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.

Learn volatility trading analysis through a practical course with R statistical software using CBOE, S&P 500, VelocityShares volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing. It explores main concepts from advanced to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced sophisticated investor. Learning volatility trading analysis is indispensable for finance careers in areas such as derivatives research, derivatives development, and derivatives trading mainly within investment banks and hedge funds. It is also essential for academic careers in derivatives finance. And it is necessary for experienced sophisticated investors' volatility trading strategies research.

One clever way to pierce the PR swamp of my inbox is with an optimistic twist. If that optimism is backed by data? Topcoder PR recently won my inbox with this email subject header: "Coders aren't scared of losing work to AI – Topcoder community explains why." It helps that I've known about Topcoder for years. With 1,200,000 developers, all signed up to collaborate on crowdsourced projects and compete in online challenges, Topcoder know a thing or two about what makes developers tick – and how coders upskill against requirements.

With "Introduction to R", you will gain a solid grounding of the fundamentals of the R language! This course has about 90 videos and 140 exercise questions, over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session. From there, you will review topics in increasing order of difficulty, starting with Data/Object Types and Operations, Importing into R, and Loops and Conditions.

Python is a popular programming language, widely used in many scenarios and easy to use to use. 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. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. To start off with your learning journey, you can learn some of the fundamental tools of the trade and apply them to real data problems.

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I haven't taken this specific Udacity course on deep learning. But, I have completed their Nanodegree for the self-driving cars that covered a decent amount of deep learning material. I won't be surprised if they borrow some of the contents from there as well. Udacity offers high quality lectures and related projects. Their content is usually ver well organized and they are constantly improving.

About this course: In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. Prerequisites and Pre-work • Completed Google Cloud Platform Fundamentals or have equivalent experience • Working knowledge of Node.js • Basic proficiency with command-line tools and Linux operating system environments • Previous course(s) in the specialization

Data science and within that, machine learning has seen an explosive uptick in both interest and application in recent years. This has meant that the job market has expanded quickly. With no real sign of slowing demand and a limit to the number of experienced individuals with computer science degrees, the market has been opened up to a diverse set of prospective candidates. Many individuals are moving into the industry from backgrounds such as the sciences, engineering or from engagement with massive open online courses (MOOCs). In fact, Andrew Ng himself recently placed emphasis on taking on interns who had completed his Deep Learning MOOC on Coursea.