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Learn 5 of the Hottest Analytics Techniques in Just 12 Minutes | Tutorial | Great Learning
 
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#BusinessAnalyticsTechniques | In this analytics tutorial, learn 5 of the hottest business analytics techniques in the industry: 1. Time Series Forecasting 2. Simple Linear Regression 3. Multiple Linear Regression 4. Logistic Regression 5. CART Analysis The techniques are widely used for data mining, predictive modelling across industries and very useful for professionals planning to build their career in analytics. Learn More: https://goo.gl/mYKpqu Know More about Great Lakes Analytics Programs: PG Program in Business Analytics (PGP-BABI): http://bit.ly/2f4ptdi PG Program in Big Data Analytics (PGP-BDA): http://bit.ly/2eT1Hgo Business Analytics Certificate Program: http://bit.ly/2wX42PD #BusinessAnalytics #DataScience #GreatLakes #GreatLearning About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 58666 Great Learning
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
 
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Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu
Views: 146615 YaleUniversity
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 792825 Claus Ebster
5 Analytics Tools for Tracking and Measurement
 
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You would need these five essential analytics tools in your tracking stack to be successful with taking your data to the next level. Mentioned Tools: Google Analytics - https://analytics.google.com Google Tag Manager - https://www.google.com/analytics/tag-manager/ Adobe Analytics - http://www.adobe.com/marketing-cloud/web-analytics.html Adobe DTM - https://dtm.adobe.com/sign_in Kiss Metrics - https://www.kissmetrics.com/ Mixpanel - https://mixpanel.com/ R Language - https://www.r-project.org/about.html SurveyMonkey - https://www.surveymonkey.com/ SurveyGizmo - https://www.surveygizmo.com/ Tableau - http://www.tableau.com/ Optimizely - https://www.optimizely.com/ Drip - https://www.drip.co/ Free GTM GTM Beginner course: https://gtmtraining.com/emailcourse Course: http://gtmtraining.com/products Learn more about measurement: http://measureschool.com Follow us…. https://twitter.com/measureschool https://www.facebook.com/measureschool . . RECOMMENDED MEASURE BOOKS: https://kit.com/Measureschool/recommended-measure-books GEAR WE USED TO PRODUCE THIS VIDEO: https://kit.com/Measureschool/measureschool-youtube-gear
Views: 35906 Measureschool
Ordinal Scale Data Analysis Techniques
 
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Introduction to the Mann-Whitney U, Wlcoxon and Kruskal-Wallis tests with demostrations of on-line calculators. Websites: http://www.socscistatistics.com/tests/Default.aspx http://www.mathcracker.com/kruskal-wallis.php
The Data Analysis Process
 
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The process of doing statistical analysis follows a clearly defined sequence of steps whether the analysis is being done in a formal setting like a medical lab or informally like you would find in a corporate environment. This lecture gives a brief overview of the process.
Views: 42146 White Crane Education
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: 1484220 ExcelIsFun
Analytical Thinking Techniques
 
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Learn how to boost your Analytical Thinking skills by deploying thinking techniques used by Business Data Analysts on a daily basis. Go beyond critical thinking and learn how to think analytically. Visit tranchetraining.com to sign up for our Business Data Analytics course.
Views: 22426 Sean John Thompson
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: 681456 Dr Nic's Maths and Stats
Learn Basic statistics for Business Analytics
 
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Please watch: "logistic regression case study" https://www.youtube.com/watch?v=M9Reulcqb2g --~-- Learn Basic statistics for Business Analytics Business Analytics and Data Science is almost same concept. For both we need to learn Statistics. In this video I tried to create value on most used statistical methods for Data Science or Business Analytics for Statistical model Building. Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics any can handle a scientific, industrial, or societal problem. I value your time and effort that is why I have capture almost 20 statically concept in this video. Learn Basic statistics for Business Analytics Here I have capture how to learn Mean, how to learn Mode, How to learn median, Concept of Sleekness, Concept of Kurtosis, learn Variables, concept of Standard deviation, Concept of Covariance, Concept of correlation, Concept of regression, How to read regression formula, how to read regression graph, Concept of Intercept, Concept of slope coefficient, Concept of Random Error, Different types of regression Analysis, Concept ANOVA (Analysis of Variance), How to read ANOVA table, How to learn R square (Interpreted R square), Concept of Adjusted R Square, Concept of F test, Concept of Information Value, Concept of WOE, Concept of Variable inflation Factors. Learn Basic statistics for Business Analytics By this video you can Start Learn statistics for Data Science and Business analytics easily and effectively. These statistics are useful when at the time of running linear regression, Logistic regression statistics models. For Statistical Data Exploration you may need to see Meager of central tendency and Data Spread in Statistics. By Understanding Mean, Mode, Median, Sleekness, Kurtosis, Variance, Standard deviation. Learn Basic statistics for Business Analytics To understand statistical relationship between variables you can use Covariance, Correlation coefficient, Regression , ANOVA (Analysis of Variance) . Learn Basic statistics for Business Analytics To understand Strength of stastical relationship between variables you can use R square, Adjusted R square, F test. If you want to understand variable importance in your stastical model you can use Information value (IV) and Weight of evidence (WOE) Concept. Information value and Weight of evidence mostly used in Logistic Regression Analysis. Learn Basic statistics for Business Analytics Variable inflation factors (VIF) is used for understanding, It is the stastical method to understand variable importance. What is the importance of this variable statically in the Regression model? By VIF we check Correlation between variable. Learn Basic statistics for Business Analytics At last I have explained when to use ANOVA, When to Use Linear regression and when to use Logistic regression. Learn Basic statistics for Business Analytics Thank you So much for watching this video, Hope I can add some value in your Journey as a Statistician, Business Analytics professional and Data Scientist professional. Blogger : http://koustav.analyticsanalysis.busi... google plus: https://plus.google.com/u/0/115750715 facebook link: https://www.facebook.com/koustav.biswas.31945?ref=bookmarks website: https://www.analyticsanalysisbusiness.com
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; *it surprises you; *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. 3.10. 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 This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that 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. Good luck with your study. Text and video (including audio) © Kent Löfgren, Sweden
Views: 668587 Kent Löfgren
What Techniques Do Business Analysts Use?
 
