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Liu Shiwen's Serve Strategies Based on Data Analysis 数据分析刘诗雯的发球战术
 
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This is a video that demonstrate the match play between Liu Shiwen and Miu Hirano. Explain the serve strategies Liu Shiwen used based on the data analysis. Good luck with your table tennis. https://youtu.be/A5mb97lp8GA Subscribe to my YouTube channel to watch more lessons. https://www.youtube.com/channel/UC10OPVuU4Ttsu1a9lW5r4Sg [Contact ] --Email : [email protected] —Instagram: yangyangtt_99
Views: 7737 yangyang TT
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: 749710 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: 166790 TEDx Talks
STEAG`s Predictive Maintenance Techniques Based on Data Analysis "GPS for Energy Data"
 
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Welcome to the first part of a series consisting of 4 clips of Dennis Braun about state-of-the-art IT solutions, tried and tested in hundreds of real world energy projects. You will learn how we approach the subject and share tried-and-tested procedures to create real and significant value. This series combines the best of German Engineering: facts, data and experience. The series will cover in the three following episodes: 1. Why is marginally improving a coal fired power plant such a humongous lever. Also we will examine a thermodynamical backed approach to evaluate a power plant condenser. 2. What are KPI. How do I use them to optimize maintenance of wind turbines. And what is a HQ-KPI vs. BigData 3. Fun with flags - anemometers, failures and experiences STEAG Energy Services` intelligent IT solutions for power plant operators: You’re not just operating a power plant, you’re running a business. In order to compete on a global stage, you have to continuously optimize efficiency and availability. The software systems used to operate them must be equally advanced. That’s why our software engineers are working nonstop to develop highly specialized solutions to meet these needs. We are constantly looking ahead to advance innovative technologies for operational planning, analysis, monitoring, optimization, and maintenance as well as for simulation and documentation. These efforts have led to cross-industry solutions that guarantee transparency and are designed to help operators continuously improve workflows and safety. If you like it make sure to follow our channel and we will be happy to have a discussion with you in the comment section. For more information please visit https://www.steag-systemtechnologies.com/en/ and follow youtube.com/c/STEAGGmbH https://www.linkedin.com/in/dennis-braun-mecheng/ https://www.linkedin.com/company/steag-energy-services-gmbh
Views: 143 STEAG GmbH
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: 3454 Bioinformatics DotCa
Predicting Stock Prices - Learn Python for Data Science #4
 
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In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 573083 Siraj Raval
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: 768629 Dr Nic's Maths and Stats
R Data Analysis Projects: Introducing Content-Based Recommendation| packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2Gi1Gzx]. Content-based methods rely on the product properties to create recommendations, they can ignore the user preferences, to begin with. Content-based method dishes out the Needed recommendation and user profile can be built in the background. With a sufficient user profile, content-based methods can be further improved or can move on to using collaborative filtering methods. The content-based filtering method provides a list of top N recommendations based on some similarity scores. • Look at the content-based recommendation system example For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 440 Packt Video
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: 902693 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: 817 Tableau Software
Path analysis with AMOS based on summary data (correlations, means, and sd's - new)
 
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This video provides a demonstration of how to input matrix summary data (correlations, means, and sd's) into AMOS in order to carry out path analysis. The example is based on summary data provided by Stage, Carter, and Nora (2004) in their article demonstrating path analysis. You can obtain a copy of the file I created in SPSS here: https://drive.google.com/open?id=1LxUDJpSCorvyD3TZuf-Qskym_lKw6ckU Obtain a copy of the .amw file here: https://drive.google.com/open?id=1QiSfQTf25KvYAdlxXjZOxxFHEyfpdhiK Go here for other links to videos and Powerpoints on SEM with AMOS: https://sites.google.com/view/statistics-for-the-real-world/contents/structural-equation-modeling
Views: 253 Mike Crowson
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: 11365 RavenPack
Webinar: Sentiment Analysis: Deep Learning, Machine Learning, Lexicon Based?
 
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“Great movie with a nice story!” What do you think, did the person like the film or hate it? Most of the time it’s easy for us to decide whether the message of a text is positive or negative. But what if you wanted to automate the process of understanding the sentiment? For example, if you have a lot of customers leaving comments, or people publishing movie reviews, you will want to discern the sentiment and find out who is posting positive or negative messages. Sentiment analysis is an important piece of many data analytics use cases. Whether it processes customer feedback, movie reviews, or tweets, sentiment scores often contribute an important piece to describing the whole scenario. These are just some examples of a long list of use cases for sentiment analysis, which includes social media analysis, 360 degree customer views, customer intelligence, competitive analysis and many more. To avoid doing this manually, we apply sentiment analysis and teach an algorithm to understand text and extract the sentiment using Natural Language Processing. The slides for this webinar are available at https://www.slideshare.net/KNIMESlides/sentiment-analysis-with-knime-analytics-platform
Views: 1532 KNIMETV
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: 6338 MyStudy
43| Mathematica Rule Based Programming - Data Analysis and Visualization
 
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Data Analysis and Visualization is a Rich-full series Directed to all students interested in Analyzing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesiers in University of Western Australia, and Contains the main flowing Topics: 1 Data Visualization in Excel 2 Array Formula in Excel 3 2D Array Formula in Excel 4 Excel Macros 5 Why Matlab 6 Problem Solving in MATLAB 7 MATLAB Orientation - Data Types and Expressions 8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions 9 Matrices in MATLAB 10 MATLAB Scripts and Functions 11 Random Numbers, Gaussian Random Numbers, Complex Numbers 12 An Examples of Script and Function Files 13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure, 14 MATLAB Loops, Nested Loops, Repetition, while, For, 15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions, 16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence, 17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all, 18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets, 19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array 20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot 21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot, 22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots, 23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid, 24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp, 25 Arrays vs. Matrix Operations, 26 Dot Products, Example Calculating Center of Mass, Center of Gravity, 27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products, 28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors 29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws, 30 Symbolic Differentiation, sym, syms, diff 31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference, 32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule, 33 Monte Carlo Integration, 34 Introduction to ODE in System Biology 35 Introduction to System Biology, Gene Circuits, 36 Solving ODEs in Matlab, Repressilator, Programming steps 37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional, 38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres, 39 Introduction to Mathematica, 40 Programming in Mathematica 41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica 42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters, 43 Mathematica Rule Based Programming, Functional Programming, 44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules, 45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle, 46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation, 47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,
Views: 100 TO Courses
Maine-Based Lesson: Data Analysis
 
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How does an organization like the Maine Department of Inland Fisheries and Wildlife use “big data”? This video accompanies ECS Unit 5: Computing and Data Analysis. A written lesson plan incorporating this video can be found at: https://drive.google.com/open?id=0B0cIgYrLkHEfaGZOYWo3cG1sUUE
Timo Boll's Serve Strategies VS Ma Long Based on Data Analysis 数据分析波尔对抗马龙的发球战术
 
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This is a video that demonstrate the match play between Timo Boll and Ma Long. Explain the serve strategies Timo Boll used based on the data analysis. Good luck with your table tennis. Subscribe to my YouTube channel to watch more lessons. https://www.youtube.com/channel/UC10OPVuU4Ttsu1a9lW5r4Sg [Contact ] --Email : [email protected] —Instagram: yangyangtt_99
Views: 3644 yangyang TT
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: 2869073 Tuts+ Computer Skills
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: 169 KAUSTUV VARUN
James Powell: Building Web-based Analysis & Simulation Platforms | PyData London 2017
 
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Titles - Building Web-based Analysis & Simulation Platforms with React/Redux, Flask, Celery, Bokeh, and Numpy 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. __ 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. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 13606 PyData
Business Data Analysis with Excel
 
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Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required! Dave will cover the following during the presentation: • The types of business data and why business data is a unique analytical challenge. • Requirements for robust business data analysis. • Using histograms, running records, and process behavior charts to analyze business data. • The rules of trend analysis. • How to properly compare business data across time, organizations, geographies, etc.Where you can learn more about the tools and techniques. *Excel spreadsheets can be found here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Business%20Data%20Analysis%20with%20Excel **Find out more about David here: https://www.meetup.com/data-science-dojo/events/236198327/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz7sf0 Watch the latest video tutorials here: https://hubs.ly/H0hz8rL0 See what our past attendees are saying here: https://hubs.ly/H0hz7ts0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 50292 Data Science Dojo
Creating a Highlight Video in INTERACT based on data analysis (2)
 
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This video shows how a highlight video is created in INTERACT, based on events generated by data analysis. INTERACT is the platform for Synchronized Viewing and Analysis of Video Footage and Audio Files in Observational Research. It allows for Content Coding and Event Logging and creates valuable Qualitative and Quantitative results. It is based on 25 years of proven technology and based on the knowledge of thousands of researchers worldwide. INTERACT enables accelerated answers to complex research questions, because it brings video, audio, physiology and live observations together, all in one single software tool. #mangoldinternational #mangoldsoftware #mangoldinteract #eyetracking #eyetracker https://www.mangold-international.com/en/products/software/behavior-research-with-mangold-interact
How to tabulate, analyze, and prepare graph from Likert Scale questionnaire data using Ms Excel.
 
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This video describes the procedure of tabulating and analyzing the likert scale survey data using Microsoft Excel. This video also explains how to prepare graph from the tabulated data. Photo courtesy: http://littlevisuals.co/
Views: 119634 Edifo
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: 9502 ExcelIsFun
Analysis of Metagenomic Data: Marker Gene Based Analysis
 
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This is the second module in the 2016 Analysis of Metagenomic Data workshop hosted by the Canadian Bioinformatics Workshops. This lecture is by William Hsiao from the BC CDC. Lecture materials and tutorials can be found at bioinformatics-ca.github.io/analysis_of_metagenomic_data_2016/. 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: 1322 Bioinformatics DotCa
SWAN: CERN's Jupyter-based interactive data analysis service - Diogo Castro (CERN)
 
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Both CERN and high energy physics (HEP) in general face unprecedented challenges in data storage, processing, and analysis. The experiments of the Large Hadron Collider (LHC) are expected to reach one exabyte of physics data this year. After processing and filtering this data, interactivity takes particular importance in the last phases of analysis, where the final results are produced, namely in the form of plots. Jupyter’s ability to provide notebooks that merge a rich narrative made of code, text, and other media materials allows CERN to offer a web-based service that addresses the needs of the community. This service, called SWAN (an acronym for service for web-based analysis), provides the HEP community with an interactive interface to access data analysis tools, such as the ROOT framework. Moreover, SWAN integrates with CERN’s infrastructure more precisely, with users’ synchronized storage (CERNBox), computing resources, and experiments data and software. Diogo Castro offers an overview of SWAN and explains how the service is being used by researchers and students, both inside and outside CERN. Diogo also discusses the evolution of the service, especially the new SWAN interface, developed on top of Jupyter, which enables both easy sharing among users and connecting to Spark clusters. Subscribe to O'Reilly on YouTube: http://goo.gl/n3QSYi Follow O'Reilly on: Twitter: http://twitter.com/oreillymedia Facebook: http://facebook.com/OReilly Instagram: https://www.instagram.com/oreillymedia LinkedIn: https://www.linkedin.com/company-beta/8459/
Views: 327 O'Reilly
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: 12208 Pine Biotech
IBPS PO MAINS 2018-Strategy to Crack Data Analysis and Interpretation Complete Planning
 
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Please watch: "Data Interpretation Questions-223 (Double Pie Diagram) SBI PO/Clerk IBPS PO #Amar Sir" https://www.youtube.com/watch?v=0SB2zH-Z5k4 --~-- BANK/SSC/RAILWAY EXAMS Vision and Planning IBPS PO MAINS 2018-Strategy to Crack Data Analysis and Interpretation Complete Planning #Amar Sir “Math Dikhta Hai”. All the best! Let us watch: “IBPS PO MAINS 2018-Strategy to Crack Data Analysis and Interpretation Complete Planning”: https://youtu.be/aO5vh--rjU8 Our aim is to prepare students at their own, particularly the poors. So all the videos are free. But those who are able and wish to contribute to run this campaign smoothly may do the same. My mobile number 9931134475 is the Paytm and BHIM number. AMAR KUMAR SINGH SBI A/c 11009037430, IFSC-SBIN0000096, Main Branch, Jamshedpur. Our time of posting of videos on You Tube: 10:30 AM and 3:30 PM every day Join on Face Book: Chanakya Career Academy: https://www.facebook.com/groups/1441520242794598/ Amar Sir's Math Tricks: Quicker Method: Just in few seconds: Without using pen and paper: SBI PO/ Clerk/ IBPS PO/ Clerk/ SSC CGL/ Railway/ RRB/ LIC/ NDA/ CDS/ IAS/ JPSC/ BPSC: By Amar Sir having an experience of 23 years of teaching: Director of Chanakya Career Academy Pvt Ltd Sakchi Office:71-Pennar Road, Darbhanga Diary-1st Floor, Opposite Indian Overseas Bank, Sakchi, Jamshedpur-831001, Jharkhand. Bistupur Office: 1st Floor, Tewary Bechar, Main Road, Above City Style, Bistupur, Jamshedpur, Jharkhand. Office Contact:0657-220596/9570880011
Views: 12450 Amar Sir
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: 11598 TeachExcel
Object-Based Image Analysis
 
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Keith Pelletier, UMN Remote Sensing and Geospatial Analysis Laboratory The geospatial community is experiencing a data-rich era where Earth-observing platforms are capturing the landscape at fine-scale spatial and temporal resolutions. These remotely-sensed data provide a view from above that is essential for analyzing natural and anthropogenic interactions over large areas. Traditional approaches to these analyses are time and labor-intensive or limited by per-pixel techniques that fail to incorporate contextual cues. Object-based image analysis (OBIA) allows researchers and decision managers to integrate data from disparate sources at multiple scales and employ color, shape, and context for creating meaningful information. In this presentation, examples from mapping terrain, vegetation and urban infrastructure are used for illustrating data integration and analysis using OBIA. More information: https://uspatial.umn.edu/brownbag
Views: 41521 U-Spatial
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: 64 Simin Zhai
IBPS PO Mains 2018 Memory Based Paper: Data Interpretation & Analysis Section
 
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IBPS PO Mains 2018 Memory Based Paper: Data Interpretation & Analysis Section | Download Now https://www.bankersadda.com/2018/11/ibps-po-mains-2018-memory-based-paper-29.html To Know More about IBPS PO 2018 exam, pattern, syllabus & exam date, visit: http://www.bankersadda.com/p/ibps-po.html To Know More about Ssc cgl exam pattern syllabus and exam date visit http://www.sscadda.com/p/ssc-cgl.html To Know More about Teaching Job exam pattern syllabus and exam date , visit ::;https://www.teachersadda.co.in/ Aspirants, Adda247 is Now In Telegram, Join Our Telegram Group [email protected] https://t.me/adda247youtube For All the Updates and Notifications. Hurry!! Join Now Official Telegram Channel of Bankersadda - https://t.me/bankersadda_Official Join Now Official Telegram Channel of SSC Adda - https://t.me/sscadda_official PLAYLIST FOR BANK AND SSC EXAMS - https://www.youtube.com/channel/UC1L2JoMpcY6MRLhFd3gg5Xg/playlists Adda247 Youtube channel is India's most popular channel for Online Coaching for IBPS Bank PO Exams and Online Coaching for SSC CGL. 1.To buy Banking & SSC Latest Pattern Video Courses of Adda247 - at Online Streaming, SD Card or Tablet click here - https://store.adda247.com/#!/videos/list To Buy Adda247 Test Series Click Here - https://store.adda247.com/#!/testseries/list To Buy Adda247 Books Click Here -https://store.adda247.com/#!/books/list 2. Download Adda247 App (India's No.1 App for Bank & SSC Exams) - http://bit.ly/adda247 3. To get all latest videos in your mailbox, subscribe to our youtube channel - https://www.youtube.com/adda247live 4. Get all updates on facebook, like us our facebook page - https://www.facebook.com/adda247live 5. Join us at twitter - https://twitter.com/adda247live
Data Preparation Step for Automated Diagnosis based on HRV Analysis and Machine Learning
 
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Video for ICSET 2016 Conference for a paper titled "Data Preparation Step for Automated Diagnosis based on HRV Analysis and Machine Learning" Abstract: This paper describes the data preparation step of a proposed method for automated diagnosis of various diseases based on heart rate variability (HRV) analysis and machine learning. HRV analysis – consisting of time-domain analysis, frequency-domain analysis, and nonlinear analysis – is employed because its resulting parameters are unique for each disease and can be used as the statistical symptoms for each disease, while machine learning techniques are employed to automate the diagnosis process. The input data consist of electrocardiogram (ECG) recordings. The proposed method is divided into three main steps, namely dataset preparation step, machine learning step, and disease classification step. The dataset preparation step aims to prepare the training data for machine learning step from raw ECG signals, and to prepare the test data for disease classification step from raw RRI signals. The machine learning step aims to obtain the classifier model and its performance metric from the prepared dataset. The disease classification step aims to perform disease diagnosis from the prepared dataset and the classifier model. The implementation of data preparation step is subsequently described with satisfactory result. Keywords: Automated diagnosis, ECG signal, RRI signal, HRV analysis, and machine learning.
Views: 665 Vincentius Timothy
SMS Equation Based Analysis
 
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Learn how to use the Equation Based Analysis tool in the SMS Advanced Desktop Software to create new datasets based on other layers currently saved to the management tree. This tool can be used to generate prescriptions or recommendations for planting, spraying, seeding and fertilizing applications, creating management zones and much more. -------------------- Download a free trial of SMS Basic https://dealer.agleader.com/kbp/index.php?View=entry&EntryID=409 Join the SMS Support Forum https://dealer.agleader.com/kbp/index.php?View=entry&EntryID=367 --------------------- Ag Leader SMS Tutorials: https://www.youtube.com/user/AgLeaderSMSTutorials Ag Leader Hardware Tutorials: https://www.youtube.com/channel/UC0wFsEhFvKXFgQaNmCe5YXw Ag Leader Technology: https://www.youtube.com/user/AgLeaderTechnology --------------------- Ag Leader Technology, Inc., based in Ames, Iowa, is a world leader in the design and production of hardware and software for precision farming. Ag Leader's product line includes yield monitors, desktop mapping software, GPS receivers, GPS-assisted vehicle steering systems, liquid application control systems, granular control systems and planter/seeder control systems. Learn more: http://www.agleader.com
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: 2286 Broad Institute
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: 6 Bob Trenwith
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.
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: 551 Enthought
SSC CGL Degree Based Data Interpretation Approach  and 2013 Asked Question
 
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Support for CAF Classes 👉 - http://bit.ly/2KGXWfG (Voluntary Fee which is 100% Optional) दोस्तों नोट्स और Updates के लिए Telegram पर हमें JOIN करे । https://t.me/cafofficial SSC CGL Degree Based Data Interpretation Approach with Solution Watch Quantitative Videos playlist : https://goo.gl/eyU3rM Watch Reasoning Videos Playlist : https://goo.gl/GLf2yu Watch English Videos Playlist : https://goo.gl/FSp3Fy Watch Current Affairs Week Wise : https://goo.gl/Mi3Ri1 Watch Computer Awareness Playlist : https://goo.gl/HfhxsV 2017 Banking / Govt Job Details ,Syllabus ,review Notification : https://goo.gl/yhR1JU If you are Preparing for SSC and Other Govt Exams then watch these Science Videos Also : Watch Advanced Mathematics playlist : https://goo.gl/5hkFQP Watch Reasoning For SSC CGL,CHSL,UPSC,State PSC ,Railway : https://goo.gl/SbxHbH Watch Science playlist : https://goo.gl/UCeAf1 ******************************************************** Follow Us below social links to reach out us. Like Our Facebook Page : https://goo.gl/V9RrYz Join our Study Group : https://goo.gl/Ygba1C Join our Twitter Handle : https://goo.gl/P6vHCs Join our Gplus updates : https://goo.gl/C97U5g Visit our Website : https://goo.gl/36WzZb For Business Queries contact Us : [email protected] *********************************************** Watch Quantitative Aptitude Videos chapter wise *********************************************** Simplification : https://goo.gl/6ItFLN Number System : https://goo.gl/QMZPpF Ratio and Proportion : https://goo.gl/2onZVW Percentage : https://goo.gl/nrCHxG Ages : https://goo.gl/Wg0zYE Number Series : https://goo.gl/ee7TGW Approximation : https://goo.gl/B4Oa7A Quadratic Equations : https://goo.gl/bovORD Surds and Indices : https://goo.gl/nY8ayV Partnership : https://goo.gl/UUdl2K Time and Work : https://goo.gl/Rf4eGh Pipe and Cisterns : https://goo.gl/jN2EBm Average : https://goo.gl/Pdn3fb Time Speed and Distance : https://goo.gl/HtgCvR profit and loss : https://goo.gl/maUx2S simple interest and compound interest : https://goo.gl/ibFuqa Mixture & Alligation : https://goo.gl/b7k1Ef Data Interpretation : https://goo.gl/ZgPYHb Boat and Stream : https://goo.gl/ME86nC Permutation and Combination : https://goo.gl/GwH59V Probability : https://goo.gl/j4KrAH Mensuration : https://goo.gl/lYo2zV Mix Quantitative Aptitude Questions (SSC , Bank , Railway Exams) : https://goo.gl/UsW7nn ********************************************* Watch Reasoning Videos chapter-wise from here : ********************************************* Syllogism : https://goo.gl/aVL4aW InEquality : https://goo.gl/DanpUW Alphabet series : https://goo.gl/u4X609 Digit Aptitude : https://goo.gl/B0EcyG Statement and Argument : https://goo.gl/39q11k Data Sufficiency : https://goo.gl/HkO7fA Ranking: https://goo.gl/kW2riL Direction Sense Test: https://goo.gl/Nu0KTs Coding Decoding : https://goo.gl/mdBiVv Blood Relation : https://goo.gl/J93Wfc Machine Input Output : https://goo.gl/M6rolH Puzzles : https://goo.gl/IVKZ8U Seating Arrangement : https://goo.gl/VceLIV Critical Reasoning : https://goo.gl/hlIYjq Course of Action : https://goo.gl/dFfyMS How Many Pair : https://goo.gl/s1C3p4 Logical Venn Diagrams : https://goo.gl/iloJ9G Analogy : https://goo.gl/vvD5Xs ******************************************* Bank Descriptive Paper Letter and Essay : https://goo.gl/DnfeXG Error Spotting for Competitive Exams : https://goo.gl/KOqRY2 ************************************* Biology : https://goo.gl/n0rdVc Physics : https://goo.gl/fkms60 Chemistry : https://goo.gl/YiCJ0P ************************************* Static GK : https://goo.gl/p1tXyp ************************************* Computer Networks : https://goo.gl/AKRGJI ************************************************************** If you are Preparing for SSC ,Railway and other Exams Except Bank then watch Advance maths also .. ************************************************************** Trigonometry : https://goo.gl/eZo1MN Algebra : https://goo.gl/JG4ybW Geometry : https://goo.gl/TMva6V Height and Distance|Trigonometry : https://goo.gl/xOsRK7 Quadrilaterial(चतुर्भुज ) All Type: https://goo.gl/fd7WZY DI for SSC Exams : https://goo.gl/FltPFW
45| Mathematica Predicates in Rule Based Programming - Data Analysis and Visualization
 
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Data Analysis and Visualization is a Rich-full series Directed to all students interested in Analyzing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesiers in University of Western Australia, and Contains the main flowing Topics: 1 Data Visualization in Excel 2 Array Formula in Excel 3 2D Array Formula in Excel 4 Excel Macros 5 Why Matlab 6 Problem Solving in MATLAB 7 MATLAB Orientation - Data Types and Expressions 8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions 9 Matrices in MATLAB 10 MATLAB Scripts and Functions 11 Random Numbers, Gaussian Random Numbers, Complex Numbers 12 An Examples of Script and Function Files 13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure, 14 MATLAB Loops, Nested Loops, Repetition, while, For, 15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions, 16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence, 17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all, 18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets, 19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array 20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot 21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot, 22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots, 23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid, 24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp, 25 Arrays vs. Matrix Operations, 26 Dot Products, Example Calculating Center of Mass, Center of Gravity, 27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products, 28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors 29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws, 30 Symbolic Differentiation, sym, syms, diff 31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference, 32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule, 33 Monte Carlo Integration, 34 Introduction to ODE in System Biology 35 Introduction to System Biology, Gene Circuits, 36 Solving ODEs in Matlab, Repressilator, Programming steps 37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional, 38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres, 39 Introduction to Mathematica, 40 Programming in Mathematica 41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica 42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters, 43 Mathematica Rule Based Programming, Functional Programming, 44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules, 45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle, 46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation, 47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,
Views: 63 TO Courses
SBi Clerk || VENN Diagram  Based Caselet Data Interpretation | ये प्रश्न पुछा गया था
 
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Support for CAF Classes 👉 - http://bit.ly/2KGXWfG (Voluntary Fee which is 100% Optional) दोस्तों नोट्स और Updates के लिए Telegram पर हमें JOIN करे । https://t.me/cafofficial ******************************************************** Follow Us below social links to reach out us. Like Our Facebook Page : https://goo.gl/V9RrYz Join our Study Group : https://goo.gl/Ygba1C Join our Twitter Handle : https://goo.gl/P6vHCs Join our Gplus updates : https://goo.gl/C97U5g Visit our Website : https://goo.gl/36WzZb For Business Queries contact Us : [email protected] *********************************************** Watch Quantitative Videos playlist : https://goo.gl/eyU3rM Watch Reasoning Videos Playlist : https://goo.gl/GLf2yu Watch English Videos Playlist : https://goo.gl/FSp3Fy Watch Computer Awareness Playlist : https://goo.gl/HfhxsV 2017 Banking / Govt Job Details ,Syllabus ,review Notification : https://goo.gl/yhR1JU If you are Preparing for SSC and Other Govt Exams then watch these Science Videos Also : Watch Advanced Mathematics playlist : https://goo.gl/5hkFQP Watch Reasoning For SSC CGL,CHSL,UPSC,State PSC ,Railway : https://goo.gl/SbxHbH Watch Quantitative Aptitude Videos chapter wise *********************************************** Simplification : https://goo.gl/6ItFLN Number System : https://goo.gl/QMZPpF Ratio and Proportion : https://goo.gl/2onZVW Percentage : https://goo.gl/nrCHxG Ages : https://goo.gl/Wg0zYE Number Series : https://goo.gl/ee7TGW Approximation : https://goo.gl/B4Oa7A Quadratic Equations : https://goo.gl/bovORD Surds and Indices : https://goo.gl/nY8ayV Partnership : https://goo.gl/UUdl2K Time and Work : https://goo.gl/Rf4eGh Pipe and Cisterns : https://goo.gl/jN2EBm Average : https://goo.gl/Pdn3fb Time Speed and Distance : https://goo.gl/HtgCvR profit and loss : https://goo.gl/maUx2S simple interest and compound interest : https://goo.gl/ibFuqa Mixture & Alligation : https://goo.gl/b7k1Ef Data Interpretation : https://goo.gl/ZgPYHb Boat and Stream : https://goo.gl/ME86nC Permutation and Combination : https://goo.gl/GwH59V Probability : https://goo.gl/j4KrAH Mensuration : https://goo.gl/lYo2zV Mix Quantitative Aptitude Questions (SSC , Bank , Railway Exams) : https://goo.gl/UsW7nn ********************************************* Watch Reasoning Videos chapter-wise from here : ********************************************* Syllogism : https://goo.gl/aVL4aW InEquality : https://goo.gl/DanpUW Alphabet series : https://goo.gl/u4X609 Digit Aptitude : https://goo.gl/B0EcyG Statement and Argument : https://goo.gl/39q11k Data Sufficiency : https://goo.gl/HkO7fA Ranking: https://goo.gl/kW2riL Direction Sense Test: https://goo.gl/Nu0KTs Coding Decoding : https://goo.gl/mdBiVv Blood Relation : https://goo.gl/J93Wfc Machine Input Output : https://goo.gl/M6rolH Puzzles : https://goo.gl/IVKZ8U Seating Arrangement : https://goo.gl/VceLIV Critical Reasoning : https://goo.gl/hlIYjq Course of Action : https://goo.gl/dFfyMS How Many Pair : https://goo.gl/s1C3p4 Logical Venn Diagrams : https://goo.gl/iloJ9G Analogy : https://goo.gl/vvD5Xs ******************************************* Bank Descriptive Paper Letter and Essay : https://goo.gl/DnfeXG Error Spotting for Competitive Exams : https://goo.gl/KOqRY2 ************************************* Biology : https://goo.gl/n0rdVc Physics : https://goo.gl/fkms60 Chemistry : https://goo.gl/YiCJ0P ************************************* Static GK : https://goo.gl/p1tXyp ************************************* Computer Networks : https://goo.gl/AKRGJI ************************************************************** If you are Preparing for SSC ,Railway and other Exams Except Bank then watch Advance maths also .. ************************************************************** Trigonometry : https://goo.gl/eZo1MN Algebra : https://goo.gl/JG4ybW Geometry : https://goo.gl/TMva6V Height and Distance|Trigonometry : https://goo.gl/xOsRK7 Quadrilaterial(चतुर्भुज ) All Type: https://goo.gl/fd7WZY DI for SSC Exams : https://goo.gl/FltPFW About us : We are dealing in various exams of SSC , Bank and other Govt exams . you can check our awesome portfolio . Our Work : 1.Quantitative Aptitude 2.Logical Reasoning 3.Current Affairs Weekly Wise 4.Computer Aptitude 5.English Language
Introducing the Common Online Data Analysis Platform
 
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Data are everywhere, except in the classroom! The Concord Consortium’s Common Online Data Analysis Platform (CODAP) provides an easy-to-use web-based data analysis tool, geared toward middle and high school students, and aimed at teachers and curriculum developers. In this video, we demonstrate CODAP, presenting some of the many ways data flows into CODAP; dynamically linked tables, graphs, and maps; and user interaction with hierarchical data structures that help make the data intuitive. The Concord Consortium is currently collaborating with several NSF-funded curriculum development projects to bring more rich experiences with data to more teachers and students. We highlight CODAP’s use in InquirySpace (physics), OceanTracks (marine biology), and Terra Populus (social science). CODAP is free and open source with a commercial-friendly license. As the CODAP community grows, we expect this platform to become more powerful, flexible, and adaptable to a variety of online contexts. We invite you to join the CODAP community. http://codap.concord.org/ Vote for this video in the NSF STEM for All Video Showcase May 17-23, 2016: http://stemforall2016.videohall.com/presentations/740
Views: 430 concordconsortium

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