Data Science: Microsoft

<br /> <div class="course-detail-video" style="background: rgb(85, 85, 85); border: 1px solid rgb(75, 180, 251); box-sizing: border-box; color: #4e4e4e; font-family: "Open Sans", Arial, Helvetica, sans-serif; font-size: 16px; height: 212px; left: 0px; line-height: 24px; margin-bottom: 20px; max-width: 40%; min-width: 200px; position: absolute; width: 320px;" tabindex="-1"> <a class="video-link" href="https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-1#" style="box-sizing: border-box; color: #2968aa; text-decoration: none;"><img alt="Play Video: Introduction to R for Data Science" src="https://prod-edx-mktg-edit.edx.org/sites/default/files/styles/course_video_banner/public/course/image/featured-card/dat204x-course_image-378x225.jpg?itok=OLc-xcYg" style="border: 0px; box-sizing: border-box; display: block; height: auto; max-width: 100%; position: relative; z-index: 1;" /></a></div> <div class="pull-left" style="background-color: white; box-sizing: border-box; color: #4e4e4e; float: left; font-family: "Open Sans", Arial, Helvetica, sans-serif; font-size: 16px; line-height: 24px;"> <h1 class="course-intro-heading" style="box-sizing: border-box; color: #050505; font-size: 26px; line-height: 1.25em; margin: 0px 0px 0.45em; padding: 0px;"> Introduction to R for </h1> <h1 class="course-intro-heading" style="box-sizing: border-box; color: #050505; font-size: 26px; line-height: 1.25em; margin: 0px 0px 0.45em; padding: 0px;"> <br /></h1> <h1 class="course-intro-heading" style="box-sizing: border-box; color: #050505; font-size: 26px; line-height: 1.25em; margin: 0px 0px 0.45em; padding: 0px;"> <br /></h1> <h1 class="course-intro-heading" style="box-sizing: border-box; color: #050505; font-size: 26px; line-height: 1.25em; margin: 0px 0px 0.45em; padding: 0px;"> <br /></h1> <h1 class="course-intro-heading" style="box-sizing: border-box; color: #050505; font-size: 26px; line-height: 1.25em; margin: 0px 0px 0.45em; padding: 0px;"> <br /></h1> <h1 class="course-intro-heading" style="box-sizing: border-box; color: #050505; font-size: 26px; line-height: 1.25em; margin: 0px 0px 0.45em; padding: 0px;"> Data Science</h1> <div class="course-intro-lead-in" style="box-sizing: border-box; font-size: 20px; line-height: 1.3em; margin-bottom: 0.585em;"> Learn the R statistical programming language, the lingua franca of data science in this hands-on course.</div> <a class="course-org-link" href="https://www.edx.org/school/microsoft" style="box-sizing: border-box; color: #2968aa; display: inline-block; text-decoration: none;"><img alt="Microsoft Logo" class="course-org-logo" src="https://prod-edx-mktg-edit.edx.org/sites/default/files/school/image/logo/microsoft201x101.png" style="border: 0px; box-sizing: border-box; display: inline; float: left; height: auto; margin: 0px 20px 0px 0px; max-width: 160px; position: relative;" /></a></div> Self-Paced<br /><a href="https://courses.edx.org/register?course_id=course-v1%3AMicrosoft%2BDAT204x%2B5T2016&enrollment_action=enroll&email_opt_in=true">Enroll Now</a><div> <br /></div> <div> <div class="content-grouping" style="box-sizing: border-box; color: #4e4e4e; font-family: "Open Sans", Arial, Helvetica, sans-serif; font-size: 16px; line-height: 24px; margin-bottom: 24px;"> <h2 class="course-info-heading" style="border-bottom-width: 0px; box-sizing: border-box; color: #050505; display: inline-block; font-size: 1.25em; line-height: 1.45em; margin: 0px; padding: 0px 0px 0.45em;"> About this course</h2> <div class="course-description wysiwyg-content expanded" style="box-sizing: border-box; position: relative;"> <div class="see-more-content" style="box-sizing: border-box; height: auto; overflow: hidden;" tabindex="-1"> <div style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 1em;"> </div> <div style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 1em;"> R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Whether you are full-time number cruncher, or just the occasional data analyst, R will suit your needs.