Of course it is, as mostly that's the actual science part in data science. But it doesn't mean that you couldn't learn it by yourself if you are smart and determined enough. In this article, I am going to list 6 books that I recommend to start with to learn statistics. The first three are lighter reads. These books are really good for setting your mind to think more numerical, mathematical and statistical.

In 1978 a young woman named Maria Klawe arrived at the University of Toronto to pursue a doctorate in computer science. She had never used a computer--much less written a line of code--but she had a PhD in math and a drive to succeed in a male-dominated field. She was so good that, nine months later, the university asked her to be a professor. Today, however, computer science is one of the few STEM fields in which the number of women has been steadily decreasing since the '80s. In the tech industry, women hold only around one-fifth of technical roles.

PREDICTIVE DATA: Degree Analytics, a startup that aims to use data and AI to increase college students' graduation rates, has raised $1 million in a "Seed B" round. The Austin-based company's investors include Rick Dalzell, formerly CIO at Amazon; David Palumbo, managing director at SoftMatch; and Larry Benz, founder of physical therapy group Confluent Health. Degree Analytics gathers student information such as attendance, library time or LMS login activity, and uses that data to make predictions on students' future behaviors. It will flag school counselors or instructors if they should intervene with a student. The company claims its platform is currently being used at three institutions, including Eastern University, and that the latest funding round will go towards further developing its platform and expanding both its "domestic and international presence."

Still founder-led by Chairman and Chief Executive Officer Richard Fairbank, Capital One is on a mission to help our customers succeed by bringing ingenuity, simplicity, and humanity to banking. We measure our efforts by the success our customers enjoy and the advocacy they exhibit. We are succeeding because they are succeeding. Guided by our shared values, we thrive in an environment where collaboration and openness are valued. We believe that innovation is powered by perspective and that teamwork and respect for each other lead to superior results.

MACHINE LEARNING FOR APPS Welcome to the most comprehensive course on Core ML, one of Apples hot new features for iOS 11. The goal with Machine Learning is to mimic the human mind. It can be used to identify things like objects or images, make predictions and even analyze and identify speech. Dive in and learn the core concepts of machine learning and start building apps that can think! In this course you going to learn everything you need to know to start building more intelligent apps and your own ML Models.

ABSTRACT We use automatic speech recognition to assess spoken English learner pronunciation based on the authentic intelligibility of the learners' spoken responses determined from support vector machine (SVM) classifier or deep learning neural network model predictions of transcription correctness. Using numeric features produced by PocketSphinx alignment mode and many recognition passes searching for the substitution and deletion of each expected phoneme and insertion of unexpected phonemes in sequence, the SVM models achieve 82% agreement with the accuracy of Amazon Mechanical Turk crowdworker transcriptions, up from 75% reported by multiple independent researchers. Using such features with SVM classifier probability prediction models can help computeraided pronunciation teaching (CAPT) systems provide intelligibility remediation. Index Terms-- phoneme alignment, pronunciation assessment, computer aided language learning, binary features 1. INTRODUCTION Authentic intelligibility, the ability of listeners to correctly transcribe recorded utterances, initially used for CAPT by [1] and [2], is a better measure of pronunciation assessment for spoken language learners compared to mispronunciations identified by expert pronunciation judges or panels of experts, because such mispronunciations are associated with only 16% of intelligibility problems, according to [3], who state: We investigated... which words are likely to be misrecognized and which words are likely to be marked as pronunciation errors. Words perceived as mispronounced remain intelligible in about half of all cases.

Data Science is one of the hottest jobs of the 21 century with an average salary of over $120,000. This course is designed learners of all backgrounds including beginners with no programming experience to experienced programmers who would like to advance to become a spatial data scientist. I will teach you programming with R to visualize, explore, and analyze your spatial data. At the end of this course, you will be able to acquire skills spatial data analysis. Enroll now in this course and start your journey of becoming a spatial data scientist!

Feeling rather good about myself as I'm writing this as I've just completed the first course of Andrew Ng's latest Deep Learning specialization on Coursera. I've been meaning to learn about Deep Learning for quite awhile now but haven't been able to wrap my heads around the theory aspect of it for longest of time. Previously, my foray into deep learning has been via Udacity's Deep Learning materials, random internet articles, and the Deep Learning textbook. Bought it from Amazon a few months ago, and am still going through the pages. Still finding it tough to find the time between going through a few pages, the day job, and sorting out the kids at night.

You may be impressed with the incredible capabilities of cameras on today's smartphones. But according to imaging experts, that's nothing compared to what may be around the corner. Researchers claim cameras of the future could have the ability to see through walls with the help of lasers, potentially leading to a new generation of'spy phones'. In this article by The Conversation, Daniele Faccio, a Professor of Quantum Technologies at the University of Glasgow, and Stephen McLaughlin, Head of the School of Engineering and Physical Sciences at Heriot-Watt University, examine the technologies that could underpin a larger revolution of camera technology. You might be really pleased with the camera technology in your latest smartphone, which can recognise your face and take slow-mo video in ultra-high definition.

This course is intended for R and data science professionals aiming to master R. Intermediate and advanced users, will both find that this course will separate them from the rest of people doing analytics with R. We don't recommend this course on beginners. We start by explaining how to work with closures, environments, dates, and more advanced topics. We then move into regex expressions and parsing html data. We explain how to write R packages, and write the proper documentation that the CRAN team expects if you want to upload your code into R's libraries. After that we introduce the necessary skills for profiling your R code.