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Qualitative analysis of interview data: A step-by-step guide
 
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The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Text and video (including audio) © Kent Löfgren, Sweden
Views: 704714 Kent Löfgren
Making data mean more through storytelling | Ben Wellington | TEDxBroadway
 
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Ben Wellington uses data to tell stories. In fact, he draws on some key lessons from fields well outside computer science and data analysis to make his observations about New York City fascinating. Never has a fire hydrant been so interesting as in this talk. Ben Wellington is a computer scientist and data analyst whose blog, I Quant NY, uses New York City open data to tell stories about everything from parking ticket geography to finding the sweet spot in MetroCard pricing. His articles have gone viral and, in some cases, led to policy changes. Wellington teaches a course on NYC open data at the Pratt Institute and is a contributor to Forbes and other publications. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 158067 TEDx Talks
Metagenomics Analysis and Assembly-Based Metagenomics
 
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This is the fourth session in the 2017 Microbiome Summer School: Big Data Analytics for Omics Science organized by the Université Laval Big Data Research Center and the Canadian Bioinformatics Workshops. This lecture is by Morgan Langille from Dalhousie University and Frederic Raymond from Universite Laval. For tutorials and lecture slides for this workshop, please visit bioinformaticsdotca.github.io. How it Begins by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100200 Artist: http://incompetech.com/
Views: 3070 Bioinformatics DotCa
Math Antics - Mean, Median and Mode
 
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Learn More at mathantics.com Visit http://www.mathantics.com for more Free math videos and additional subscription based content!
Views: 931768 mathantics
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 850617 Dr Nic's Maths and Stats
James Powell - Building Web-based Analysis & Simulation Platforms
 
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Titles - Building Web-based Analysis & Simulation Platforms with React/Redux, Flask, Celery, Bokeh, and Numpy Filmed at PyData 2017 Description What use is analytical code if it can't be integrated into a business workflow to solve real problems? This tutorial is about integrating analytical work into a real production system that can be used by business users. It focuses on building a web-based platform for managing long-running analytical code and presenting results in a convenient format, using cutting-edge combination of tools. Abstract The purpose of this stack is to be able to rapidly create web-based environments for users to interact with the results of analytical and simulation processes (without needing to retrain one's self as a web programmer!) This tutorial is composed of the following pieces: building a simple simulation using Numpy. For the purposes of this tutorial, we model a very simple Monte Carlo simulation with a number of user-controllable, tweakable algorithm inputs and model parameters. The simulation is chosen to be simple enough to present and code quickly. The purpose of this tutorial is not building Monte Carlo simulations but packaging them into lightweight production systems. Celery for launching and managing the above simulation jobs. This tutorial will not cover all aspects of Celery. It will merely show how the tool can be used as a job management system. Flask as a very thin JSON API layer. The tutorial will make use of Flask plugins for quickly building JSON APIs. This is the thinnest and least interesting component of the tutorial and won't be covered in great depth. React + Redux for a slick, simple single-page app. Attendees are expected to be least familiar with Javascript and the React ecosystem. The tutorial will spend a fair amount of time on this component, and will cover setting up build environment using Babel (for JSX transpilation) and Gulp as a build system. Bokeh for presenting graphical results from the simulation. This component may be cut based on time considerations. If time permits, it might also be possible to discuss the use of React Native to quickly build mobile apps using the same infrastructure. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 10674 PyData
Data & Analytics helps executives make business-defining decisions better and faster
 
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Learn more at PwC.com - http://pwc.to/1GiIFe4 Highly data-driven companies are making better business decisions. PwC considers how data and analytics can make a difference when it comes to the most significant decisions about the strategic direction of your business.
Views: 41390 PwC
A Day in the Life of a Data Analyst
 
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Take a look behind the scenes at the Intermountain Healthcare employees that keep us running smoothly! Our Data Analyst's work hard each day using data, research, numbers, and demographics to help people live the healthiest lives possible.
Views: 124259 Intermountain Healthcare
Slaying Excel Dragons Book #44; Data Analysis Filter Feature: Extract Data Based on Criteria
 