</div> <div style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 1em;"> This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required.</div> <div style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 1em;"> What makes this course unique is that you will continuously practice your newly acquired skills through interactive in-browser coding challenges using the DataCamp platform. Instead of passively watching videos, you will solve real data problems while receiving instant and personalized feedback that guides you to the correct solution.</div> <div style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 1em;"> Enjoy!</div> <div style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 1em;"> </div> </div> <div aria-hidden="true" class="see-more-cta" style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em;"> <a class="see-more-reveal" href="https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-1#!" style="box-sizing: border-box; color: #2968aa; text-decoration: none;"><span aria-hidden="true" class="fa-stack fa-lg" style="box-sizing: border-box; display: inline-block; font-size: 1.33333em; height: 2em; line-height: 2em; position: relative; vertical-align: middle; width: 20px;"><span class="fa fa-circle-o fa-stack-1x" style="-webkit-font-smoothing: antialiased; box-sizing: border-box; display: inline-block; font-family: FontAwesome; font-size: inherit; font-stretch: normal; left: 0px; line-height: inherit; position: absolute; text-align: center; text-rendering: auto; transform: translate(0px, 0px); width: 20px;"></span><span class="fa fa-minus fa-stack-1x" style="-webkit-font-smoothing: antialiased; box-sizing: border-box; display: inline-block; font-family: FontAwesome; font-size: 0.5em; font-stretch: normal; left: 0px; line-height: inherit; position: absolute; text-align: center; text-rendering: auto; transform: translate(0px, 0px); width: 20px;"></span></span> Hide</a></div> </div> </div> <div class="content-grouping" style="box-sizing: border-box; color: #4e4e4e; font-family: "Open Sans", Arial, Helvetica, sans-serif; font-size: 16px; line-height: 24px; margin-bottom: 24px;"> <h3 class="course-info-heading" style="border-bottom-width: 0px; box-sizing: border-box; color: #050505; display: inline-block; font-size: 1.25em; line-height: 1.5em; margin: 0px; padding: 0px;"> What you'll learn</h3> <div class="course-info-list wysiwyg-content" style="box-sizing: border-box;"> <ul style="box-sizing: border-box; margin: 0px 0px 20px; padding: 12px 0px 12px 16px;"> <li style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 5px;">Introductory R language fundamentals and basic syntax</li> <li style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 5px;">What R is and how it’s used to perform data analysis</li> <li style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 5px;">Become familiar with the major R data structures</li> <li style="box-sizing: border-box; font-size: 1em !important; line-height: 1.5em; margin-bottom: 5px;">Create your own visualizations using R</li> </ul> </div> <div aria-hidden="true" style="box-sizing: border-box; font-size: 1em; line-height: 1.5em; margin-bottom: 1em;"> <a class="see-more-cta toggle-display" href="https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-1#!" style="box-sizing: border-box; color: #2968aa; margin-bottom: 0px; text-decoration: none;"><span aria-hidden="true" class="fa fa-file-text-o fa-lg" style="-webkit-font-smoothing: antialiased; box-sizing: border-box; display: inline-block; font-family: FontAwesome; font-size: 1.33333em; font-stretch: normal; line-height: 0.75em; text-rendering: auto; transform: translate(0px, 0px); vertical-align: -15%;"></span> View Course Syllabus</a></div> </div> </div>

