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LSE Research in Mandarin | Functional data analysis and machine learning
 
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Contributors: Dr Xinghao Qiao, Dr Catherine Xiang In this video of ‘LSE Research in Mandarin’, Dr Xinghao Qiao talks to Dr Catherine Xiang about his research on using functional data analysis method to identify key variables in the use of big data. He also talks about implications of statistics in machine learning.
Data Analysis in Excel Tutorial
 
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Data Analysis using Microsoft Excel using SUMIF , CHOOSE and DATE Functions
Views: 82187 TEKNISHA
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1516566 ExcelIsFun
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
 
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Download File: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch00/Excel2013StatisticsChapter00.xlsx All Excel Files for All Video files: http://people.highline.edu/mgirvin/excelisfun.htm. Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More: (00:08) Introduction to class (00:49) Cells, Worksheets, Workbooks, File Names (02:54) Navigating Worksheets & Workbook (03:58) Navigation Keys (04:15) Keyboard move Active Sheet (05:40) Ribbon Tabs (06:25) Add buttons to Quick Access Tool Bar (07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis & Charting (08:55) Introduction to Data Analysis (10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells (11:16) Keyboard Enter puts content in cell and move selected cell down (13:00) Data Type DEFAULT Alignments (13:11) First Formula. Entering Cell References in formulas (13:35) Keyboard Ctrl + Enter puts content in cell & keep cell selected (14:45) Why we don’t override DEFAULT Alignments (15:05) Keyboard Ctrl + Z is Undo (17:05) Proper Data Sets & Raw Data (24:21) How To Enter Data & Data Labels (24:21) Stylistic Formatting (26:35) AVERAGE Function (27:31) Format Formulas Differently than Raw Data (28:30) Keyboard Ctrl + C is Copy. Keyboard Ctrl + V is Paste (29:59) Use Eraser remove Formatting Only (29:19) Keyboard Ctrl + B adds Bold (29:57) Excel’s Golden Rule (31:43) Keyboard F2 puts cell in Edit Mode (32:01) Violating Excel’s Golden Rule (34:12) Arrow Keys to put cell references in formulas (35:40) Full Discussion about Formulas & Formulas Elements (37:22) SUM function Keyboard is Alt + = (38:22) Aggregate functions (38:50) Why we use ranges in functions (40:56) COUNT & COUNTA functions (42:47) Edit Formula & change cell references (44:18) Absolute & Relative Cell References (45:52) Use Delete Key, Not Right-click Delete (46:40) Fill Handle & Angry Rabbit to copy formula (47:41) Keyboard F4 Locks Cell Reference (make Absolute) (49:45) Keyboard Tab puts content in Cell and move selected Cell to right (50:55) Order of Operation error (52:17) Range Finder to find formula errors (52:34) Lock Cell Reference after you put cell in Edit Mode (53:58) Quickly copy an edited formula down a column (53:07) F2 key in last cell to find formula errors (54:15) Fix incorrect range in function (54:55) SQRT function & Fractional Exponents (57:20) STDEV.P function (58:10) Navigate Large Data Sets (58:48) Keyboard Ctrl + Arrow jumps to bottom of data set (59:42) Keyboard Ctrl + Shift + Arrow selects to bottom of data set (Current Range) (01:01:41) Keyboard Shift + Enter puts content in Cell and move selected Cell up (01:02:55) Counting with conditions or criteria: COUNTIFS function (01:03:43) Keyboard Ctrl + Backspace jumps back to Active Cell (01:05:31) Counting between an upper & lower limit with COUNTIFS (01:07:36) COUNTIFS copied down column (01:10:08) Joining Comparative Operator with Cell Reference in formula (01:12:50) Data Analysis features in Excel (01:13:44) Sorting (01:16:59) Filtering (01:20:39) Introduction to PivotTables (01:23:39) Create PivotTable dialog box (01:24:33) Dragging & dropping Fields to create PivotTable (01:25:31) Dragging Field to Row area creates a Unique List (01:26:17) Outline/Tabular Layout (01:27:00) Value Field Settings dialog to change: Number Formatting, Function, Name (01:28:12) 2nd & 3rd PivotTable examples (01:31:23) What is a Cross Tabulated Report? (01:33:04) Create Cross Tabulated Report w PivotTable (01:35:05) Show PivotTable Field List (01:36:48) How to Pivot the Report (01:37:50) Summarize Survey Data with PivotTable. (01:38:34) Keyboard Alt, N, V opens PivotTable dialog box (01:41:38) PivotTable with 3 calculations: COUNT, MAX & MIN (01:43:25) Count & Count Number calculations in a PivotTable (01:45:30) Excel 2013 Charts to Visually Articulate Quantitative Data (01:47:00) #1 Rule for Charts: No Chart Junk! (01:47:30) Explain chart types: Column, Bar, Pie, Line and X-Y Scatter Chart (01:51:34) Create Column Chart using Recommended Chart feature (01:53:00) Remove Field Buttons from Pivot Chart (01:54:10) Chart Formatting Task Pane (01:54:45) Vary Fill Color by point (01:55:15) Format Axis with Numbers by Formatting Source Data in PivotTable (01:56:02) Add Data Labels to Chart (01:57:28) Copy Chart & Create Bar Chart (01:57:48) Change Chart Type (01:58:15) Change Gap Width. (01:59:17) Create Pie Chart (01:59:23) Do NOT use 3-D Pie (01:59:42) Add % Data Labels to Pie Chart (02:00:25) Create Line Chart From PivotTable (02:01:20) Link Chart Tile to Cell (02:02:20) Move a Chart (02:02:33) Create an X-Y Scatter Chart (02:03:35) Add Axis Labels (02:05:27) Number Formatting to help save time (02:07:24) Number Formatting is a Façade (02:10:27) General Number Format (02:10:52) Percentage Number Formatting (02:14:03) Don’t Multiply Relative Frequency by 100 (02:17:27) Formula for % Change & End Amount
Views: 411829 ExcelIsFun
Microsoft Excel data analysis tool for statistics mean, median, hypothesis, regression
 