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Download files: https://people.highline.edu/mgirvin/ExcelIsFun.htm Learn Excel from beginning to end. Complete Lessons about Excel. This video series accompanies the published book, Slaying Excel Dragons, ISBN 9781615470006 Chapter 6: Data Analysis Filter Feature: Extract Data Based on Criteria Pages 420 - 433 Topics: 1. Turn On Filter with Keyboard Shortcut (Toggle) 2. Filter Hides records that do not match criteria 3. Filter with One Criterion 4. Filter With Two Criteria (And criteria) 5. Filter With Two Criteria (Or criteria) 6. Extract Records 7. Clear Filters 8. Right-click Filtering 9. Filter by Color 10. Filter Below Average 11. Filter Top Five Sales 12. Sort after filter 13. Filter Words 14. Contains 15. Filter Dates 16. Filter to add or average with criteria Excel. Excel Basics. Excel intermediate. Excel How To. Learn Excel. Excel 2010.
Views: 9442 ExcelIsFun
Philip Evans: How data will transform business
 
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What does the future of business look like? In an informative talk, Philip Evans gives a quick primer on two long-standing theories in strategy — and explains why he thinks they are essentially invalid. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
Views: 233038 TED
Analyzing and modeling complex and big data | Professor Maria Fasli | TEDxUniversityofEssex
 
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This talk was given at a local TEDx event, produced independently of the TED Conferences. The amount of information that we are creating is increasing at an incredible speed. But how are we going to manage it? Professor Maria Fasli is based in the School of Computer Science and Electronic Engineering at the University of Essex. She obtained her BSc from the Department of Informatics of T.E.I. Thessaloniki (Greece). She received her PhD from the University of Essex in 2000 having worked under the supervision of Ray Turner in axiomatic systems for intelligent agents. She has previously worked in the area of data mining and machine learning. Her current research interests lie in agents and multi-agent systems and in particular formal theories for reasoning agents, group formation and social order as well as the applications of agent technology to e-commerce. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 135559 TEDx Talks
Choosing which statistical test to use - statistics help.
 
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Seven different statistical tests and a process by which you can decide which to use. The tests are: Test for a mean, test for a proportion, difference of proportions, difference of two means - independent samples, difference of two means - paired, chi-squared test for independence and regression. This video draws together videos about Helen, her brother, Luke and the choconutties. There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.
Views: 731437 Dr Nic's Maths and Stats
Temporal analysis: Generating time series from events based data
 
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Often data is captured in a different format than required for analysis. Have you ever needed to perform historical analysis on events-based data? For example, how do you calculate turnover based on employees' start and end dates? Or, if sensor data captures when a device switches between on, off, and idle, how do you calculate the percent of time that a device was active per period? Join this Jedi session to find out!
Views: 556 Tableau Software
How to Extract Data from a Spreadsheet using VLOOKUP, MATCH and INDEX
 
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When you need to find and extract a column of data from one table and place it in another, use the VLOOKUP function. This function works in any version of Excel in Windows and Mac, and also in Google Sheets. It allows you to find data in one table using some identifier it has in common with another table. The two tables can be on different sheets or even on different workbooks. There is also an HLOOKUP function, which does the same thing, but with data arranged horizontally, across rows. See the companion tutorial on Tuts+ at https://computers.tutsplus.com/tutorials/how-to-extract-data-from-a-spreadsheet-using-vlookup-match-and-index--cms-20641. By Bob Flisser.
Views: 2773062 Tuts+ Computer Skills
EAF #99 - Criteria-based analysis of a large fitness testing data set
 
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Part 1 of 2: This video demonstrates how you can calculate criteria based averages, maximums and minimums of a large fitness testing data set. Uses COIUNTIFS, AVERAGEIFS, MAXIFS, AGGREGATE, TODAY, NOW For a copy of the file please email [email protected] This channel is all about excel for people who work in sport and use Excel for 2016 If you are interested in more Excel tutorials check out my three series on Vimeo for S&Cs and Sport Scientists https://vimeo.com/ondemand/athletemonitoring https://vimeo.com/ondemand/strengthtemplate https://vimeo.com/ondemand/excelforstrengthcoaches Discount codes below (different codes for US v rest of world) Intro to Excel for S&Cs Tricks or TricksUS = 50% Excel Tricks for Sports: Create an Athlete Monitoring System Track or TrackUSA = 50% Excel Tricks for Sports - Strength Training Manager ET4S or ET4SUSA = 50% If you are a performance analyst then consider this video series and other excellent resources from the videoanalyst http://thevideoanalyst.com/courses/excel-for-analysts/
Data Science - 5.3.6 - Evidence-based Data Analysis part 1
 