Data Science: Microsoft

Introduction to R for  Data Science Learn the R statistical programming language, the lingua franca of data science in this...

Read more »

Journey to the Frontier of Computational Biology

<div dir="ltr" style="text-align: left;" trbidi="on"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgmzASbfy8U22Ykm4tkyUmFNdKTN2Z31robaQbKNGCF1cwWPiarjU1IWLXP31LDuuEvhF_i3omJbvz5wVgJTlilsFbaWQyLkLeELeYwfQ4FHHpPg6aNr5nyCT7c4PgOot2_8FB1Ly6Az5ir/s1600/UCSanDiego-Coursera-wide+%25281%2529.png" imageanchor="1"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgmzASbfy8U22Ykm4tkyUmFNdKTN2Z31robaQbKNGCF1cwWPiarjU1IWLXP31LDuuEvhF_i3omJbvz5wVgJTlilsFbaWQyLkLeELeYwfQ4FHHpPg6aNr5nyCT7c4PgOot2_8FB1Ly6Az5ir/s400/UCSanDiego-Coursera-wide+%25281%2529.png" /></a><br /><br /><br /><a href="https://www.coursera.org/specializations/computational-biology">Program Information</a><br /><h2 style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 22px; font-weight: normal; line-height: 24px; margin: 36px 0px 16px; padding: 0px;">Join Us in a Top 50 MOOC of All Time!</h2><div style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; margin-bottom: 20px; min-height: 20px;">How do we sequence and compare genomes? How do we identify the genetic basis for disease? How do we construct an evolutionary Tree of Life for all species on Earth?</div><div style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; margin-bottom: 20px; min-height: 20px;">When you complete this Specialization, you will learn how to answer many questions in modern biology that have become inseparable from the computational approaches used to solve them. You will also obtain a toolkit of existing software resources built on these computational approaches and that are used by thousands of biologists every day in one of the fastest growing fields in science.</div><div style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; margin-bottom: 20px; min-height: 20px;">Although this Specialization centers on computational topics, you do not need to know how to program in order to complete it. If you are interested in programming, we feature an "Honors Track" (called "hacker track" in previous runs of the course). The Honors Track allows you to implement the bioinformatics algorithms that you will encounter along the way in dozens of automatically graded coding challenges. By completing the Honors Track, you will be a bioinformatics software professional!</div><div style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; margin-bottom: 20px; min-height: 20px;">Learn more about the Bioinformatics Specialization (including why we are wearing these crazy outfits) by watching our <a href="https://youtu.be/yfXeKPt0nw4" rel="noopener nofollow" style="background: transparent; box-sizing: border-box; color: #428bca; margin-bottom: 0px !important; text-decoration: none;" target="_blank">introductory video</a>.</div><div style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; margin-bottom: 20px; min-height: 20px;">You can purchase the Specialization's print companion, <em style="box-sizing: border-box;">Bioinformatics Algorithms: An Active Learning Approach</em>, from the <a href="http://bioinformaticsalgorithms.com/" rel="noopener nofollow" style="background: transparent; box-sizing: border-box; color: #428bca; margin-bottom: 0px !important; text-decoration: none;" target="_blank">textbook website</a>.</div><div style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; min-height: 20px;">Our first course, "Finding Hidden Messages in DNA", was named a top-50 MOOC of all time by <a href="https://www.class-central.com/report/top-moocs/" rel="noopener nofollow" style="background: transparent; box-sizing: border-box; color: #428bca; margin-bottom: 0px !important; text-decoration: none;" target="_blank">Class Central</a>!</div></div>

Journey to the Frontier of Computational Biology

Program Information Join Us in a Top 50 MOOC of All Time! How do we sequence and compare genomes? How do we identify the genetic basis for d...

Read more »

Computational Neuroscience - Washington University

<div dir="ltr" style="text-align: left;" trbidi="on"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgQkytVSituCC6dHv3OXDE37PLoQOI4_i0uateqaSIvLc2tsnXQNDWkVBqZD_lcLHniKlYIeumxvJ0OS35nUVpNMNxnt8ic9givXjSWP8gFcmK60pmBtBiSDR6Ym12-XvWAKHakGT3PfANo/s1600/UW_square_180x180.png" imageanchor="1"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgQkytVSituCC6dHv3OXDE37PLoQOI4_i0uateqaSIvLc2tsnXQNDWkVBqZD_lcLHniKlYIeumxvJ0OS35nUVpNMNxnt8ic9givXjSWP8gFcmK60pmBtBiSDR6Ym12-XvWAKHakGT3PfANo/s400/UW_square_180x180.png" /></a><br /><br /><a href="https://www.coursera.org/learn/computational-neurosciencecompneuro">Program Information</a><br /><br /><strong class="body-2-text" data-reactid=".s1lfym5w5c.0.0.1.1.0.0.0:$section_overview.1.1.0.0.0.0.0" style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; white-space: pre-wrap;">About this course: </strong><span data-reactid=".s1lfym5w5c.0.0.1.1.0.0.0:$section_overview.1.1.0.0.0.0.1" style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; white-space: pre-wrap;">This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.</span><br /><span data-reactid=".s1lfym5w5c.0.0.1.1.0.0.0:$section_overview.1.1.0.0.0.0.1" style="background-color: white; box-sizing: border-box; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; white-space: pre-wrap;"><br /></span><div class="headline-2-text partner-name" data-reactid=".s1lfym5w5c.0.0.1.1.0.0.0:$section_creators.1.0.0" style="box-sizing: border-box; font-family: OpenSans-Light, Arial, sans-serif; font-size: 20px; line-height: 24px; margin-bottom: 15px;"><span style="background-color: white;">About University of Washington</span></div><span data-reactid=".s1lfym5w5c.0.0.1.1.0.0.0:$section_overview.1.1.0.0.0.0.1" style="background-color: white; box-sizing: border-box; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; white-space: pre-wrap;"></span><br /><div class="body-1-text" data-reactid=".s1lfym5w5c.0.0.1.1.0.0.0:$section_creators.1.0.1" style="box-sizing: border-box; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px;"><span style="background-color: white;">Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.</span></div></div>

Computational Neuroscience - Washington University

Program Information About this course: This course provides an introduction to basic computational methods for understanding what nervous sy...

Read more »

Expertise for Professionals and Students in Biotechnology and Biomedical Data Sciences

<div dir="ltr" style="text-align: left;" trbidi="on"><br /><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiwtCPXOu0JnmuvmSGJi6gMGyv-ztxoNBJ0ejcqOT75w3TsvVm-lAu1WOe3f7RKPRb8vGm6WU1aQ9X8RtEUufg-Ojvv97QL-3FuolCUl0H7PE8VBJNjbVjTS0weUILgKc-sihz20BGVRHA0/s1600/wide_logo_200X48_v2.png" imageanchor="1"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiwtCPXOu0JnmuvmSGJi6gMGyv-ztxoNBJ0ejcqOT75w3TsvVm-lAu1WOe3f7RKPRb8vGm6WU1aQ9X8RtEUufg-Ojvv97QL-3FuolCUl0H7PE8VBJNjbVjTS0weUILgKc-sihz20BGVRHA0/s400/wide_logo_200X48_v2.png" /></a><br /><br /><a href="https://www.coursera.org/specializations/systems-biology">Program Information</a><br /><br /><h1 style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans-Light, Arial, sans-serif; font-size: 32px; font-weight: normal; line-height: 36px; margin: 0px 0px 20px; padding: 0px;">Design systems-level experiments using appropriate cutting edge techniques, collect big data, and analyze and interpret small and big data sets quantitatively.</h1><div style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; min-height: 20px;">The Systems Biology Specialization covers the concepts and methodologies used in systems-level analysis of biomedical systems. Successful participants will learn how to use experimental, computational and mathematical methods in systems biology and how to design practical systems-level frameworks to address questions in a variety of biomedical fields. In the final Capstone Project, students will apply the methods they learned in five courses of specialization to work on a research project.</div><div style="background-color: whitesmoke; box-sizing: border-box; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; min-height: 20px;"><br /></div><div class="partner-marketing-blurb" data-reactid=".0.0.1.1.1.1.3.1:$27.0.1.0" style="box-sizing: border-box; font-family: OpenSans-Light, Arial, sans-serif; font-size: 16px; line-height: 19px;">Icahn School of Medicine at Mount Sinai, in New York City, is a leader in medical and scientific training and education, biomedical research and patient care. The Institute for Systems Biomedicine is an interdisciplinary entity that brings together faculty who are world leaders in systems biology, medicine and systems pharmacology research at Mount Sinai.</div><div class="partner-short-description" data-reactid=".0.0.1.1.1.1.3.1:$27.0.1.1" style="box-sizing: border-box; font-family: OpenSans-Light, Arial, sans-serif; font-size: 14px; line-height: 19px; padding-top: 25px;">The Icahn School of Medicine at Mount Sinai, in New York City is a leader in medical and scientific training and education, biomedical research and patient care.</div></div>

Expertise for Professionals and Students in Biotechnology and Biomedical Data Sciences

Program Information Design systems-level experiments using appropriate cutting edge techniques, collect big data, and analyze and interpret ...

