, how light from a scene enters a camera and projects onto a 2D image. By defining these models mathematically, we will be able understand exactly how a point in 3D corresponds to a point in the image and how an image will change as we move a camera in a 3D environment. In the later modules, we will be able to use this information to perform complex perception tasks such as reconstructing 3D scenes from video.

We just unlocked a Free Preview of our Machine Learning Engineer Nanodegree Program! Discover amazing new content, and explore your future in Machine Learning, today! The Machine Learning Engineer Nanodegree program has been one of Udacity's benchmark programs for over 2 years. Thousands of students have graduated the program, and many have gone on to great careers at companies like Google, Amazon, and more. As technology evolves, so does our curriculum, and we think much of the program's success can be attributed to keeping the content up-to-the-minute current.

These days, I am sure 90% of LinkedIn traffic contains one of these terms: DS, ML or DL -- acronyms for Data Science, Machine Learning or Deep Learning. Beware of the cliche though: "80% of all the statistics are made on the spot". If you blinked on these acronyms perhaps you need to google a bit and then continue reading the rest of this post. This post has 2 goals. First, it attempts to put all the fellow Data Science learners at ease.

This list can provide inspiration for which courses you might consider for yourself in the coming year, but does it also say anything about the evolution of MOOCs? Are there trends in which types of courses are becoming more popular? And what are the reasons behind those movements? I reached out to a Coursera spokesperson to find out. As you probably already noticed, tech is booming. The subject is "continuing to draw significant interest: artificial intelligence, blockchain, and anything at the intersection of business and data analytics continue to see growth," Coursera confirmed.

Data Science is an interdisciplinary field that employs techniques to extract knowledge from data. As one of the fast growing fields in technology, the interest for Data Science is booming, and the demand for specialized talent is on the rise. This course takes a practical approach to Data Science, presenting solutions for common and not-so-common problems in the form of recipes. This video will begin from exploring your data using the different methods like data acquisition, data cleaning, data mining, machine learning, and data visualization, applied to a variety of different data types like structured data or free-form text. It will show how to deal with text using different methods like text normalization and calculating word frequencies.

This video course will demonstrate the steps for analyzing unstructured data with the R/R Studio software. The approaches will be illustrated using practical applications for business, healthcare, and retail data, among others. At the end the video course you will have mastered obtaining and visualizing data with R. You will also be confident with data cleaning, preparation, and sentiment analysis with R. Dr. Bharatendra Rai is a professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit.