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Search results “Opinion mining and sentiment analysis thesis outline”
Random Forest Classifier for News Articles Sentiment Analysis
 
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Introduction DATA MINING It is the process to discover the knowledge or hidden pattern form large databases. The overall goal of data mining is to extract and obtain information from databases and transfer it into an understandable format for use in future. It is used by Business intelligence organizations, Financial analysts, Marketing organizations, and companies with a strong consumer focus like retail ,financial and communication . It can also be seen as one of the core process of knowledge discovery in data base (KDD). It can be viewed as process of Knowledge Discovery in database. Data Extraction/gathering:- To collect the data from sources . Eg: data warehousing. Data cleansing :- To eliminate bogus data and errors. Feature extraction:- To extract only task relevant data : i.e to obtain the interesting attributes of data . Pattern extraction and discovery :- This step is seen as process of data mining , where one should concentrate the effort. Visualization of the data and Evaluation of results :- To create knowledge base. CLASSIFICATION Classification is a technique of data mining to classify each item into predefined set of groups or classes. The goal of classification is to accurately predict the target class for each item in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks. The simplest type of classification problem is binary classification. In binary classification, the target attribute has only two possible values: for example, high credit rating or low credit rating. Multiclass targets have more than two values: for example, low, medium, high, or unknown credit rating. SENTIMENT ANALYSIS Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic. The attitude may be his or her judgment or evaluation, affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader). With opinion mining, we can distinguish poor content from high quality content. Random Forest Technique In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample. This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 75716 Linguamatics
The Hazards of AI: Beware! | Hamidreza Keshavarz Mohammadian | TEDxTehran
 
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AI is improving every day and we found a widespread application of it in our daily lives.How deep is this influence? We did get into top gear but do we have a destination or we are going nowhere? Is this beautiful forest road going to the valley? Hamidreza Keshavarz was born in Tehran in 1983. He attended the Allameh Helli school (NODET) where he later became a teacher and head of the department. He holds a Ph.D. degree in Computer Engineering from Tarbiat Modares University. His main interest areas are data science and artificial intelligence and his thesis, entitled “Sentiment analysis based on the extraction of lexicon features” is about opinion mining on social media. He has published 12 papers and is a reviewer for international journals and conferences. He was awarded for presenting his thesis in the countrywide “Presenting your thesis in three minutes” competition. He has been in love with computers since early childhood when computers were not as widespread as today. His love with computers was intensified when he started programming at age 11. He wrote a Paintbrush program in the Assembly language at age 12, which cemented his desire to become active in this field. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Views: 439 TEDx Talks
Data Mining Paper Review
 
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Recorded with http://screencast-o-matic.com
Views: 119 venu gopal valeti
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: 691216 Kent Löfgren
Textual Analysis PowerPoint
 
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The overview and findings of my textual analysis project for COM610.
Views: 447 Nikki Edmondson
MLSA - Multi Language Sentiment Analysis
 
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JHU Information Retrieval class project. Performing sentiment analysis on ranked documents retrieved per user query on multiple languages.
Views: 42 Jorge M Ramirez
Literature Review Preparation Creating a Summary Table
 
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This video shows you exactly how to create a summary table for your research articles. It outlines what information should go in the table and provides helpful summary hints. eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" on Amazon: http://amzn.to/1hB2eBd OR the PDF: http://books.google.ca/books/about/Research_terminology_simplified.html?id=tLMRAgAAQBAJ&redir_esc=y http://youstudynursing.com/ Once you have found literature that you want to include in your review the task of summarizing it can be daunting. It is helpful to use a data extraction tool while you are reviewing each article. Then, creating a table that captures key points you need to consider for your analysis will make your summary more accurate, effective and complete. This step is so important that I get my students to do it for marks. If you are working on a literature review, trust me, you don't want to skip this step. If you do, the review will end up taking a lot longer to complete and you will be more likely to miss important information. Also, if you are working on a team these tables are absolutely essential for communication and collaboration. To set up your table, first identify the number of columns you think you will need. I usually start with seven. You can add more later if you need to, but I find it easier to remove information before publication than to add it. The headings in your table will depend on the information you need to collect, which depends on the purpose of your review. In this video I will go over the ones I recommend using as well as a few other helpful options. In the first column, always list the author and the year of publication. To make things easier, you will also want to save your articles in a folder on your hard drive by the author and year of publication. I will often also note the country that the study was conducted in. That way it is easy for me to quickly identify if more research is needed in my country specific to the topic of inquiry. You can also note the country later in the table. Discipline may also be useful to note either in the same column or a separate one if you are looking at a multi-disciplinary topic such as hand hygiene practices. It can help you identify if you need to consider looking in other areas to capture missing disciplines or if there is a lack of evidence particular to a discipline. However, if your literature review is focused on a particular discipline, such as nursing, than this information would not add anything to your table and should not be used. Remember to keep your table as concise as possible. Include the topic or focus of the study as well as the research purpose or research question in the next column. The focus of the article is absolutely critical to your summary table. Remember to be concise and specific. I also like to quote the purpose of the article here. Noting the conceptual or theoretical framework will help to inform you of the perspective the researchers are taking. You may also notice common ones that you could consider for your future research proposal. In any review it is important to note the paradigm and methods used. Typically, for first year students I only expect them to identify the paradigm as Qualitative or Quantitative. In upper years of the program and when I publish I expect a more specific identification of the methodology. Sometimes, depending on the purpose of the review, I use separate columns for the design, sampling method and data collection or analysis methods. For pragmatic reasons I still limit the total number of columns in my table to seven or eight. The context, setting and sample should also be noted. This is another location that the country that the study was conducted in can be listed. Just don't put the same information in two spots. Be concise and consistent. Whenever you are putting more than one type of information in a column make sure you are also consistent in the way and order it is listed. For example, always note the setting then the sample in this column. Use a bulleted list or separate information by paragraphs or periods. Key Findings need to be presented in a brief way. Make sure you are not simply writing everything down. What findings are of particular interest to the focus of your literature review? The more concise you are the better. Stay focused. Noting the gaps in the research will help you think about what research needs to be done. Make note of the limitations of the study you are reading as well as areas for future research. This step can be particularly useful when laying the foundation for your next research project. Many published reviews now include all or part of these summary tables. Go take a look at what has been published for more examples of how to construct your table. Music By http://instrumentalsfree.com
Views: 59267 NurseKillam
Lesson 5 Basic Python for Data Analytics Social Media & Twitter Analysis
 
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The objective of this channel is to give you an overview of pandas in analytics for business practitioners especially as Marketing/ Social Media Analyst tapping on big data: pandas is a DataFrame Framework, a library that stores data in a highly efficient spreadsheet format and functions. Efficient in: Data Structure (numpy) Computing time (since DataFrame is processed by C++, it runs in a well streamlined computing environment) Highly optimized and updated processes And I will end the sharing with some planned resources to help you learn analytics in the future. Feel free to access my github for Twitter Social Media Analysis (http://bit.ly/2koxDdZ) This is the playlist where I am going to explain step by step of this tutorial (https://youtu.be/YnMhFV8Q_K4) Hopefully by the end of this video you could be more inspired to learn analytics and follow through the journey Feel free to open my repository(contains powerpoint slides at): https://drive.google.com/drive/folders/0B7MOgjR94z_veUdHVGV4aENZSkk
Views: 1625 Vincent Tatan
Outlining sequence and format
 
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This short video discusses outlining an essay or term paper as both sequence and format.
Views: 505 James Patterson
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 6080 Clickmyproject
Using twitter to predict heart disease | Lyle Ungar | TEDxPenn
 
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Can Twitter predict heart disease? Day in and day out, we use social media, making it the center of our social lives, work lives, and private lives. Lyle Ungar reveals how our behavior on social media actually reflects aspects about our health and happiness. Lyle Ungar is a professor of Computer and Information Science and Psychology at the University of Pennsylvania and has analyzed 148 million tweets from more than 1,300 counties that represent 88 percent of the U.S. population. His published research has been focused around the area of text mining. He has published over 200 articles and holds eleven patents. His current research deals with statistical natural language processing, spectral methods, and the use of social media to understand the psychology of individuals and communities. 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: 3919 TEDx Talks
dissertation and thesis writing| MATLAB lecture 2
 
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THESIS WORK OMN MATLAB| THESIS IN CHANDIGARH| M TECH THESIS| PHD THESIS IN CHANDIGARH This is my secondlecture on MATLAB . Please like, subscribe and share my video.Assignment Statement "=" means, Equal sign has to store its right side value into its left side variable. https://thesisworkchd.files.wordpress.com/2018/01/matlab2.pdf THESIS WORK ON MATLAB We provide thesis assistance and guidance in Chandigarh with full thesis help and readymade M.Tech thesis writing in MATLAB with full documentation in Chandigarh , Delhi, Haryana, Punjab, Jalandhar, Mohali, Panchkula, Ludiana, Amritsar and nearby area’s M.Tech. students by providing a platform for knowledge sharing between our expert team. Some of the important areas in which we provide thesis assistance presently have been listed below: BIOMEDICAL BASED PROJECTS: 1. AUTOMATIC DETECTION OF GLAUCOMA IN FUNDUS IMAGES. 2. DETECTION OF BRAIN TUMOR USING MATLAB 3. LUNG CANCER DIAGNOSIS MODEL USING BNI. 4. ELECTROCARDIOGRAM (ECG) SIMULATIONN USNG MATLAB FACE RECOGNITION: 5. FACE DETECTION USING GABOR FEATURE EXTRACTION & NEURAL NETWORK 6. FACE RECOGNITION HISTOGRAM PROCESSED GUI 7. FACE RECOGNITION USING KEKRE TRANSFORM FINGERPRINT RECOGNITION: 8. MINUTIAE BASED FINGERPRINT RECOGNITION. 9. FINGERPRINT RECOGNITION USING NEURAL NETWORK RECOGNITION/ EXTRACTION/ SEGMENTATION/WATERMARKING: 10. ENGLISH CHARACTER RECOGNITION USING NEURAL NETWORK 11. NUMBER RECOGNITION USING IMAGE PROCESSING 12. CHECK NUMBER READER USING IMAGE PROCESSING 13. DETECTION OF COLOUR OF VEHICLES. 14. SEGMENTATION & EXTRACTION OF IMAGES, TEXTS, NUMBERS, OBJECTS. 15. SHAPE RECOGNITION USING MATLAB IN THE CONTEXT OF IMAGE PROCESSING 16. RETINAL BLOOD VESSEL EXTRACTION USING MATLAB 17. RECONGITION AND LOCATING A TARGET FROM A GIVEN IMAGE. 18. PHASE BASED TEMPLATE MATCHING 19. A DETECTION OF COLOUR FROM AN INPUT IMAGE 20. CAESAR CIPHER ENCRYPTION-DECRIPTION 21. IMAGE SEGMENTATION - MULTISCALE ENERGY-BASED LEVEL SETS 22. THE IMAGE MEASUREMENT TOOL USING MATLAB 23. A DIGITAL VIDEO WATERMARKING TECHNIQUE BASED ON IDENTICAL FRAME EXTRACTION IN 3-LEVEL DWT (ALSO FOR 5-LEVEL DWT) 25. RELATED TO STEGANOGRAPHY AND CRYPTOGRAPHY 26. RELATED TO THE ALL TYPES OF WATERMARKING TECHNIQUES A. TEXT WATERMARKING B. IMAGE WATERMARKING C. VIDEO WATERMARKING D. COMBINATION OF TEXT AND IMAGE WITH KEY 27. OFFLINE SIGNATURE RECOGNITION USING NEURAL NETWORKS APPROACH 28. FRUIT RECOGNITAION RELATED PROJECTS 29. VESSEL SEGMENTATION AND TRACKING 30. PROPOSED SYSTEM FOR DATA HIDING USING CRYPTOGRAPHY AND STEGANOGRAPHY 31. BASED ON IMAGE COMPRESSION ALGORITHM USING DIFFERENT TECHNIQUES 32. GRAYSCALE IMAGE DIGITAL WATERMARKING TECHNOLOGY BASED ON WAVELET ANALYSIS 33. CONTENT-BASED IMAGE RETRIEVAL 34. IMAGE PROCESSING BASED INTELLIGENT TRAFFIC CONTROLLER 35. MORPHOLOGY APPROACH IN IMAGE PROCESSING And many more……….. http://www.thesisworkchd.com/
Views: 82 Pushpraj Kaushik
Data mining in lung cancer pathologic staging diagnosis: Correlation-clinical&pathology information
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 111 Clickmyproject
SAP Twitter Analysis App
 
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With SAP HANA, we have developed a Twitter Analysis App to address and analyse some of the ‘unstructured data’ we receive from everyday tweets. So let’s see how it works.
Views: 2285 SAP Business One
Outlining and the Hierarchy of Ideas
 
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Don't forget to hit the Like and Subscribe videos to make sure you receive notifications about upcoming Literature, Grammar, Reading, Writing, and World History lessons from MrBrayman.Info. Below is the outline of the slides used in the lesson: Outlining and the Hierarchy of Ideas What’s a Hierarchy? Hierarchy (n.): The levels of rank or importance that separate individuals, ideas, or objects. Ex. The hierarchy in a family: Mom & Dad, eldest sister, younger sister, baby brother, the dog. Ex. The king, the queen, the lords, the knights, the peasants The Hierarchy of Ideas in an Essay When you write essays, follow these steps: Read and re-read the prompt to make sure that you understand the required topic(s), form, and length. Write your thesis in response to the prompt. Write out a list of sub-topics that will address the topic in the prompt and that will prove the thesis. Write your body paragraphs one-by-one. Write your introduction using your body paragraph topics for your enumeration. Write your conclusion, reworking the material from the introduction so that you say the same thing, just a different way. EDIT-EDIT-EDIT-EDIT-EDIT-EDIT-EDIT-EDIT-EDIT Lesson Completed
Views: 304 Brook Brayman
Implications of word use in outline conversation Phd dissertation of Staphan Ludwig
 
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The business implications of word use in outline conversation - This study offers suggestions for managers in managing online user – communities by assessing the online community through text – mining and specific linguistic styles.
How to easily perform text data content analysis with Excel
 
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Perform complex text analysis with ease. Automatically find unique phrase patterns within text, identify phrase and word frequency, custom latent variable frequency and definition, unique and common words within text phrases, and more. This is data mining made easy. Video Topics: 1) How to insert text content data for analysis 2) Perform qualitative content analysis on sample survey 3) Review text content phrase themes and findings within data 4) Review frequency of words and phrase patterns found within data 5) Label word and phrase patterns found within data
Views: 59926 etableutilities
Python: Extract text from PDF file using the Terminal and Tika-Python, NLTK
 
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my script for extracting data from a PDF or similar text files used it for analysis and such github: https://github.com/kriszl/textsearch
Views: 93 krisz
PDF file: Reading and Extracting data using Python
 
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This a basic program for understanding PyPDF2 module and its methods. Simple program to read data in a PDF file.
Views: 4255 P Prog
Sociology Research Methods: Crash Course Sociology #4
 
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Today we’re talking about how we actually DO sociology. Nicole explains the research method: form a question and a hypothesis, collect data, and analyze that data to contribute to our theories about society. Crash Course is made with Adobe Creative Cloud. Get a free trial here: https://www.adobe.com/creativecloud.html *** The Dress via Wired: https://www.wired.com/2015/02/science-one-agrees-color-dress/ Original: http://swiked.tumblr.com/post/112073818575/guys-please-help-me-is-this-dress-white-and *** Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark, Les Aker, Robert Kunz, William McGraw, Jeffrey Thompson, Jason A Saslow, Rizwan Kassim, Eric Prestemon, Malcolm Callis, Steve Marshall, Advait Shinde, Rachel Bright, Kyle Anderson, Ian Dundore, Tim Curwick, Ken Penttinen, Caleb Weeks, Kathrin Janßen, Nathan Taylor, Yana Leonor, Andrei Krishkevich, Brian Thomas Gossett, Chris Peters, Kathy & Tim Philip, Mayumi Maeda, Eric Kitchen, SR Foxley, Justin Zingsheim, Andrea Bareis, Moritz Schmidt, Bader AlGhamdi, Jessica Wode, Daniel Baulig, Jirat -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 342549 CrashCourse
Sketch Engine for Terminology and Translation
 
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A tool for term extraction and language analysis with focus on translators and interpreters
Views: 891 Sketch Engine
Extract Facebook Data and save as CSV
 
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Extract data from the Facebook Graph API using the facepager tool. Much easier for those of us who struggle with API keys ;) . Blog Post: http://davidsherlock.co.uk/using-facepager-find-comments-facebook-page-posts/
Views: 198021 David Sherlock
Machine Learning with Scikit-Learn - The Cancer Dataset - 32 - The Decision Function
 
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In this machine learning series I will work on the Wisconsin Breast Cancer dataset that comes with scikit-learn. I will train a few algorithms and evaluate their performance. I will use ipython (Jupyter) and the code will be available on github. The code: https://github.com/CristiVlad25/ml-sklearn/blob/master/Machine%20Learning%20with%20Scikit-Learn%20-%20The%20Cancer%20Dataset%20-%2032%20-%20The%20Decision%20Function.ipynb In this machine learning tutorial we start discussing about and looking into uncertainty estimation in scikit-learn. We use the recently trained SVM on the cancer dataset (that we built over the previous few videos) to inspect the decision function, in detail (the raw values) and more abstractly (using Boolean interrogations). This helps us better understand how our algorithm 'makes' decisions. Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad Recommended readings: 1. Andreas Müller and Sarah Guido ML book: https://www.amazon.com/dp/1449369413/ 2. Aurelien Geron: Hands-On Machine Learning with Scikit-Learn and TensorFlow: https://www.amazon.com/dp/B06XNKV5TS/
Views: 1458 Cristi Vlad
PyData Chicago 2018-01
 
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Introduction: Convolutional neural networks, or deep learning, is currently gaining lots of traction due to the ease of running models through open source APIs. In large part, this talk will focus on the 2016 Kaggle Data Science Bowl, Transforming How We Diagnose Heart Disease. Medical image processing has become a hot topic as of recently due to the complexity of the problem and the potential cost saving benefits for health care providers. In addition, this talk will broadly cover how to handle medical data in Python and how to install TensorFlow on AWS in order to run deep learning models. In this Python-based tutorial, we will walk through some techniques for handling medical data in DICOM format as well as run through a tutorial of using a TensorFlow API wrapper (Keras) for running your own deep learning models. Speaker Bio: Danny Malter is data scientist with Hyatt Hotels, working on predictive modeling used by different business units within the company. Current projects include predicting how many nights individual guests will stay in the future and customizing email marketing content for better guest experiences. Prior to Hyatt, Danny worked three years for Cengage Learning, focusing on web analytics, data analysis, and data visualization. He holds an MS from DePaul University in Predictive Analytics and focused on computations and machine learning models. On the side, you can usually find him playing around with baseball data. Below are links to the content. The last three files (HTML files) need to be downloaded to be viewed. Slides: https://docs.google.com/presentation/d/19h8K4BruPO_X4kxW6lPwaQSkoEC_qfXrTKPQhwKbNDM/edit?usp=sharing DICOM Data Demo: https://drive.google.com/file/d/1ynIdx3JJ7Tm3rM9cVLUL12ahg_nsZdBK/view?usp=sharing Keras Tutorial: https://drive.google.com/file/d/1ePQxYH7PTXAYBfUIQgLmfxlIgPxl-10o/view?usp=sharing Image Similarity: https://drive.google.com/file/d/1WuHf8BnXeuOH52GkrPw_NawT3y5HlHnI/view?usp=sharing
Views: 280 Ji Dong
Intelligent Exploratory Text Editing
 
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Writing is an iterative process comprising exploratory, drafting, revising, and editing stages. This technology assists writers by doing most of the hard work during the exploratory stage. Relevant papers in the literature are automatically discovered and presented to the writers in visually-appealing forms to aid the exploration process.
Views: 254 Wilson Wong
Summary to Datasets
 
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Summary to Datasets
Views: 1350 Social Networks
Mining Your Logs - Gaining Insight Through Visualization
 
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Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 25287 GoogleTechTalks
Ep. 1: Finding a Research Direction
 
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Recommended Citation: Tracy, S. J. [Get Your Qual On]. (2016). Making a difference with qualitative research [Video file]. Retrieved from https://youtu.be/tLu5IifevxM. For more information in formal text, see: Tracy, S. J. (2012). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. Hoboken, NJ: Wiley-Blackwell. Whenever you’re taking a qualitative methods course or embarking on a research project, one of the first questions often is, “What is my project going to be about?” “What am I going to study for the semester?” So oftentimes for the first couple assignments, you’re asked to come up with a ‘Research Topic.’ However, I’m here to tell you that that is not a great way to start! Because “topic” is so broad. You don’t want to do “topic” because it doesn’t tell you what’s interesting or important. So my recommendation instead is that you start with a ‘Research Problem’ -- and this is a shout-out to Stan Deetz who really was the one who brought this to my attention when I was a graduate student. So rather than starting like, for me, with this broad topic of “Emotion in Organizations,” I might start instead with a specific problem, like some people are being emotionally abused in an organization, or some people are burned out or stressed out, or some people are feeling like they have to fake their emotions and that is alienating themselves to other facets of themselves that aren’t the organizationally prescribed one. So when you start with a problem, the cool thing is is that you already have a rationale built in because if your research ends up shedding light on that issue, then you at least have some practice significance. That is a main thing that qualitative research can do is draw practical significance and help create organizations, institutions, families and so on so that they are flourishing and not just surviving. The other thing that I encourage folks to ask themselves is this question here: “How did you come to be[come] curious about [that topic]?” (Is that what I said? “How did you come to be curious about that?” So I need to learn how to read and do videos at the same time!) If you ask yourself how is it that you became curious, you get at what you are super passionate about. And we know that with any type of research when you do it well, systematically, there’s going to become times that it becomes boring and overwhelming and that you’re going to have to keep going even when it is super tough. So if you ask yourself from the get-go, “Why is it that I became interested in this topic?” you’ll figure out the angle that you’re most interested in. So if I say, “Why is it that I became interested in emotions and organizations?” I can go back to situations, for instance when I worked on the cruise ship and I felt like I was not able to express the entire set of facets of Sarah Tracy. I was only allowed to be the happy, excited person. Even when my grandmother died, I wasn’t allowed to grieve. By helping me know why I’m curious about it that helps drive the research design and it helps me realize, “Okay, in future research, I need to observe situations where maybe people are asked to maintain a sense of self that is different than other important senses of themselves,” or I need to ask interview questions where I say, “Are there times when you feel like you have a facade on? And if you’re wearing that facade, how is it that you find other ways to express other facets of yourself?” And so that really helps kind of narrow. So that’s it for this little video on “Finding a Research Direction.” Thanks!
Views: 730 Get Your Qual On
More Data Mining with Weka (5.1: Simple neural networks)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 1: Simple neural networks http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 22031 WekaMOOC
Using your dissertation or thesis to market yourself
 
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In this video Dr. Ziene Mottiar, DIT speaks to Alex Gibson whose area of interest is Social Media and Peter Lewis from the Careers Office in DIT, about how to use your dissertation or thesis research to promote yourself and aid your career development. They discuss the use of social media as well as how to present your research work in a CV and interview.
Views: 388 ZieneMottiar
Importing gene symbol data into Excel correctly
 
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Microsoft Excel (and some other spreadsheet programs) used with default settings automatically converts some gene symbols from text format into a date, such as the SEPT* and MARCH* gene symbols. This video shows how to import gene symbol data (CSV or tab separated) into Excel so that you can avoid introducing these errors. The video uses Excel 2016 for Mac but the same procedure can be done on Windows versions and previous Excel versions. View in 720P
SPSS Questionnaire/Survey Data Entry - Part 1
 
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How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 501015 Quantitative Specialists
My Master Thesis Presentation and Defense
 
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The presentation was made using "Keynote"
Views: 233570 Adham Elshahabi
WEKA MEDICAL DATA ANALYSES
 
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WEKA MEDICAL DATA ALALYSYS
Visual Analysis of Historic Hotel Visitation Patterns
 
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This video describes an interactive visual tool for exploring the visitation patterns of guests at two hotels in central Pennsylvania from 1894 to 1900. It is implemented as a coordinated multiple view visualization in Improvise, a a desktop application developed by Chris Weaver for building and browsing visual interfaces that perform highly interactive querying of multidimensional data sets. To read a full paper about this work, see: http://www.cs.ou.edu/~weaver/academic/publications/weaver-2007b.pdf For more about Improvise, visit http://www.cs.ou.edu/~weaver/improvise/index.html Please cite as: Chris Weaver, David Fyfe, Anthony Robinson, Deryck Holdsworth, Donna Peuquet, Alan M. MacEachren 2006. Visual Analysis of Historic Hotel Visitation Patterns, Video Posted on Youtube, Sept 24, 2010 (produced to accompany C. Weaver, D. Fyfe, A.C. Robinson, D. Holdsworth, D. Peuquet, A.M. MacEachren, "Visual Analysis of Historic Hotel Visitation Patterns," IEEE Symposium on Visual Analytics Science and Technology 2006, Baltimore, MD, pp. 35-42 2006.)
Views: 929 GeoVISTACenter
Advanced Data Mining with Weka (4.3: Using Naive Bayes and JRip)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 3: Using Naive Bayes and JRip http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3933 WekaMOOC
Downloading and installing MAXQDA
 
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To join online SPSS Foundation Training at just $10 click here: https://www.udemy.com/spss-statistics... Syllabus: Unit 1: Developing the familiarity with SPSS Processer Entering data in SPSS editor. Solving the compatibility issues with different types of file. Inserting and defining variables and cases. Managing fonts and labels. Data screening and cleaning. Missing Value Analysis. Sorting, Transposing, Restructuring, Splitting, and Merging. Compute & Recode functions. Visual Binning & Optimal Binning. Research with SPSS (random number generation). Unit 2: Working with descriptive statistics Frequency tables, Using frequency tables for analyzing qualitative data, Explore, Graphical representation of statistical data: histogram (simple vs. clustered), boxplot, line charts, scattorplot (simple, grouped, matrix, drop-line), P-P plots, Q-Q plots, Addressing conditionalities and errors, computing standard scores using SPSS, reporting the descriptive output in APA format. Unit 3: Hypothesis Testing Sample & Population, concept of confidence interval, Testing normality assumption in SPSS, Testing for Skewness and Kurtosis, Kolmogorov–Smirnov test, Test for outliers: Mahalanobis Test, Dealing with the non-normal data, testing for homoscedasticity (Levene’s test) and multicollinearity. Unit 4: Testing the differences between group means t – test (one sample, independent- sample, paired sample), ANOVA-GLM 1 (one way), Post-hoc analysis, Reporting the output in APA format. Unit 5: Correlational Analysis Data entry for correlational analysis, Choice of a suitable correlational coefficient: non-parametric correlation (Kendall’s tau), Parametric correlation (Pearson’s, Spearman’s), Special correlation (Biserial, Point-biserial), Partial and Distance Correlation Unit 6: Regression (Linear & Multiple) The method of Least Squares, Linear modeling, Assessing the goodness of fit, Simple regression, Multiple regression (sum of squares, R and R2 , hierarchical, step-wise), Choosing a method based on your research objectives, checking the accuracy of regression model. Unit 7: Logistic regression, Choosing method (Enter, forward, backward) & covariates, choosing contrast and reference (indicator, Helmert and others), predicted values: probabilities & group membership, Influence statistics: Cook, Leverage values, DfBetas, Residuals (unstandardized, logit, studentized, standardized, devaince), Statics and plot: classification, Hosmer-Lemeshow goodness-of-fit, performing bootstrap, Choosing the right block, interpreting -2loglikelihood, Omnibus test, interpreting contingence and classification table, interpreting Wald statistics and odd ratios. Reporting the output in APA format Unit 8: Non-parametric tests When to use, Assumptions, Comparing two independent conditions (Wilcoxon rank-sum test, Mann-Whitney test), Several independent groups (Kruskal- Wallis test), Comparing two related conditions (Wilcoxon signed-rank test), Several related groups (Friedman’s anova), Post-hoc analysis in non-parametric analysis. Categorical testing: Pearson’s Chi-square test, Fisher’s exact test, Likelihood ratio, Yates’ correction, Loglinear Analysis. Reporting the output in APA format. Unit 9: Factor Analysis Theoretical foundations of factor analysis, Exploratory and Confirmatory factor analysis, testing data sufficiency for EFA & CFA, Principal component Analysis, Factor rotation, factor extraction, using factor analysis for test construction, Interpreting the SPSS output: KMO & Bartlett’s test, initial solutions, correlation matrix, anti-image, explaining the total variance, communalities, eigen-values, scree plot, rotated component matrix, component transformation matrix, factor naming Unit 10: Structural Equation Modelling using IBM AMOS Getting familiar with AMOS graphics, defining the variables-endogeneous, exogeneous, residual; Model building, Meeting the assumptions of SEM, Dealing with the non-normal data, Bootstrapping, detecting the outliers-Mahalanobis Distance Mediation Analysis, Indirect and Direct Effects, Testing the EFA model for surveys and tests, Explaining the model-p values, estimates, standard error, critical ratio, Understanding the indices of model fit- chi square, relative chi square, GFI, AGFI, PGFI, SRMR, NFI, TLI, CFI, RMSEA; Registration: For registration in classroom course and any other details contact [email protected] Program date: Summer batch: Second week of May Winter Batch: Second week of December Fee for classroom course/person: $100 if registered 3 months advance for both batch else $ 250. Group discount available.
Views: 288 Heurexler Research
Tour Of New Features In Newly Released DTube 0.6 📺 (The Cryptoverse)
 
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Follow me on Steemit and earn crypto for your best comments: https://steemit.com/@marketingmonk The new and improved DTube, version 0.6 has just been launched so here is an easy to digest video tour of all the new features. https://about.d.tube/ So before we begin, let me not make any assumptions here, DTube is a decentralised rival for YouTube built on the Steem blockchain and IPFS technology. The combination of these two technologies means neither the data nor the video files are centrally hosted. This creates a one of a kind platform that allows people to earn cryptocurrency rewards, is resistant to censorship, has no ads AND is entirely open source. You find out more by going to about.d.tube but for now let’s begin the tour of all the new features in version 0.6. Also note that this is simply the video version of the official DTube 0.6 announcement post on Steemit. My thinking was that most people won’t be bothered to read it and DTube will not get enough appreciation. As a video, many more people will get the information and DTube will get the recognition it so dearly deserves. https://steemit.com/dtube/@heimindanger/d-tube-0-6-pushing-it-to-the-limit Growing By A Video A Minute At time of recording D.Tube has grown to the point where a new video is uploaded every minute! That gives you some perspective and destroys any notion that DTube is some theoretical project that is only being used by a handful of people. More Encoding Power For DTube to be usable by the average user they should have to put in as little effort as possible. That means the DTube system needs to take whatever video file you give it and then automatically process it behind the scenes into a format that everyone is able to view. DTube has grown so much since v0.5, 2 more dedicated servers have been brought online, just for encoding the videos. New Logo So now we have this new branding which is much better and much clearer. New Media Kit https://about.d.tube/mediakit.html Following on from the new logo, this forms part of the new DTube media kit, which was one of the most popular requests. This allows the DTube brand to appear consistently when people use it within their videos as well as opens up the possibility for you to create your own t-shirts, mugs and all other kind of merchandise bearing the DTube logo. Improved Loading Logic In this version of DTube when you click on a video the player will scan the IPFS network to find the copy of the file that has the fastest connection to you and then load it from there. This not only means the video will start playing faster when you first click on it, it should also play smoother and without interruptions. The video player is now also using a full custom design with a proper settings menu where you can change the speed at which the video plays. This is also where new settings can be added in future updates. Hotkeys The player now also responds to keyboard commands. Press space to play and pause the video. Press left and right arrow keys to go back 5 seconds or forward 5 seconds. If you press a number such as 5, it will jump to the 5 minute point. Frame Thumbnail Preview You’ll find another feature when it comes to scrubbing the timeline. When you hover your mouse of the timeline, you now get a thumbnail preview so you can find particular part of the video. Thumbnail Resizing We spoke earlier on about how DTube takes in all kind of different video files and then processed them into a universal format everyone can view. Well, DTube is now also doing that for pictures. Now, no matter if someone uploads a picture straight from their digital camera, DTube will resize the image to a small thumbnail so it loads super fast for the viewers but without the person uploading having to put in any effort. More than that though, DTube now only loads thumbnails that are in view. You can see this in action by visiting the hot videos page. Discovery One big drawback of DTube up until now was that unless you were already a super popular creator, you wouldn’t get in the hot or trending actions and thus would never be discovered. In this new version of DTube when you open up the side menu you will see there are now dedicated pages for, hot, trending and new videos. But there’s more. These pages are now infinitely scrollable, making it much more likely for smaller creators to get their videos found. Tag Browsing And speaking of discovering stuff… You can now browse Dtube by tags. Let’s say if we click on a video, we watched it, we liked it and wanted more of the same… You’ll notice that the tags are now listed right under the video player. So I just click on one of these and boom… I end up on another infinitely scrolling page full of videos that have this tag. And finally… if you need help with anything DTube related people use the DTube discord channel because that is now the official hub of the DTube community: https://discord.gg/dtube
Views: 4560 The Cryptoverse
IDEA 10: Passport
 
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IDEA 10: Passport Hello. Welcome to this video about CaseWare IDEA Passport. This video is brought to you by CaseWare Analytics. One of our goals at CaseWare is to help our clients maximize the return on their investment in IDEA through continuing education. CaseWare Passport is a new feature in IDEA version 10 which provides a single point of access for valuable IDEA resources, both within the CaseWare Analytics Support Portal as well as on partner sites. These resources include the Marketplace where you have access to numerous apps to plug into IDEA through the SmartAnalyzer interface, hundreds of custom functions and scripts you can download for free, comprehensive documentation, videos to support your learning and forums where you can talk to other IDEA users. In addition, there is a link to the main Support Portal where you also have access to premium audit content made available by AuditNet. AuditNet is a vast digital network where auditors can share resources, tools and experiences including audit work programs and other audit documentation. All of these resources can be downloaded and used for free as part of your maintenance and support agreement. Downloadable IDEA resources are all contained in the Downloads section of the support portal. Here you can find Documentation including Installation Guides, a large collection of Custom Functions, and plugins for different import components, including an ACL import component which allows you to import ACL files directly into IDEA. Documentation also includes handy resources such as the Guidelines for Requesting Data from Data Sources as well as the more comprehensive Practical Guide to Obtaining Data for Auditors.Both these PDF guides contain tips to help you get the data you need for a successful audit and can be saved for reference with the click of a button. In the same area is the library of Custom Functions for IDEA. There are dozens of functions available for download here and installation is as simple as copying the downloaded file to the Custom Functions.ILB folder in your IDEA project. Once the function is in that folder it will appear in the Custom Functions list in the Equation Editor. CaseWare Analytics also has a significant collection of IDEAScripts available for your use. Like the Custom Functions, installation of a downloaded script is as simple as copying the file to the Macros folder in your Project Library. Then the script can be opened and edited in the IDEAScript editor. All scripts are available in both ASCII and Unicode formats. In addition to the scripts themselves, there are other resources available to help you learn to master this powerful tool.The IDEAScript Documentation and Resources tab contains several best practice guides, a document which outlines command line parameters, and a list of error codes. In addition to this extensive library of print resources, there is also a video library. The Videos tab contains a variety of videos illustrating how to perform tasks in IDEA as well as a collection of print based tutorials. There are 19 IDEA Version 10 videos which follow the material in the printed IDEA Tutorial. Use them together to get up and running fast with IDEA. There are also other free video collections including a series dealing with Report Reader and assorted webinars. CaseWare also has a certification program to prove you’re a skilled IDEA user. If you are interested in pursuing certification, there is information about CaseWare’s Certified IDEA Data Analyst and Certified IDEAScript Expert designations on the certification tab. If after all this, you still need some support, you can contact us though any of these channels. Remember to check back frequently because new content is added regularly. SUBSCRIBE: http://bit.ly/29BfMuV About CaseWare Analytics: CaseWare Analytics is home to IDEA® Data Analysis and CaseWare Monitor. Our software solutions are built on a foundation of industry best practices and domain expertise enabling audit, compliance and finance professionals to assess risk, accumulate audit evidence, uncover trends, identify issues and provide the necessary intelligence to make informed decisions, ensure compliance and improve business processes. These resources include the Marketplace where you have access to numerous apps to plug into IDEA through the SmartAnalyzer interface, hundreds of custom functions and scripts you can download for free, comprehensive documentation, videos to support your learning and forums where you can talk to other IDEA users. WEBSITE: http://bit.ly/1fbul6J BLOG: http://bit.ly/1i7s1vu TWITTER: http://bit.ly/29BTBan
Views: 2488 CaseWare Analytics
Iceberg: a fast table format for S3
 
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Netflix’s Big Data Platform team manages data warehouse in Amazon S3 with over 60 petabytes of data and writes hundreds of terabytes of data every day. With a data warehouse at this scale, it is a constant challenge to keep improving performance. This talk will focus on Iceberg, a new table metadata format that is designed for managing huge tables backed by S3 storage. Iceberg decreases job planning time from minutes to under a second, while also isolating reads from writes to guarantee jobs always use consistent table snapshots. In this session, you'll learn: • Some background about big data at Netflix • Why Iceberg is needed and the drawbacks of the current tables used by Spark and Hive • How Iceberg maintains table metadata to make queries fast and reliable • The benefits of Iceberg's design and how it is changing the way Netflix manages its data warehouse • How you can get started using Iceberg
Views: 677 DataWorks Summit
RENT - Look Pretty and Do As Little as Possible: A Video Essay
 
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RENT is terrible and I hate it. https://www.patreon.com/loosecanon
Views: 1139104 Lindsay Ellis
How To... Calculate Pearson's Correlation Coefficient (r) by Hand
 
09:26
Step-by-step instructions for calculating the correlation coefficient (r) for sample data, to determine in there is a relationship between two variables.
Views: 424339 Eugene O'Loughlin
Sequence Extraction using bioedit Part 2
 
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Demonstration of sequence extraction using bioedit. Includes filtering sequnces based on keyword or substring in sequence, filtering smaller or lrger than a certain Sequence length,
Views: 501 rsingh1980
Get Hired Faster After Getting a Personalized Resume Analysis
 
02:06
What is standing in your way of getting that interview for your dream job? You guessed it, your resume! The resume is the single most important document in your job hunting repertoire. Without a resume that accurately portrays your applicable skills and experience, you have zero chance of landing that job. As a hiring manager for Business Analysts, I have reviewed hundreds of resumes. I will be the first to tell you, I have likely disqualified many outstanding Business Analysts because their resumes did a poor job in telling me they were great. Let me help you to ensure you are not making the same mistakes! Check out this link to learn more about the Personalized Resume Analysis course. http://thebaguide.teachable.com/courses/personalized-resume-analysis ------------------------------------------------------ The BA Guide provides practical, real-world coaching and training for both current and aspiring Business Analysts. Subscribe to the channel and grow your skills to new heights! Website ► www.TheBAGuide.com Twitter ► https://twitter.com/TheBAGuide Facebook ► https://www.facebook.com/jeremy.aschenbrenner.1 LinkedIn► https://www.linkedin.com/groups/8484505
Find themes and analyze text in NVivo 9 | NVivo Tutorial Video
 
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Learn how to use NVivo's text analysis features to help you identify themes and explore the use of language in your project. For more information about NVivo visit: http://bit.ly/sQbS3m
Views: 102937 NVivo by QSR

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