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This video covers a few topics using the data analysis tool. After this video you should be able to: a) Find and use data analysis on excel to calculate statistics b) Calculate the mean, median, mode, standard deviation, range and coefficient variation on a variable set of data in excel. c) Conduct a confidence interval in excel. d) Complete a T-test in excel to help complete a hypothesis test. e) Conduct a linear regression analysis output from excel and create a scatter diagram.
Views: 96661 Me ee
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 177377 CS Dojo
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: 716310 Dr Nic's Maths and Stats
Recent advances in functional data analysis
 
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RSS 2013 International Conference John Moriarty (University of Manchester, UK) Surajit Ray (University of Glasgow, UK, and Boston University, USA) Laura M. Sangalli (Politecnico di Milano, Italy) Slides can be downloaded from: www.rss.org.uk/video
Views: 1910 RoyalStatSoc
Data Analyst Job Description | What 4 Skills Will You Need To Be A Data Analyst?
 
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In this video we are going to define the job description of a data analyst, what a data analyst does, and the best online course to become a data analyst. ► Full Playlist Explaining Data Jargon ( http://bit.ly/2mB4G0N ) ► Top 4 Best Laptops for Data Analysts ( https://youtu.be/Vtk50Um_yxA ) ► Break Into the Data Industry with the best data analytics online learning resources from Edureka! ( http://bit.ly/2yCbsac ) --- affiliate link to help support this channel!^ Currently the average pay for a data analyst is $76,419 on the button, according to glassdoor I receive a lot of questions about what it takes to become a data analyst and what is a data analyst. Clearing up what a data analyst does everyday and what that description means to someone looking to enter the data science industry What will you actually be asked to do on the day to day as a data analyst. ► Top 4 Responsibilities in the Daily Life of a Data Analyst: 1 ) Mathematics Although mathematics only makes up about 20% of the day to day life of a data analyst. It is still important to have a strong understanding of the foundations of mathematics. - Addition - Subtraction - Multiplication - Division - Most Importantly --- Statistics Data analytics is all about statistics. Most of the statistics will be handled by the tools you are working with, but in order to be a great data analyst it is best to know why the tools are producing specific results. A strong understanding of statistics will be useful to you. 2 ) Computer Programming You must be able to work proficiently in one or more computer programming languages. This make up for roughly 60%-70% of your daily work. in order to analyze data it must be queried (drawn) from a large data warehouse. You will use computer programming languages such as SQL, Python, and R to query data. Before we move on let me define the term Query, if it does not resonate with you. You need strong computer programming skills in order to accomplish this task. As a data analyst you will do a lot of drawing and analyzing data. ► For more info on databases, SQL, and other jargon check out our Video Series on Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) 3 ) Know the Tools of the Trade Once you query data from the database onto your workspace you will begin to utilize data analytics tools to process, scrub, and analyze data (data Jargon explained on our Video series ^^^). You will be able to perform these tasks by using tools like Hadoop, Open Refine, Tableau, Apache Spark, etc... As you process the data you will begin to see connections between the data sets. You will see some of the following errors and you will want to remove these in order to ensure that your data analysis is accurate: - Duplicated data - Improperly formatted data - Incomplete data - Inaccurate data - This data will corrupt your findings and could possibly lose you client or employer millions of dollars. Make sure you know how to use those data analytics tools WELL! 4 ) Communicate and Present Insights Data Analyst will also be called upon to clearly and consciously present your research to clients, managers, or executives. Ok, now I know you are curious if you are capable of learning all of these crucial skills. Yes, you can, but there is a clause. You have to learn from the best. The guys over at Edureka.co are the leading professionals in the big data training industry. Based out of India, home to over 101,000 individuals in the data science industry (at the time of this writing). They are eager to make a way for themselves in the new digital economy. They are on the cutting edge of data analytics and eager to teach it to anyone worldwide. Testimonies of increased salaries, new employment, and 597,089 (updated) satisfied learners make edureka the best choice to learn the skills you need in the data industry. Question is will you actually do it. Imagine deregulating yourself for the data industry. Right now, it is a black hole, you don't know what's inside, but it is screaming opportunity from the darkness. TURN ON THE LIGHT and break into the data industry. A future proof opportunity for the next decade and beyond. ► Edureka Big Data Masters Program ( http://bit.ly/2yCbsac ) affiliate link^ ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Computer ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel!
Views: 87267 Ben G Kaiser
Exploration of Functional Data: Regression, Classification, Interpolation
 