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Data Science PLAYLIST: https://tinyurl.com/DataSciencePlaylist Unit 5: Reproducible Research Part 3: Reproducible Research Checklist & Evidence-based Data Analysis Lesson 6 - Evidence-based Data Analysis part 1 Notes: https://tinyurl.com/DataScienceNotes
Views: 5 Bob Trenwith
Deciding based on data. Predictive Analysis and EDA (Exploratory Data Analysis)
 
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Evidence data based decision making. Predictions based on data and statistics. Useful for Engineering Sciences Finances and Legal matters. Engineering Statistics. NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/, date.
Views: 32 8289468
Data analysis with python and Pandas - Select Row, column based on condition Tutorial 10
 
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This video will explain how to select subgroup of rows based on logical condition. Visit complete course on Data Science with Python : https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=YTSOCIAL090 For All other visit my udemy profile at : https://www.udemy.com/user/ankitmistry/
Views: 5691 MyStudy
IDS Project 2018: Based on Data Preprocessing(Analysis of red wine dataset)
 
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Analysis is based on the 12 different attributes of the red wine dataset and it concludes the accuracy with which we can help in manufacturing superior quality of red wine.
Views: 84 KAUSTUV VARUN
Data Scientist Vs Data Analyst
 
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In this video I want to talk about the differences between a data scientist and a data analyst. is data science a viable career and if so should you try to become a data scientist or a data analyst. ► Full Playlist Exploring All Things Data Science ( http://bit.ly/2mB4G0N ) ► Top 4 Best Laptops for the Data Industry ( https://youtu.be/Vtk50Um_yxA ) ► Data Scientist Masters Certification ( http://bit.ly/2yCbsac ) ► Get the Best Certified Tutorials on Data Analytics... http://jobsinthefuture.com/index.php/2017/10/13/data-scientist-vs-data-analytics-what-is-the-big-data-difference/ Questions: - What is the best career path for a data scientist? - How do I become a data analyst? - What is the difference between a data scientist and a data analyst? - Is data science the same as data analytics? - Is data science a viable career path? - Is data analytics a viable career path? Jobs related to data science are booming right now with the tech industry growing at a rapid pace, but there is a lot of confusion between the Role of a Data Scientist and a Data Analyst... I am going to QUICKLY breakdown the difference for you so that you can get started right away with your career in the Data Analytics industry! First of all what is data analytics... Data analytics is the extraction a large, large, large amounts of data that are stored within a data base. This data comes from a multiplicity of places all over the world via website traffic, in-store and online purchases, social media activity, traffic patterns, etc, etc, etc.... the list could go on and on. Basically everything we do is being collected to be used as data to advertise to us, keep us safer when we are driving, or help us find the restaurant we want to eat at. Now to The Role of Data Scientist - The IT Rock Star! Data Scientists are the top professionals in their industry. They usually hold a Masters Degree in some relative Computer Science degree or even a PhD. They understand, very well, data from a business point of view and he/she can make accurate prediction of the data to advise clients on their next big business move! Data scientists have a solid foundation of computer applications, modeling, statistics and math! Highly Advanced in coding (Python, MySQL, R, JavaScript, etc... Ability to do high levels of math quickly Fantastic Statistical Analysis Abilities Great Communication Skill: Written and Oral And they have a brilliant Knack for communicating between the IT world and the Business Professionals. Starting Salary: $115,000 The Role of Data Analyst A Data Analyst is very important in the world of data science. They are in charge of collecting, organizing, and and obtaining statistical information from a large amount of data sets (Data sets are very large pools of data that must be searched in order to find the data that is relevant to a specific study). They are also the ones responsible for formulating all of their findings into an accurate report of powerpoint presentation to give to their client or internal team. Strong Understanding of Hadoop Based Analytics (Program to help extract data from large data sets and the analyze the data) Familiar with data analytics in a business setting Must have data storing a retrieval skills Proficiency in decision making Have the ability to transform data into understandable presentation Starting Salary: $60,000 ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 101198 Ben G Kaiser
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hadoop Training | Edureka
 
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** Flat 20% Off (Use Code: YOUTUBE20) Hadoop Training: https://www.edureka.co/hadoop ** This Edureka Big Data tutorial ( Big Data Hadoop Blog series: https://goo.gl/LFesy8 ) helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial: 1) Evolution of Data 2) What is Big Data? 3) Big Data as an Opportunity 4) Problems in Encasing Big Data Opportunity 5) Hadoop as a Solution 6) Hadoop Ecosystem 7) Edureka Big Data & Hadoop Training Subscribe to our channel to get video updates. Hit the subscribe button above. #edureka #edurekaBigData #BigData #BigDataTutorial #BigDataTraining Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Check our complete Hadoop playlist here: https://goo.gl/hzUO0m - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 40 hours of assignment and 30 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig, Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark - - - - - - - - - - - - - - Who should go for this course? If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building successful career in Big Data - - - - - - - - - - - - - - Why Learn Hadoop? Big Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Customer Review: Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favourite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! ~ This is the killer education app... I've take two courses, and I'm taking two more.”
Views: 715461 edureka!
A Machine Learning-Based Trading Strategy Using Sentiment Analysis Data
 
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Slides available ► https://goo.gl/1Xc3fJ Full Event ► https://goo.gl/ucxU1S Watch all sessions: ► https://goo.gl/LrMFPA Tucker Balch - Co-Founder & CTO - Lucena Research. In this talk, Tucker shows how sentiment information in combination with a Machine Learning technique can provide a successful stock trading strategy. Specifically, he creates a predictive Machine Learning-based model for company stock prices based on the recent sentiment data; he uses that model as an input to build portfolios that are re-balanced weekly and simulate the performance of those portfolios. His results indicate that the sentiment information has predictive value and is useful as part of a Machine Learning strategy that significantly outperforms the market from which the candidate equities are drawn. Presentation held at 3rd Annual RavenPack Research Symposium entitled "Big Data Analytics for Alpha, Smart Beta & Risk Management". Visit us at ►https://www.ravenpack.com/ Follow RavenPack on Twitter ► https://twitter.com/RavenPack
Views: 10706 RavenPack
BroadE: Use of Web-based annotation tools for bioinformatic analysis of proteomics data
 
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Copyright Broad Institute, 2013. All rights reserved. The presentation above was filmed during the 2012 Proteomics Workshop, part of the BroadE Workshop series. The Proteomics Workshop provides a working knowledge of what proteomics is and how it can accelerate biologists' and clinicians' research. The focus of the workshop is on the most important technologies and experimental approaches used in modern mass spectrometry (MS)-based proteomics.
Views: 2207 Broad Institute
Data Analysis in Excel 9 - Filter Data Based on The Frist, Last, or Middle Values in a Cell in Excel
 
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Visit http://www.TeachExcel.com for more, including Excel Consulting, Macros, and Tutorials. This Excel Video Tutorial shows you how to Filter data in Excel based on the First, Last, or Middle Values in a cell. This covers some of the great Filter features in Excel. This tutorial will allow you to better view and analyze large data sets in Excel as well as to drill down content in Excel in order to view smaller subsets of a larger data set. For Excel consulting, classes, or to get the spreadsheet or macro used here visit the website http://www.TeachExcel.com There, you can also get more free Excel video tutorials, macros, tips, and a forum for Excel. Have a great day!
Views: 11524 TeachExcel
Change Impact Analysis based on Linked Data
 
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Application of Linked Data approach to System Engineering. Leading European transportation and health care companies takes up the challenge to establish and push forward an Interoperability Specification (IOS) as an open European standard for safety-critical system. This video shows how CRYSTAL Artemis project - http://www.crystal-artemis.eu/ - leveraged on the linked data approach to support a typical aerospace engineering method like a Change Impact Analysis with respect to interoperability. About CRYSTAL: CRYSTAL -- Critical System Engineering Acceleration- is an ARTEMIS project that take up results from previous European research projects to define an Interoperability standard (IOS) and a Reference Technology Platform (RTP) with a clear objective of industrialisation. IOS and RTP will enable a better integration of engineering tools based on the internet principles and web technologies like linked data throughout Product Lifecycle Management. With 4 industrial domains represented in this project including the aerospace domain, the project has proposed a user driven approach based on industrial scenarios and technology bricks.
NodeMerge: Template Based Efficient Data Reduction For Big-Data Causality Analysis
 
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To counter attacks, enterprises often rely on causality analysis on the system activity data. However, one major challenge is that ubiquitous system monitoring generates a colossal amount of data and hosting it is prohibitively expensive. There is a strong demand for techniques that reduce the storage of data for causality analysis and yet preserve the quality of the causality analysis. Read this paper in the ACM Digital Library: https://dl.acm.org/citation.cfm?id=3243763
Sleep quality analysis based on data collected by bluetooth ECG monitor clothing
 
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We analyzed data and wrote algorithms on Arduino to make sleep quality analysis and developed interface models to display real-time data on Processing.
Views: 62 Simin Zhai
UConnRCMPy: Python-based Data Analysis for Rapid Compression Machines | SciPy 2016 | Bryan Weber
 
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The ignition delay of a fuel/air mixture is an important quantity in designing combustion devices, and these data are also used to validate computational kinetic models for combustion. One of the typical experimental devices used to measure the ignition delay is called a Rapid Compression Machine (RCM). This work presents UConnRCMPy, an open-source Python package to process experimental data from the RCM at the University of Connecticut. Given an experimental measurement, UConnRCMPy computes the thermodynamic conditions in the reactor of the RCM during an experiment along with the ignition delay. UConnRCMPy relies on several packages from the SciPy stack and the broader scientific Python community. UConnRCMPy implements an extensible framework, so that alternative experimental data formats can be incorporated easily. In this way, UConnRCMPy improves the consistency of RCM data processing and enables reproducible analysis of the data.
Views: 512 Enthought
Bioinformatics Analysis of Nano-based Omics Data: Penny Nymark
 
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Pekka Kohonen and Penny Nymark (Misvik Biology Oy / eNanoMapper) Bioinformatics analysis of an omics dataset generated on a nanoparticle category will be carried out and the dose-dependence and mechanistic interpretations discussed. This video was taken at the February 2016 Enanomapper Workshop in Basel, find out more here: https://enanomapper.net/events/workshop-basel-2016/program
Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data
 
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User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst’s manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas. https://autodeskresearch.com/publications/annotation-graphs _________________________________ Jian Zhao, Michael Glueck, Simon Breslav, Fanny Chevalier, Azam Khan (2016) Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data based on User-Authored Annotations. IEEE Transactions on Visualization and Computer Graphics. To appear. http://dx.doi.org/10.1109/TVCG.2016.2598543 Autodesk Research http://www.autodeskresearch.com
Views: 515 Autodesk Research
Projects based Multi Omics Data Analysis Education
 
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Learn More: edu.t-bio.info Effective, precise and personalized diagnostics and treatment stemming from an improved understanding of diseases has been in part driven by increased and improved data collection, especially high-throughput data. Genomic, transcriptomic, proteomic, metabolomic, and other ‘omic datasets enable biological research on a scale not possible even in the recent past.
Views: 12121 Pine Biotech
🚀Crypto PRICE EXPLOSION🛑RESET? (Bo Polny)
 
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Bitcoin INDEX (LINK): https://www.gold2020forecast.com/cryptocurrency-index LEGAL & DISCLAIMER: The above represents the opinion and analysis of Mr. Polny, based on data available to him, at the time of writing. Mr. Polny's opinions are his own, and are not a recommendation or an offer to buy or sell securities, commodities and/or cryptocurrencies. Mr. Polny is an independent analyst who receives no compensation of any kind from any groups, individuals or corporations. As trading and investing in any financial markets may involve serious risk of loss, Mr. Polny recommends that you consult with a qualified investment advisor, one licensed by appropriate regulatory agencies in your legal jurisdiction and do your own due diligence and research when making any kind of a transaction with financial ramifications. Although an experienced analyst, Mr. Polny is not a Registered Securities Advisor. Therefore Mr. Polny’s opinions on the markets, stocks and commodities are his own and can not be construed as a solicitation to buy and sell securities, commodities and/or cryptocurrencies. __________________________________________ Gold 2020 Forecast, Stock Market Crash, Gold, Silver, Bo Polny, Dollar Crash, Global Stock Crash, Bubble, Bond Bubble, Hyperinflation, Gold Fever, Web bot, webbot, Clif High, Daniel’s Timeline, earthquake, Tsunami, Volcano, Bitcoin, Prophet, prophets, Litecoin, DigiByte, Etherium, Psychic, Cryptocurrencies, cryptocurrency, Blockchain, Bitcoin ETF, October 2018, Prophecy, Lois Vogel Sharp, King of Glory Ministries, Bix Weir, Mike Maloney, Greg Mannarino, The Moon, Tone Vays, TheChartGuys, DataDash, CryptosRUs, Crypto Crew University, sunny decree, AMTC, Chris Green, Christopher Green, Greg Hunter, USA Watchdog, Elliott Wave, Alessio Rastani
Views: 11343 Gold 2020 Forecast
HashFlare Cloud Mining - Is It Worth It? - Analysis Based on My Own Data (Re-Uploaded)
 
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I've gotten a lot of questions on previous videos about cloud mining and HashFlare. Is it worth it? Should you try it? Well, I decided to give it a shot and this is what I discovered... (Note: This video was re-uploaded to correct a couple errors in editing. Thanks for understanding.) Get Jambart's Excel Spreadsheet Here: http://www.investingwithjambart.com/calculators/ Join the Discord Discussion: https://goo.gl/v2wRbF Some Other Cryptocurrency Opportunities: 1. Mine for crypto using your own equipment! Join MiningPoolHub: https://miningpoolhub.com/index.php?page=register Download AwesomeMiner: http://www.awesomeminer.com/download.aspx Watch my tutorial on how to set it up: https://youtu.be/Tnw4zSD7TtA 2. Keep your Bitcoin secure with offline storage: https://goo.gl/pRvmgE Other helpful websites: Join CoinBase to purchase Bitcoin & get $10 in free BTC: https://goo.gl/wB6Z7h Great Portfolio Manager - CoinTracker.info (10% off with this link): https://goo.gl/xHWVei ---------- Tips/Donations Graciously Accepted: ETN: etnk1VBUHGA4RHxUPmc3Q43UaaoRvfero6USYBiZejkDSsvx42nBmtZAoKnux3WN7pd38qHsxVse6XQLRa4YfnmdA75CY1SB72 BTC: 1JhgmwdQ4SLjPVbvKKHg2NyRrQUmFkZMwz LTC: LdtBYEPS3xzV1iMNaeeQ6maTGX3KcxrTaR ETH: 0x1B06d92E01b33E20461a7eD379641877bb53AB20 ZEN: zndJteGckncjc5kfho8g2uzXQZQ6LSvj3ku ---------- Music from audioblocks.com: http://audioblocks.refr.cc/ZFW24KF Please like this video if you find it helpful; leave comments below to tell me what you think. Thanks! Happy investing and God bless. Disclaimer: I am not a financial adviser. Please do all of your own research. I am not responsible for any damage done to your computer. I am not responsible for decisions you make regarding how you spend your own time or money. I am simply a hobby miner who likes to share knowledge.
Views: 1498 Goose-Tech
David Abbott - Integrating data-driven and model-based analysis tools for functional MRI [2015]
 
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Lecture at Neuroinformatics 2015 in Cairns, Australia. One day special session organized by the INCF Australia Node, August 22. Session 2: Imaging informatics Talk title: Integrating data-driven and model-based analysis tools for functional MRI Speaker: David Abbott, The Florey Institute of Neuroscience and Mental Health
Views: 91 INCF
AssetWise - Risk Based Inspection Analysis and Data Management
 
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As an integrity engineer, you need to conduct risk based inspection analyses to maintain the integrity of piping and pressurized vessels. With AssetWise, you can reduce the risk of containment loss as a result of deterioration. AssetWise combines analysis with inspection data management that supports a dynamic risk-based inspection process. See more at: https://www.bentley.com/en/products/product-line/asset-reliability-software/assetwise-apm-strategy-development
Data Driven Decisions
 
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See the full course: https://goo.gl/yTEWsf Follow along with the course eBook: https://goo.gl/iqDH61 Advanced analytics brings about a more objective form of decision making, what is called data-driven decision making. The implicit premise of big data is that decisions can be made wholly based upon data and computerized models, shifting the locus of decision making from people and intuition to data and formal models. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 2052 Complexity Labs
Web-Based Analysis of Mass Spectrometry Data
 
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ImageXD | June 8, 2016 | 1:00-1:30 p.m. | 190 Doe Library, UC Berkeley Speaker: Ben Bowen, Lawrence Berkeley National Lab Incredible advances are being made in image processing techniques and tools, but the scientists who use them typically don’t have the opportunity to communicate with scientists who work on similar problems in different domains. This workshop aims to address that. At ImageXD we will gather researchers from a variety of fields who work with images as a primary source of data. Invited speakers will discuss their image processing pipelines, identifying common principles, algorithms, and tools. Throughout, we aim learn from one another while strengthening ties across disciplinary boundaries. Learn more: http://www.imagexd.org/
Classroom Stats: Fun, Flexible, Free Mobile Data Collection and Web-Based Analysis
 
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Presented by: Adam Childers and David Taylor, Roanoke College Classroom Stats is an integrated mobile and web-based data collection and analysis platform. Instructors can quickly send out questions (quantitative and categorical) through the web application that students can answer on their mobile devices and see the results analyzed in real time. Classroom Stats makes teaching and learning statistics fun and interactive as it seamlessly integrates students’ data into the classroom. Visit: http://www.classroomstats.com
Views: 2 CAUSEweb