Read more »

Master Statistics with R: Duke University

<div dir="ltr" style="text-align: left;" trbidi="on"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEionIIzqyZyZP3vSjBkU9PVcJYfKmvjFLYSLd8O-a_UCzoTkAJUtKm1CNrMXO9lOb0WnC0GvGcKqdqAZYB7oaBGgbP0g1lbL_JgHjI6zy7ITfwemJQcjeZ-ZBHC0UC3O9cggTMaojGMiYkv/s1600/dukewidelogo2.png" imageanchor="1"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEionIIzqyZyZP3vSjBkU9PVcJYfKmvjFLYSLd8O-a_UCzoTkAJUtKm1CNrMXO9lOb0WnC0GvGcKqdqAZYB7oaBGgbP0g1lbL_JgHjI6zy7ITfwemJQcjeZ-ZBHC0UC3O9cggTMaojGMiYkv/s400/dukewidelogo2.png" /></a><br /><br />Program: Paid<br /><a href="https://www.coursera.org/specializations/statistics">Program Information</a><br /><br /><div class="rc-SectionHeader" data-reactid=".0.0.1.1.1.1.0.0" style="background-color: whitesmoke; border-bottom-color: rgb(230, 230, 230); border-bottom-style: solid; border-bottom-width: 1px; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; margin-bottom: 20px;"><div class="s12n-section-header-title" data-reactid=".0.0.1.1.1.1.0.0.0" style="border-bottom-color: rgb(184, 145, 115); border-bottom-style: solid; border-bottom-width: 1px; box-sizing: border-box; display: inline-block; font-size: 16px; line-height: 19px; margin: 0px 0px -1px; padding: 10px 40px 10px 0px;">About This Specialization</div></div><div class="rc-CML styled" data-reactid=".0.0.1.1.1.1.0.1" style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; word-wrap: break-word;"><div data-reactid=".0.0.1.1.1.1.0.1.0" style="box-sizing: border-box; margin-bottom: 0px !important;"><div style="box-sizing: border-box; margin-bottom: 20px; min-height: 20px;">In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.</div><div style="box-sizing: border-box; min-height: 20px;">You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.</div><div style="box-sizing: border-box; min-height: 20px;"><br /></div></div></div></div>

Master Statistics with R: Duke University

Program: Paid Program Information About This Specialization In this Specialization, you will learn to analyze and visualize data in R and cr...

Read more »

Data Science: Big Data Specialization: UC San Diego

<div dir="ltr" style="text-align: left;" trbidi="on"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg6davQOk2r_Fv8BtENJErvEadEuNifLnpANHuduz0S1hdSF4SqgD5krDVpV-NA89cBZQyrh8Ao4FHDErxnsUJhXcBBoHInu-PYgbdGlJOiIOe8LL1bUuSClgNvcKShnK2t5DJexyoFInqf/s1600/UCSanDiego-Coursera-wide.png" imageanchor="1"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg6davQOk2r_Fv8BtENJErvEadEuNifLnpANHuduz0S1hdSF4SqgD5krDVpV-NA89cBZQyrh8Ao4FHDErxnsUJhXcBBoHInu-PYgbdGlJOiIOe8LL1bUuSClgNvcKShnK2t5DJexyoFInqf/s400/UCSanDiego-Coursera-wide.png" /></a><br /><br />Program fee: paid <br /><a href="https://www.coursera.org/specializations/bigdata">Program information</a><br /><br /><br /><div data-reactid=".0.0.1.1.1.1.0.2" style="-webkit-text-stroke-width: 0px; background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: 21px; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px;"></div><br /><div class="rc-CML styled" data-reactid=".0.0.1.1.1.1.0.1" style="-webkit-text-stroke-width: 0px; background-color: whitesmoke; box-sizing: border-box; font-family: OpenSans, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: 21px; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px; word-wrap: break-word;"><div data-reactid=".0.0.1.1.1.1.0.1.0" style="box-sizing: border-box; margin-bottom: 0px !important;"><div style="box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; margin-bottom: 0px !important; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 20px;">Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.</div><div style="box-sizing: border-box; color: #333333; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; margin-bottom: 0px !important; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 20px;"><br /></div><div class="partner-marketing-blurb" data-reactid=".0.0.1.1.1.1.3.1:$53.0.1.0" style="box-sizing: border-box; font-family: OpenSans-Light, Arial, sans-serif; font-size: 16px; line-height: 19px;">The San Diego Supercomputer Center (SDSC) at UC San Diego is a leader in data-intensive computing and cyberinfrastructure. SDSC supports hundreds of multidisciplinary programs spanning domains from earth sciences and biology to astrophysics and bioinformatics.</div><div class="partner-short-description" data-reactid=".0.0.1.1.1.1.3.1:$53.0.1.1" style="box-sizing: border-box; font-family: OpenSans-Light, Arial, sans-serif; line-height: 19px; padding-top: 25px;">UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.</div></div></div></div>

Data Science: Big Data Specialization: UC San Diego

Program fee: paid Program information Do you need to understand big data and how it will impact your business? This Specialization is for yo...

Read more »

Data Science Specilazation: Johns Hopkins University

<div dir="ltr" style="text-align: left;" trbidi="on"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj17xW8IWzun_qXETJv-na80ApUNrf2bshq-hTN6UaBLEkWUEY_g4jAYPDtgNbCb4vjw3pDCofIxe5mvVXTtDlYceZedK6QZWdaHxg7FJzcrcIrM2Ra37vVVKPsY7IPCtbhqtDIdF5WkBn5/s1600/JHU-Logo-Square-Mini_180px.png" imageanchor="1"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj17xW8IWzun_qXETJv-na80ApUNrf2bshq-hTN6UaBLEkWUEY_g4jAYPDtgNbCb4vjw3pDCofIxe5mvVXTtDlYceZedK6QZWdaHxg7FJzcrcIrM2Ra37vVVKPsY7IPCtbhqtDIdF5WkBn5/s400/JHU-Logo-Square-Mini_180px.png" /></a><br />Program fee: Paid  <br /><a href="https://www.coursera.org/specializations/jhu-data-science#">Program Information</a><br /><br /><h1 style="background-color: whitesmoke; box-sizing: border-box; color: #333333; font-family: OpenSans-Light, Arial, sans-serif; font-size: 32px; font-weight: normal; line-height: 36px; margin: 0px 0px 20px; padding: 0px;">Ask the right questions, manipulate data sets, and create visualizations to communicate results.</h1><div style="background-color: whitesmoke; box-sizing: border-box; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; min-height: 20px;"><span style="color: #333333;">This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inference</span>s and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.</div><div style="background-color: whitesmoke; box-sizing: border-box; font-family: OpenSans, Arial, sans-serif; font-size: 14px; line-height: 21px; min-height: 20px;"><br /></div><div class="partner-marketing-blurb" data-reactid=".0.0.1.1.1.1.3.1:$8.0.1.0" style="box-sizing: border-box; font-family: OpenSans-Light, Arial, sans-serif; font-size: 16px; line-height: 19px;">Johns Hopkins University is recognized as a destination for excellent, ambitious scholars and a world leader in teaching and research. The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.</div><div class="partner-short-description" data-reactid=".0.0.1.1.1.1.3.1:$8.0.1.1" style="box-sizing: border-box; font-family: OpenSans-Light, Arial, sans-serif; font-size: 14px; line-height: 19px; padding-top: 25px;">The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.</div><div class="partner-short-description" data-reactid=".0.0.1.1.1.1.3.1:$8.0.1.1" style="box-sizing: border-box; font-family: OpenSans-Light, Arial, sans-serif; font-size: 14px; line-height: 19px; padding-top: 25px;"><br /></div><div class="partner-short-description" data-reactid=".0.0.1.1.1.1.3.1:$8.0.1.1" style="box-sizing: border-box; color: white; font-family: OpenSans-Light, Arial, sans-serif; font-size: 14px; line-height: 19px; padding-top: 25px;"><br /></div></div>

Data Science Specilazation: Johns Hopkins University

Program fee: Paid   Program Information Ask the right questions, manipulate data sets, and create visualizations to communicate results. Thi...