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This is a ~3-minute video highlight produced by undergraduate students Candice Schumann and John Talbot regarding their research topic during the 2014 AMALTHEA REU Program at Florida Institute of Technology in Melbourne, FL. They were mentored by doctoral student Rana Haber and associate professor Dr. Adrian M. Peter (Engineering Systems Department). More details about their project can be found at http://www.amalthea-reu.org.
Understanding descriptive and inferential statistics | lynda.com overview
 
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This statistical analysis overview explains descriptive and inferential statistics. Watch more at http://www.lynda.com/Excel-2007-tutorials/business-statistics/71213-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-71213-0101 This specific tutorial is just a single movie from chapter one of the Excel 2007: Business Statistics course presented by lynda.com author Curt Frye. The complete Excel 2007: Business Statistics course has a total duration of 4 hours and 19 minutes and covers formulas and functions for calculating averages and standard deviations, charts and graphs for summarizing data, and the Analysis ToolPak add-in for even greater insights into data Excel 2007: Business Statistics table of contents: Introduction 1. Introducing Statistics 2. Learning Useful Excel Techniques 3. Summarizing Data Using Tables and Graphics 4. Describing Data Using Numerical Methods 5. Using Probability Distributions 6. Sampling Values from a Population 7. Testing Hypotheses 8. Using Linear and Multiple Regression Conclusion
Views: 106973 LinkedIn Learning
How to Analyze Satisfaction Survey Data in Excel with Countif
 
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Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey ----- Soar beyond the dusty shelf report with my free 7-day course: https://depictdatastudio.teachable.com/p/soar-beyond-the-dusty-shelf-report-in-7-days/ Most "professional" reports are too long, dense, and jargony. Transform your reports with my course. You'll never look at reports the same way again.
Views: 359630 Ann K. Emery
Grounded Theory | Overview
 
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Grounded Theory is a Qualitative approach that let's theory emerge from data. This video is a conversation starter about Grounded Theory basics and shows some examples of axial coding. Coding, categories, and memoing. There a various types of Grounded Theory and two particular popular methods are highlighted.
Views: 41940 Diana Lizarraga
Excel and Questionnaires: How to enter the data and create the charts
 
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This is a tutorial on how to enter the results of your questionnaires in Excel 2010. It then shows you how to create frequency tables (using the countif function not the frequency function). The next stage is creating charts.
Views: 354700 Deirdre Macnamara
Data Analysis with MATLAB for Excel Users
 
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This webinar highlights how MATLAB can work with Excel. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Learn MATLAB for Free: https://goo.gl/xIiHyG Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar highlights how MATLAB can supplement the capabilities of Excel by providing access to thousands of pre-built engineering and advanced analysis functions and versatile visualization tools. Learn more about using MATLAB with Excel: http://goo.gl/3vkFMW Learn more about MATLAB: http://goo.gl/YKadxi Through product demonstrations you will see how to: • Access data from spreadsheets • Plot data and customize figures • Perform statistical analysis and fitting • Automatically generate reports to document your analysis • Freely distribute your MATLAB functions as Excel add-ins This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets. Previous knowledge of MATLAB is not required. About the Presenter: Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.
Views: 232644 MATLAB
What Qualitative Data Analysis software can and can’t do for you – an intro video
 
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This is a brief intro video on what qualitative data analysis software can do, and what it can't do. It explores the functionality of qualitative data analysis software by distinguishing four main functions: Organization of Data, Annotation of Data, Searching of Data, and Display of Data. For more information on Qualitative Data Analysis Software and Qualitative Methods, visit my website: http://squaremethodology.com/ The production of this video was made possible through the support of MERIT Library at the School of Education, University of Wisconsin-Madison.
Views: 4646 squaremethodology
What is GROUNDED THEORY? What does GROUNDED THEORY mean? GROUNDED THEORY meaning & explanation
 
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I MAKE CUTE BABIES - https://amzn.to/2DqiynS What is GROUNDED THEORY? What does GROUNDED THEORY mean? GROUNDED THEORY meaning - GROUNDED THEORY definition - GROUNDED THEORY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Grounded theory (GT) is a systematic methodology in the social sciences involving the construction of theory through the analysis of data. Grounded theory is a research methodology which operates almost in a reverse fashion from social science research in the positivist tradition. Unlike positivist research, a study using grounded theory is likely to begin with a question, or even just with the collection of qualitative data. As researchers review the data collected, repeated ideas, concepts or elements become apparent, and are tagged with codes, which have been extracted from the data. As more data are collected, and as data are re-reviewed, codes can be grouped into concepts, and then into categories. These categories may become the basis for new theory. Thus, grounded theory is quite different from the traditional model of research, where the researcher chooses an existing theoretical framework, and only then collects data to show how the theory does or does not apply to the phenomenon under study. Grounded theory combines diverse traditions in sociology, positivism and symbolic interactionism as it is according to Ralph, Birks & Chapman (2015) "methodologically dynamic". Glaser's strong training in positivism enabled him to code the qualitative responses, however Strauss's training looked at the "active" role of people who live in it. Strauss recognized the profundity and richness of qualitative research regarding social processes and the complexity of social life, Glaser recognized the systematic analysis inherent in quantitative research through line by line examination, followed by the generation of codes, categories, and properties. According to Glaser (1992), the strategy of Grounded Theory is to take the interpretation of meaning in social interaction on board and study "the interrelationship between meaning in the perception of the subjects and their action". Therefore, through the meaning of symbols, human beings interpret their world and the actors who interact with them, while Grounded Theory translates and discovers new understandings of human beings' behaviors that are generated from the meaning of symbols. Symbolic interactionism is considered to be one of the most important theories to have influenced grounded theory, according to it understanding the world by interpreting human interaction, which occurs through the use of symbols, such as language. According to Milliken and Schreiber in Aldiabat and Navenec, the grounded theorist's task is to gain knowledge about the socially-shared meaning that forms the behaviors and the reality of the participants being studied. Once the data are collected, grounded theory analysis involves the following basic steps: 1. Coding text and theorizing: In grounded theory research, the search for the theory starts with the very first line of the very first interview that one codes. It involves taking a small chunk of the text where line by line is being coded. Useful concepts are being identified where key phrases are being marked. The concepts are named. Another chunk of text is then taken and the above-mentioned steps are being repeated. According to Strauss and Corbin, this process is called open coding and Charmaz called it initial coding. Basically, this process is breaking data into conceptual components. The next step involves a lot more theorizing, as in when coding is being done examples are being pulled out, examples of concepts together and think about how each concept can be related to a larger more inclusive concept. This involves the constant comparative method and it goes on throughout the grounding theory process, right up through the development of complete theories. 2. Memoing and theorizing: Memoing is when the running notes of each of the concepts that are being identified are kept. It is the intermediate step between the coding and the first draft of the completed analysis. Memos are field notes about the concepts in which one lays out their observations and insights. Memoing starts with the first concept that has been identified and continues right through the process of breaking the text and of building theories. 3. Integrating, refining and writing up theories: Once coding categories emerges, the next step is to link them together in theoretical models around a central category that hold everything together.
Views: 18378 The Audiopedia
Grounded Theory - Core Elements. Part 1
 
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In this two part video, Graham R Gibbs introduces the idea of developing grounded theory and discusses some of the core elements of the approach to qualitative data analysis. See: Gibbs, Graham Robert. (2012) 'Grounded theory, coding and computer-assisted analysis'. In S. Becker, A. Bryman & H. Ferguson (eds.), Understanding Research for Social Policy and Social Work: Themes, Methods and Approaches. 2nd edn. Bristol: Policy Press. pp. 337-343. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) http://creativecommons.org/licenses/by-nc-sa/4.0/
Views: 109358 Graham R Gibbs
MATLAB Tools for Scientists: Introduction to Statistical Analysis
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- Researchers and scientists have to commonly process, visualize and analyze large amounts of data to extract patterns, identify trends and relationships between variables, prove hypothesis, etc. A variety of statistical techniques are used in this data mining and analysis process. Using a realistic data from a clinical study, we will provide an overview of the statistical analysis and visualization capabilities in the MATLAB product family. Highlights include: • Data management and organization • Data filtering and visualization • Descriptive statistics • Hypothesis testing and ANOVA • Regression analysis
Views: 14892 MATLAB
Simple Linear regression analysis using Microsoft Excel's data analysis toolpak and ANOVA Concepts
 
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Knowledge Varsity (www.KnowledgeVarsity.com) is sharing this video with the audience.
Views: 132704 KnowledgeVarsity
Big Data Analytics in Hindi | big data analytics tutorial
 
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Big Data Analytics in Hindi | big data analytics tutorial Welcome to this video of big data analytics. This video of big data analytics will explain you the concept of big data, sources of big data and big data analytics, what are the benefits of big data analytics. We are doing online transactions; we are using debit cards, credit cards, atm cards. In all this we are creating data. We are into online shopping, we are in to online selling. This also creates big data. Universities are offering online courses, online examinations ,all these transactions are creating big data. We are into online booking of tickets, online hotel booking. We are using social media like face book, twitter and other social media sources. We are sharing pics , videos and text messages there, what all is that. That is data, we can say big data. Traffic monitoring, aircraft monitoring etc , all these things are creating big data. After watching this video viewers will be able to know about various sources of big data. This video is produced in Hindi; so that it can be easily understood by all the person's who like to learn the concept of big data in Hindi. In big data analytics, we can have idea of customer behavior when they buy online from websites. While selling something on a online selling website, with the help of big data analytics, we can have idea about the type and price of the product that mostly customer buy online. What is the use of selling something that no one buys. Also Visit our website http://www.ethtimes.com Our facebook page http://www.facebook.com/learningseveryday Please watch full video for complete concept of big data analytic s. -~-~~-~~~-~~-~- Please watch: "what is flow chart | symbols of flowchart explained in hindi" https://www.youtube.com/watch?v=k2I8gp1NGGU -~-~~-~~~-~~-~-
Views: 20233 Learning Everyday
Correlation analysis using Excel
 
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How to run a correlation analysis using Excel and write up the findings for a report
Views: 295796 Chris Olson
SPSS for Beginners 1: Introduction
 
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Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.
Views: 1427079 Research By Design
Survival Models: Introduction to Survival Analysis | Data Science
 
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In this video you will learn the basics of Survival Models. This is an introductory session. Hands on using SAS is there in another video. You will learn what is Kaplan Mayer estimation, Cox proportional hazard model theory etc. Correction : Normality assumption is not required for Linear Regression as said in the video. But a number of results from linear regression can not be fully explained with this For training, consulting or help Contact : [email protected] For Study Packs : http://analyticuniversity.com/ Study Packs : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 70082 Analytics University
Introduction to ANOVA
 
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statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Views: 381551 statslectures
Data Analysis In Excel Max Min Mode Median Hindi
 
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Data Analysis in Excel Course- learn the basics of Data Analysis by understanding the Min formula, Max formula, Median Formula and Mode.sngl formulain Excel . The 4 formulas allow you to analysis data for basic traits like lowest value, highest value, the value in middle and the most commonly occurring value. At the end of the video there's a simple and effective chart tutorial also. To watch more videos and download the files visit http://www.myelesson.org To Buy a Excel Course DVD visit . https://www.instamojo.com/Devika/combo-pack-all-in-one-ms-excel-course-cd-in-/ 10 Most Used Formulas MS Excel https://www.youtube.com/watch?v=KyMj8HEBNAk Learn Basic Excel Skills For Beginners || Part 1 https://www.youtube.com/watch?v=3kNEv3s8TuA 10 Most Used Excel Formula https://www.youtube.com/watch?v=2t3FDi98GBk **Most Imporant Excel Formuls Tutorials** Learn Vlookup Formula For Beginners in Excel https://www.youtube.com/watch?v=vomClevScJQ 5 Excel Questions Asked in Job Interviews https://www.youtube.com/watch?v=7Iwx4AMdij8 Create Speedometer Chart In Excel https://www.youtube.com/watch?v=f6c93-fQlCs Learn the Basic of Excel for Beginners || Part 2 https://www.youtube.com/watch?v=qeMSV9T1PoI Create Pareto Chart In Excel https://www.youtube.com/watch?v=2UdajrDMjRE How to Create Dashboard in Excel https://www.youtube.com/watch?v=RM8T1eYBjQY Excel Interview Questions & Answers https://www.youtube.com/watch?v=Zjv1If63nGU
Views: 20321 My E-Lesson
Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Training | Edureka
 
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( Python Training : https://www.edureka.co/python ) This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014. This Python Pandas tutorial video helps you to learn following topics: 1. What is Data Analysis? 2. What is Pandas? 3. Pandas Operations 4. Use-case Check out our Python Training Playlist: https://goo.gl/Na1p9G Subscribe to our channel to get video updates. Hit the subscribe button above. #Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonPandas How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 145151 edureka!
Quick Data Analysis with Google Sheets | Part 1
 
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Spreadsheet software like Excel or Google Sheets are still a very widely used toolset for analyzing data. Sheets has some built-in Quick analysis features that can help you to get a overview on your data and very fast get to insights. #DataAnalysis #GoogleSheet #measure 🔗 Links mentioned in the video: Supermetrics: http://supermetrics.com/?aff=1014 GA Demo account: https://support.google.com/analytics/answer/6367342?hl=en 🎓 Learn more from Measureschool: http://measureschool.com/products GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear Our tracking stack: Google Analytics: https://analytics.google.com/analytics/web/ Google Tag Manager: https://tagmanager.google.com/ Supermetrics: http://supermetrics.com/?aff=1014 ActiveCampaign: https://www.activecampaign.com/?_r=K93ZWF56 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool
Views: 12213 Measureschool
Statistical Text Analysis for Social Science
 
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What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? In order to answer these questions effectively, we must apply and develop scientific methods in statistics, computation, and linguistics. In this talk I will illustrate these methods in a project that analyzes events in international politics. Political scientists are interested in studying international relations through *event data*: time series records of who did what to whom, as described in news articles. To address this event extraction problem, we develop an unsupervised Bayesian model of semantic event classes, which learns the verbs and textual descriptions that correspond to types of diplomatic and military interactions between countries. The model uses dynamic logistic normal priors to drive the learning of semantic classes; but unlike a topic model, it leverages deeper linguistic analysis of syntactic argument structure. Using a corpus of several million news articles over 15 years, we quantitatively evaluate how well its event types match ones defined by experts in previous work, and how well its inferences about countries correspond to real-world conflict. The method also supports exploratory analysis; for example, of the recent history of Israeli-Palestinian relations.
Views: 1148 Microsoft Research
YOW! Lambda Jam 2016 Tim Thornton - Data Analysis with Vector Functional Programming #YOWLambdaJam
 
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Vector / array functional programming languages have been around for a long time, beginning with the introduction of APL. Modern dialects of APL such as j, k, and q offer a radical paradigm of functional programming wherein arrays are at the forefront of computation, and through the use of specific higher-order functions known as ‘adverbs’, programmers can concisely express complex algorithms and programs without resorting to loops, and rarely resorting to explicit recursion. This talk will provide an introduction to the paradigm of vector functional programming through the use of the q programming language, a very efficient, interpreted, dynamically-typed vector language from Kx Systems. After giving a brief history of vector languages, select syntax and semantics of the language will be introduced, emphasizing the terse notation as a facilitator of thought, as expressed in Iverson’s Turing Award paper 1. Through small examples of vector and atomic operations, to run-length encoding, key ideas such as verbs and adverbs, atomic vector operations, and array-based functional programming adhering to the “rule” of “no stinking loops” 2 will be demonstrated. After an introduction to the language, an end-to-end example will be shown within a practical domain for vector functional programming: fast data analysis. The analysis will involve reading data from CSV files, scraping and processing data from the web, joining gathered data, and analysis — all using Q. The attendee will leave the talk with a basic understanding of the vector functional paradigm, how it may be useful in practical domains, an understanding of array-based thinking (practical in any functional programming language), and hopefully, an appreciation — or at least openness — toward terse and precise syntax. 1 http://www.jsoftware.com/papers/tot.htm 2 http://nsl.com Tim Thornton is a q and web developer and functional programming enthusiast. Working at FD Labs (formerly Bedarra Research Labs), Tim spends his days applying vector programming to various data analysis challenges, primarily within data visualization. His interest in type theory motivated his undergraduate thesis designing a static refinement type system for q. For more on YOW! Lambda Jam, visit http://lambdajam.yowconference.com.au
Views: 9066 YOW! Conferences
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 813551 Claus Ebster
Extracting More Information Out of Data
 
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(Visit: http://www.uctv.tv/) Terry Speed, UC Berkeley Professor of Statistics, delivers the 99th annual Martin Meyerson Faculty Research Lecture. His research and teaching interests have concerned the application of statistics to genetics and molecular biology. Within that sub field, eventually to be named bioinformatics, his interests are broad, including biomolecular sequence analysis, the mapping of genes in experimental animals and humans, and functional genomics. He has been particularly involved in the low-level analysis of microarray data, and more recently, next-generation DNA sequence analysis. [1/2013] [Science] [Show ID: 23678]
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
Views: 516667 Siraj Raval
Types of statistical studies | Statistical studies | Probability and Statistics | Khan Academy
 
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Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/e/types-of-statistical-studies?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/v/correlation-and-causality?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistical-studies/statistical-questions/v/reasonable-samples?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 168269 Khan Academy
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 689685 statisticsfun
Overview of �Big Data� Research at TU Berlin
 
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Intro - By Volker Markl Part 1 - Query Optimization with MapReduce Functions, Kostas Tzoumas Abstract: Many systems for big data analytics employ a data flow programming abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions are very common. We address the problem of performing data flow optimization at this level of abstraction, where the semantics of operators are not known. Traditionally, query optimization is applied to queries with known algebraic semantics. In this work, we find that a handful of properties, rather than a full algebraic specification, suffice to establish reordering conditions for data processing operators. We show that these properties can be accurately estimated for black box operators using a shallow static code analysis pass based on reverse data and control flow analysis over the general-purpose code of their user-defined functions. We design and implement an optimizer for parallel data flows that does not assume knowledge of semantics or algebraic properties of operators. Our evaluation confirms that the optimizer can apply common rewritings such as selection reordering, bushy join order enumeration, and limited forms of aggregation push-down, hence yielding similar rewriting power as modern relational DBMS optimizers. Moreover, it can optimize the operator order of non-relational data flows, a unique feature among today's systems. Part 2 - Spinning Fast Iterative Data Flows, Stephan Ewen Abstract: Parallel data flow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a challenge for current systems. While certain types of bulk iterative algorithms are supported by novel data flow frameworks, these systems cannot exploit computational dependencies present in many algorithms, such as graph algorithms. As a result, these algorithms are inefficiently executed and have led to specialized systems based on other paradigms, such as message passing or shared memory. We propose a method to integrate "incremental iterations", a form of workset iterations, with parallel data flows. After showing how to integrate bulk iterations into a dataflow system and its optimizer, we present an extension to the programming model for incremental iterations. The extension alleviates for the lack of mutable state in dataflows and allows for exploiting the "sparse computational dependencies" inherent in many iterative algorithms. The evaluation of a prototypical implementation shows that those aspects lead to up to two orders of magnitude speedup in algorithm runtime, when exploited. In our experiments, the improved dataflow system is highly competitive with specialized systems while maintaining a transparent and unified data flow abstraction. Part 3 - A Taxonomy of Platforms for Analytics on Big Data, Thomas Bodner Abstract: Within the past few years, industrial and academic organizations designed a wealth of systems for data-intensive analytics including MapReduce, SCOPE/Dryad, ASTERIX, Stratosphere, Spark, and many others. These systems are being applied to new applications from diverse domains other than (traditional) relational OLAP, making it difficult to understand the tradeoffs between them and the workloads for which they were built. We present a taxonomy of existing system stacks based on their architectural components and the design choices made related to data processing and programmability to sort this space. We further demonstrate a web repository for sharing Big Data analytics platform information and use cases. The repository enables researchers and practitioners to store and retrieve data and queries for their use case, and to easily reproduce experiments from others on different platforms, simplifying comparisons.
Views: 353 Microsoft Research
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 417750 edureka!
Analyzing neurological disorders using functional and structural brain imaging data
 
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In this talk we overview methodology for predicting and analyzing clinical outcomes, especially focusing on neurological disorders, using functional and structural brain imaging data. We focus on resting state functional connectivity data via fMRI as well as structural imaging data via T1 MRI and diffusion weighted MRI. We consider these modalities and variety of methods for feature extraction, prediction and analysis. We apply the methodology to developmental disorders, particularly attention deficit hyperactivity, cognitive impairment and Alzheimer�s disease and multiple sclerosis.
Views: 839 Microsoft Research
Data analysis to improve service quality and customer satisfaction:suzuki's Group
 
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The Suzuki Laboratory is pursuing a broad range of research based on data analysis. This includes the development of statistical methodologies and applied research on quality control and marketing, as well as statistical quality control. The Suzuki Lab also does investigative research on customer relationship management, consumer behavior analysis, and sports management. Q.One of our projects is to improve customer satisfaction with pro baseball through field surveys. Our students actually do this work at ballparks, by such means as collecting opinions from spectators and conducting questionnaire surveys. By collecting and analyzing this data, we evaluate the quality of services and investigate how satisfied customers are. The first stage of data analysis is simply collecting statistics. Students design questions, then hand out questionnaires at ballparks. The students first of all propose their own hypotheses, so they can design questionnaires to see what sort of things customers want, or what sort of things should be evaluated. The data brought back to the Suzuki Lab then enters the analysis stage. The analysis method used is covariance structure analysis, an advanced form of multivariate analysis. Covariance structure analysis shows how each factor affects the results, and how the factors influence each other. For example, if a team has better fan services, customers are more inclined to support the team, watch it play, and be satisfied overall. Also, the figures show that fan services have a big impact on overall satisfaction, and that they function effectively. Results like these are displayed in graphical forms that can be understood at a glance. This makes it possible to verify, from various perspectives, what constitutes improvements in service quality and customer satisfaction, and what must be done to achieve them. The Suzuki Lab also has partnerships with a variety of businesses, and is steadily getting results through hands-on surveys and analysis. Q.We firmly believe that these results are helpful. In fact, weve actually demonstrated this already. Looking ahead, wed like to continue forming industrial-academic partnerships with businesses, so we can not only make academic contributions, but do research that is relevant to business and has a social impact as well. Data analysis methodologies evolve day by day. The Suzuki Lab makes progress by considering the social environment in which businesses operate.
R For Qualitative Analysis
 
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Introduction to R Tutorial to learn qualitative analysis package Word2Vec. Learn how to turn text into vectors and create a dendrogram and text map of your data. No prior coding experience necessary.
Views: 4721 Eren Kavvas
Stanford Seminar - Towards theories of single-trial high dimensional neural data analysis
 
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EE380: Computer Systems Colloquium Seminar Towards theories of single-trial high dimensional neural data analysis Speaker: Surya Ganguli, Stanford, Applied Physics Neuroscience has entered a golden age in which experimental technologies now allow us to record thousands of neurons, over many trials during complex behaviors, yielding large-scale, high dimensional datasets. However, while we can record thousands of neurons, mammalian circuits controlling complex behaviors can contain tens of millions of behaviorally relevant neurons. Thus, despite significant experimental advances, neuroscience remains in a vastly undersampled measurement regime. Nevertheless, a wide array of statistical procedures for dimensionality reduction of multineuronal recordings uncover remarkably insightful, low dimensional neural state space dynamics whose geometry reveals how behavior and cognition emerge from neural circuits. What theoretical principles explain this remarkable success; in essence, how is it that we can understand anything about the brain while recording an infinitesimal fraction of its degrees of freedom? We present a theory that addresses this question, and test it using neural data recorded from reaching monkeys. Overall, this theory yields a picture of the neural measurement process as a random projection of neural dynamics, conceptual insights into how we can reliably recover neural state space dynamics in such under-sampled measurement regimes, and quantitative guidelines for the design of future experiments. Moreover, it reveals the existence of phase transition boundaries in our ability to successfully decode cognition and behavior on single trials as a function of the number of recorded neurons, the complexity of the task, and the smoothness of neural dynamics. We will also discuss non-negative tensor analysis methods to perform multi-timescale dimensionality reduction and demixing of neural dynamics that reveal how rapid neural dynamics within single trials mediate perception, cognition and action, and how slow changes in these dynamics mediate learning. About the Speaker: Prof. Surya Ganguli triple majored in physics, mathematics, and electrical engineering and computer science at MIT, completed a masters in mathematics and a PhD in string theory at Berkeley, and a postdoc in theoretical neuroscience at UCSF. He is now a professor of Applied Physics at Stanford where he leads the Neural Dynamics and Computation Lab, and is also a consulting professor at the Google Brain Research Team. His research spans the fields of physics, machine learning and neuroscience, focusing on understanding and improving how both biological and artificial neural networks learn striking emergent computations. He has been awarded a Swartz-Fellowship in computational neuroscience, a Burroughs-Wellcome Career Award at the Scientific Interface, a Terman Award, NIPS Outstanding Paper Award, an Alfred P. Sloan foundation fellowship, a James S. McDonnell Foundation scholar award in human cognition, a McKnight Scholar award in Neuroscience, and a Simons Investigator Award in the mathematical modeling of living systems. For more information about this seminar and its speaker, you can visit https://ee380.stanford.edu/Abstracts/180502.html Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum. Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week. Learn more: http://bit.ly/WinYX5
Views: 906 stanfordonline
Coding Text Using Microsoft Word
 
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Describes how to use Word's comment feature to code text and then extract text segments to a table for analysis. The video uses a modified version of a Word macro available at http://www.thedoctools.com/index.php?show=mt_comments_extract
Views: 86672 Harold Peach
Data Analytics Teams and Procurement, CAPS Research
 
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Published December 2018 by CAPS Research Researchers: Benjamin Shao, Ph.D., Robert D. St. Louis, Ph.D. Procurement leaders are turning to data analytics teams to help them leverage the wealth of data available and meet business goals, but what skills should the team have? This research investigates assembling, structuring, retaining, and supporting the team. It examines how data analytics can facilitate procurement practices related to spend analysis, contract management, market intelligence, and supply chain risk. Insightful comments by CPOs at various stages of the data analytics journey offer a real-life look at how data analytics teams function and succeed in many organizations. Learn more about this study and about CAPS Research and member benefits at https://www.capsresearch.org/research/
Views: 67 CAPS Research
OpenfMRI allows neuroscientists to share brain research data - Science Nation
 
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Researchers around the world can compare notes on one of the most powerful tools available for imaging human brain function, the fMRI, thanks to support from the National Science Foundation (NSF). An fMRI is a functional magnetic resonance imaging scan that measures brain activity by detecting changes in blood oxygenation and flow. Researchers use fMRI to watch how blood flows through active areas of the brain in real time, and the scans can be used to produce "maps" of activity during a brain's thought processes. These maps change based on what a person is thinking. Globally, researchers run more than 2,000 fMRI studies every year, but currently, there is limited infrastructure for sharing results. With support from NSF's Directorate for Computer and Information Science and Engineering (CISE), cognitive neuroscientist Russell Poldrack and a team at Stanford University launched new infrastructure to enable sharing. The project, called OpenfMRI, allows scientists to share their data easily and securely in a standardized format. The advantages are clear to Stanford neuroscientist Vinod Menon, who researches brain development in children with ADHD and autism. Menon is using OpenfMRI to validate his research because he says the more fMRI scans he can analyze, the more certain he can be of his conclusions. Menon says as more studies are added to OpenfMRI, it becomes a powerful tool for diagnosing and treating neurological disorders. The research in this episode is supported by NSF grant #1131441 , CRCNS Data Sharing: An open data repository for cognitive neuroscience: The OpenfMRI Project. CRCNS stands for Collaborative Research in Computational Neuroscience. NSF Grant #/URL: http://www.nsf.gov/awardsearch/showAward?AWD_ID=1131441&HistoricalAwards=false Miles O'Brien, Science Nation Correspondent Ann Kellan, Science Nation Producer
Operations Research 05A: Sensitivity Analysis & Shadow Price
 
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Textbooks: https://amzn.to/2VgimyJ https://amzn.to/2CHalvx https://amzn.to/2Svk11k In this video, we'll talk about how to perform the sensitivity analysis and how to explain the shadow price for LP problems. ---------------------------------------- Smart Energy Operations Research Lab (SEORL): http://binghamton.edu/seorl YOUTUBE CHANNEL: http://youtube.com/yongtwang
Views: 87713 Yong Wang
Data Analysis: Graphical Representation - Mathematics - Probability and Statistics - TU Delft
 
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In this video you will learn three graphical tools, which enable you to structure data easily: histograms, kernel density estimate and empirical distribution functions. This prelecture is part of the probability and statistics courses taught at TU Delft.
Views: 3360 Mathematics TU Delft
How to Analyze Data with Minitab 17
 
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Minitab is the world's most trusted statistical software for Six Sigma and statistics education. Minitab 17 makes it easy for everyone to analyze data like an expert. http://www.minitab.com?WT_id=smy
Views: 104007 MinitabInc

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