Category: Training & Certifications

Feed: Hadoop – Cloudera Engineering Blog. Author: Justin Kestelyn. I’ve always held a strong bias that education is most effective when the student learns by doing. As a developer of technical curricula, my goal is to have training participants engage with real and relevant problems as much as possible through hands-on exercises. The high rate at which Apache Hadoop is changing, both as a technology and as an ecosystem, makes developing Cloudera training courses not only demanding but also seriously fun and rewarding. I recently undertook the challenge of upgrading the Cloudera Administrator Training for Apache Hadoop. I more than ... Read More

Feed: Analytics Courses Discussions - AnalyticBridge Author: Vincent Granville Originally posted in OCR. I've never heard about many of these programs (see top 10 below), and I have questions regarding the methodology used for rankings: Affordability (1/3):the overall cost of the program and/or credit hour. Flexibility (1/3): the number of credit hours and/or the length of time it takes to obtain the degree Requirements (1/3): the variety of admissions requirements and/or prerequisite classes needed to qualify for the program Source for picture: where data scientists live Other organizations such as IEEE rank programs according to ROI, which is in my opinion a better metric, ... Read More

Feed: Analytics Courses Discussions - AnalyticBridge Author: Vincent Granville Submitted by Sudhanshu Ahuja. Image source: Link Which top Masters Courses should you consider for a great career in data-science? A frequently cited study by McKinsey predicts that by 2018, the United States could face a shortage of 140,000 to 190,000 "people with deep analytic skills" as well as 1.5 million "managers and analysts with the know-how to use the analysis of big data to make effective decisions." The field is so hot right now that Roy Lowrance, the managing director of New York University's new Center for Data Science program says "Anything ... Read More

Feed: Analytics Courses Discussions - AnalyticBridge Author: Vincent Granville Course material for 2014-1015.  Lectures Lecture 1: Introduction slides Video Lecture 2: Linear prediction slides Video Lecture 3: Maximum likelihood slides.pdf Video Lectures 4 & 5: Regularizers, basis functions and cross-validation slides.pdf Video 1 Video 2 Lecture 6: Optimisation slides.pdf Video Lecture 7: Logistic regression slides.pdf Video Lecture 8: Back-propagation and layer-wise design of neural nets slides.pdf Video Lecture 9: Neural networks and deep learning with Torch slides.pdf Video Lecture 10: Convolutional neural networks slides.pdf Video Lecture 11: Max-margin learning and siamese networks slides.pdf Video Lecture 12: Recurrent neural networks and LSTMs slides.pdf Video Lecture 13: Hand-writing with recurrent neural networks (Guest speaker: Alex Graves from Google Deepmind) Lecture 14: Variational autoencoders and image generation (Guest speaker: Karol Gregor from Google Deepmind) Lecture 15: Reinforcement learning with ... Read More

Feed: Analytics Courses Discussions - AnalyticBridge Author: Vincent Granville Sponsored Announcement by SlideRule. Classes start January 5, 2015. Topics covered Probability and statistics: Probability & combinatorics, random variables & distributions, descriptive statistics, dependent & independent events, regression and inferential statistics. (16 hours) R Basics: Installing R and RStudio, shortcuts, common commands, syntax quirks and basic visualization with the ggplot2 package. (4 hours) Exploratory Data Analysis: EDA vs classical & Bayesian approaches, histograms, frequency polygons, box-plots, quartiles, scatter plots, heat maps etc. (20 hours) Data Wrangling: The split-apply-combine paradigm, R based tools and packages for data wrangling - reshape2, plyr, dplyr etc. (4 hours) ... Read More

Feed: Analytics Courses Discussions - AnalyticBridge Author: Vincent Granville Your next employer wants you to have a Master's degree or graduate certificate. Now's the time. Northeastern is the place. Perhaps nowhere is the use of experiential, practical, hands-on learning more important than in the fields known simply as "STEM" ― science, technology, engineering and mathematics. Northeastern's best-in-class model for experiential learning is what sets us apart and earned us a Top 50 national ranking. We're also #1 in career services (2014 Princeton Review). Our world-class graduate programs build upon your undergraduate education and work experience in a practical and meaningful way that will accelerate ... Read More

Feed: Analytics Courses Discussions - AnalyticBridge Author: Vincent Granville By University of Illinois at Urbana-Champaign. Instructors: Abel Bliss, Professor, Department of Computer Science; John C. Hart, Professor, Department of Computer Science; ChengXiang Zhai, Professor, Department of Computer Science.Purpose:Understand the basic concepts and principles in data mining and visualization. Learn commonly used algorithms for mining both structured and unstructured (text) data. Understand how to handle a large amount of text data with search engines. Gain experience applying some of the algorithms to solve real world data mining problems. Module 1 - Pattern Discovery in Data Mining Introduction to data mining Concepts and ... Read More

Whether or not you’ve dabbled with queries or databases, earning a MCSA certification will attract the eyes and wallets of company execs across the states and beyond. SQL is a go-to software for implementing data warehouses, as well as efficiently managing massive amounts of data. In this bundle, currently discounted 91%, you'll access three courses: Microsoft 70-461: Querying Microsoft SQL Server 2012 Microsoft 70-462: Administering Microsoft SQL Server 2012 Databases Microsoft 70-463: Implementing A Data Warehouse With Microsoft SQL Server 2012 This $438 course bundle is available, for a limited time, for just $35.99. Learn more about this bundle, the ... Read More

I’m attending Rachel Schutt’s Columbia University Data Science course on Wednesdays this semester and I’m planning to blog the class. Here’s what happened yesterday at the first meeting. Syllabus Rachel started by going through the syllabus. Here were her main points: The prerequisites for this class are: linear algebra, basic statistics, and some programming. The goals of this class are: to learn what data scientists do. and to learn to do some of those things. Rachel will teach for a couple weeks, then we will have guest lectures. The profiles of those speakers vary considerably, as do their backgrounds. Yet they are all ... Read More