Exploratory Data Analysis Lesson 1: What is EDA? (1 hour) We'll start by learn about what exploratory data analysis (EDA) is and why it is important. Lesson 2: R Basics (3 hours) EDA, which comes before formal hypothesis testing and modeling, makes use of visual methods to analyze and summarize data sets. Lesson 3: Explore One Variable (4 hours) We perform EDA to understand the distribution of a variable and to check for anomalies and outliers. Problem Set 3 (2 hours) Lesson 4: Explore Two Variables (4 hours) EDA allows us to identify the most important variables and relationships within a data set before building predictive models. Problem Set 4 (2 hours) Lesson 5: Explore Many Variables (4 hours) Data sets can be complex. Problem Set 5 (2 hours) Lesson 6: Diamonds and Price Predictions (2 hours) Investigate the diamonds data set alongside Facebook Data Scientist, Solomon Messing. Final Project (10+ hours) You've explored simulated Facebook user data and the diamonds data set.
Home - Stamford High School Metropolis–Hastings algorithm In statistics and in statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This sequence can be used to approximate the distribution (i.e., to generate a histogram), or to compute an integral (such as an expected value). Metropolis–Hastings and other MCMC algorithms are generally used for sampling from multi-dimensional distributions, especially when the number of dimensions is high. For single-dimensional distributions, other methods are usually available (e.g. adaptive rejection sampling) that can directly return independent samples from the distribution, and are free from the problem of auto-correlated samples that is inherent in MCMC methods. History The algorithm was named after Nicholas Metropolis, who was an author along with Arianna W. Intuition Metropolis algorithm . ), we will always accept the move. . where to
Intro to Hadoop and MapReduce Intro to Hadoop and MapReduce How to Process Big Data Intermediate Approx. 1 month Assumes 6hr/wk (work at your own pace) Built by Join 85,474 Students Enroll in Course $199 /month after 14-day trial Best for learners serious about course completion & career advancement You get Instructor videos See All Instructor videos Learn by doing exercises Projects with reviews Stuck? Verified Certificate Instructor videos Watch concise videos that teach you not just how, but why. Learn by doing exercises Learn by doing exercises and quizzes in between instructional videos. Projects with feedback Showcase your skills with a project: . Stuck? Get unstuck with support from Udacity Coaches in the forums and scheduled, live sessions. Verified Certificate Stand out to employers with a certificate that counts. Access Course Materials Free Learn by doing exercises and view project instructions Projects with reviews Stuck? Verified Certificate These features are available when you enroll View Trailer Course Summary Projects Syllabus
Joel Klein, Sal Khan and Sebastian Thrun on Inventing the Future of Education, at Disrupt SF Three trailblazing figures in educational technology are showcasing the future of learning at our upcoming annual conference, Disrupt San Francisco. Former New York education Chancellor, Joel Klein, will get into more of the details about the recently announced Amplify project, News Corp’s ambitious venture to create tailored, digital learning for the American education system. Bill Gates’ “favorite teacher”, Sal Khan, who founded the Youtube-based Khan Academy, will speak about his pioneering work in the “flipped classroom” and launch a new feature to his site. And Google fellow and CEO of Udacity, Sebastian Thrun, will discuss how he opened the walled garden of American higher education free of charge to students around the world. These education leaders will join an all-star lineup at Disrupt SF Sept 8-12, including Yahoo CEO Marissa Mayer, Marc Benioff, Ron Conway, Kevin Rose, Matt Cohler, TechCrunch founder Michael Arrington, Vinod Khosla and many others. In January 2011, Joel I.
R-bloggers | R news & tutorials from the web Data Wrangling with MongoDB Class Summary In this course, we will explore how to wrangle data from diverse sources and shape it to enable data-driven applications. Some data scientists spend the bulk of their time doing this! Students will learn how to gather and extract data from widely used data formats. This is a great course for those interested in entry-level data science positions as well as current business/data analysts looking to add big data to their repertoire, and managers working with data professionals or looking to leverage big data. What Will I Learn? At the end of the class, students should be able to: Programmatically extract data stored in common formats such as csv, Microsoft Excel, JSON, XML and scrape web sites to parse data from HTML.Audit data for quality (validity, accuracy, completeness, consistency, and uniformity) and critically assess options for cleaning data in different contexts. What Should I Know? The ideal student should have the following skills: Syllabus Lesson 3: Data Quality
Digital Health Space Normal vs. Binomial: What are the hallmarks and differences? vs. Binomial: What are the hallmarks and differences? (z) DISTRIBUTION The normal (z) distribution is a continuous distribution that arises in many natural processes. The bell-shaped normal curve has probabilities that are found as the area between any two z values. Not all natural processes produce normal distributions. Here are some example problems. The central limit theorem (CLT) says that the sampling distribution of xbar will approach a normal distribution, namely N(m, s/Ön), if the sample size is large. A binomial distribution is very different from a normal distribution, and yet if the sample size is large enough, the shapes will be quite similar. The key difference is that a binomial distribution is discrete, not continuous. The requirements for a binomial distribution are Consider X = number of sixes when a fair die is rolled 31 times. Is X a binomial r.v.? 1) X counts the number of successes (sixes) in 31 trials. Since X is binomial, we say X follows the B(31, 1/6) distribution.
CAPM Certification Ready to apply? Register and log in to get started. PMI’s Certified Associate in Project Management (CAPM)® is a valuable entry-level certification for project practitioners. Designed for those with little or no project experience, the CAPM® demonstrates your understanding of the fundamental knowledge, terminology and processes of effective project management. Whether you are new to project management, or already serving as a subject matter expert on project teams, the CAPM can get your career on the right path or take it to the next level. Who should apply? If you’re a less experienced project practitioner looking to demonstrate your commitment to project management, improve your ability to manage larger projects and earn additional responsibility, and stand out to potential employers, the CAPM certification is right for you. CAPM Eligibility Overview To apply for the CAPM, you need to have: This is an overview of the requirements. How to apply and prepare for the exam Maintain Your CAPM
Salk Institute Study on regulation of p53 Scientists at the Salk Institute for Biological Studies have found clues to the functioning of an important damage response protein in cells. The protein, p53, can cause cells to stop dividing or even to commit suicide when they show signs of DNA damage, and it is responsible for much of the tissue destruction that follows exposure to ionizing radiation or DNA-damaging drugs such as the ones commonly used for cancer therapy. The new finding shows that a short segment on p53 is needed to fine-tune the protein's activity in blood-forming stem cells and their progeny after they incur DNA damage. Click here to read full release from Salk Institute . Among the research institutions NCI funds across the United States, it currently designates 66 as Cancer Centers.
Digital Analytics Fundamentals - - Unit 2 - Getting started with digital analytics Analytics Academy Login Unit 2 - Getting started with digital analytics The importance of digital analytics Text Version Lesson objectives Understand what "digital measurement" meansRecognize why digital analytics tools like Google Analytics are important to implement for your business Additional resources Activity 2.1 - The importance of digital analytics Complete the definition of "digital analytics" by choosing the best option to fill in the blanks for the statement below. Previous Page Next Page