Read more »

Data Analysis for Life Sciences

<div dir="ltr" style="text-align: left;" trbidi="on"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhX8msjumQc7pWAfTnsw5w5-A-pIPQGeU-DsbBPbOqSi8E5QEBqcSZ-gUokl3uLLvY3SPgNNUKYlRrOObRZ9VP0Mu87bD1oUAnk0GA4VPulCQxwzLwHqxUMJLROMxRJoobiJKJBl8iXyH6w/s1600/harvard_logo_200x101_0+%25281%2529.png" imageanchor="1"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhX8msjumQc7pWAfTnsw5w5-A-pIPQGeU-DsbBPbOqSi8E5QEBqcSZ-gUokl3uLLvY3SPgNNUKYlRrOObRZ9VP0Mu87bD1oUAnk0GA4VPulCQxwzLwHqxUMJLROMxRJoobiJKJBl8iXyH6w/s400/harvard_logo_200x101_0+%25281%2529.png" /></a><br /><br />Program: <span style="background-color: white; color: #4e4e4e; font-family: "Open Sans", Arial, Helvetica, sans-serif; font-size: 16px; font-weight: 700; line-height: 24px; text-align: center;">Self-Paced</span><br /><span style="background-color: white; color: #4e4e4e; font-family: "Open Sans", Arial, Helvetica, sans-serif; font-size: 16px; font-weight: 700; line-height: 24px; text-align: center;"><br /></span><div class="overview expanded" style="background-color: white; box-sizing: border-box; color: #4e4e4e; font-family: "Open Sans", Arial, Helvetica, sans-serif; font-size: 16px; line-height: 24px; position: relative;"><div class="see-more-content" style="box-sizing: border-box; height: auto; outline: none; overflow: hidden;" tabindex="-1"><h3 style="box-sizing: border-box; color: #4a4a4a; font-size: 1.1em; line-height: 1.5em; margin: 0px 0px 1.1em; padding: 0px;"><span style="box-sizing: border-box; color: #303030;"><a href="https://www.edx.org/xseries/data-analysis-life-sciences">XSeries Program Overview</a></span></h3><div style="box-sizing: border-box; margin-bottom: 1em;">Currently, biomedical research groups around the world are producing more data than they can handle.</div><div style="box-sizing: border-box; margin-bottom: 1em;">The training and skills acquired by taking the Data Analysis for Life Sciences XSeries will result in greater success in biological discovery and improving individual and population health.</div><div style="box-sizing: border-box; margin-bottom: 1em;">In this XSeries, you will gain the tools to analyze and interpret life sciences data. You will learn the basic statistical concepts and R programming skills necessary for analyzing real data.</div><div style="box-sizing: border-box; margin-bottom: 1em;">R is an open source free statistical software and is the most widely used data analysis platforms among academic statisticians.</div><div style="box-sizing: border-box; margin-bottom: 1em;">Taught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, who for the past 15 years has focused on the analysis of genomics data, this XSeries is perfect for anyone in the life sciences who wants to learn how to analyze data. Problem sets will require coding in the R language to ensure learners fully grasp and master key concepts.</div></div><div aria-hidden="true" class="see-more-cta" style="box-sizing: border-box;"><br /></div></div><div style="background-color: white; box-sizing: border-box; color: #4e4e4e; font-family: "Open Sans", Arial, Helvetica, sans-serif; font-size: 16px; line-height: 24px;"><h3 style="box-sizing: border-box; color: #4a4a4a; font-size: 1.1em; line-height: 1.5em; margin: 0px 0px 1.1em; padding: 0px;"><span style="box-sizing: border-box; color: #303030;">What You'll Learn</span></h3><ul style="box-sizing: border-box; margin: 1em 0px; padding: 0px 0px 0px 40px;"><li style="box-sizing: border-box;">Basic statistical concepts and R programming skills necessary for analyzing data in the life sciences</li><li style="box-sizing: border-box;">The underlying mathematical basics of linear models useful for data analysis in the life sciences</li><li style="box-sizing: border-box;">The techniques commonly used to perform statistical inference on high throughput data</li><li style="box-sizing: border-box;">Several techniques that are widely used in the analysis of high-dimensional data</li></ul><div class="separator" style="clear: both; text-align: center;"><iframe width="320" height="266" class="YOUTUBE-iframe-video" data-thumbnail-src="https://i.ytimg.com/vi/nwFyIxc4AE0/0.jpg" src="https://www.youtube.com/embed/nwFyIxc4AE0?feature=player_embedded" frameborder="0" allowfullscreen></iframe></div><div><br /></div></div></div>

Data Analysis for Life Sciences

Program:  Self-Paced XSeries Program Overview Currently, biomedical research groups around the world are producing more data than they can h...

Read more »