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This KnowledgeKnugget™ (KK) is part of an eCourse "Business Analysis Defined". VIEW COURSE OUTLINE at http://businessanalysisexperts.com/product/video-course-business-analysis-defined/. Also available as Paperback or Kindle eBook at http://www.amazon.com/dp/B00K7MM50O/. DESCRIPTION: Although the field of IT Business Analysis offers great career opportunities for those seeking employment, some business analysis skills are essential for any adult in the business world today. For example, the task of defining the requirements for an IT solution is handed to Business Analysts as well as Subject Matter Experts, Developers, System Analysts, Product Owners, Project Managers, Line Managers, or any other business expert. Applying business analysis techniques to define their business needs results in much higher chances for a successful IT project. In this KnowledgeKnugget™ you will learn what business analysis techniques and tools are most commonly used around the world based on surveys of actual business analysts. This KnowledgeKnugget™ answers questions like: 1. What are the primary activities in business analysis? 2. What tools or techniques do they use? To view more IT requirements training, visit the Business Analysis Learning Store at http://businessanalysisexperts.com/business-analysis-training-store/. PARTIAL TRANSCRIPT: Business analysis is the process of studying a business or any other organization to identify business opportunities / problem areas and suggest potential solutions. A wide range of people with various titles, roles and responsibilities actually apply business analysis techniques within an organization. There are three fundamentally different flavors or levels of business analysis: 1. Strategic Business Analysis (aka Enterprise Analysis) (http://businessanalysisexperts.com/strategic-business-analysis/ ) 2. Tactical Business Analysis (http://businessanalysisexperts.com/tactical-business-analysis/) 3. Operational Business Analysis (http://businessanalysisexperts.com/operational-business-analysis/Operational Business Analysis) Strategic Business Analysis is the study of business visions, goals, objectives, and strategies of an organization or an organizational unit to identify the desired future. It encompasses the analysis of existing organizational structure, policies, politics, problems, opportunities, and application architecture to build a business case for change. This analysis employs business analysis techniques such as Variance Analysis, Feasibility Analysis, Force Field Analysis, Decision Analysis, and Key Performance Indicators to support senior management in the decision-making process. The primary outcome of this work is a set of defined, prioritized projects and initiatives that the organization will undertake to create the desired future. If the initiative includes the development of software using an Agile Software Development Methodology (SDM) (http://businessanalysisexperts.com/product/business-analysis-agile-methodologies/), strategic business analysis techniques identify themes and/or epics, and initiate a product backlog. Tactical Business Analysis is at the project or initiative level to flush out the details of the proposed solution and to ensure that it meets the needs of the business community. Commonly used business analysis techniques at this level include Stakeholder Identification (http://businessanalysisexperts.com/product/how-to-identify-stakeholders-it-projects/), Interviewing (http://businessanalysisexperts.com/product/requirements-elicitation-gathering-business-stakeholder-it-requirements/), Facilitation (http://businessanalysisexperts.com/product/how-to-facilitate-requirements-gathering-workshops/), Baselining, Coverage Matrices, MoSCoW Analysis (http://businessanalysisexperts.com/product/requirements-prioritization-two-simple-techniques/), Benchmarking, Business Rules Analysis, Change Management, Process and Data Modeling (http://businessanalysisexperts.com/product/business-data-modeling-informational-requirements/), and Functional Decomposition (http://businessanalysisexperts.com/product/video-course-exposing-functional-and-non-functional-requirements/). In an Agile environment, Tactical Business Analysis adds to the Product Backlog and/or Release Plans expressed in Themes, Business Epics, Architecture Epics, User Stories (http://businessanalysisexperts.com/product/video-course-writing-user-stories/), and User Story Epics. In a traditional setting, the primary outcome of Tactical Business Analysis is a set of textual and/or modeled Business and Stakeholder Requirements (http://businessanalysisexperts.com/product/video-course-writing-requirements/). ..........
Views: 286410 BA-EXPERTS
Big Data Tools and Technologies | Big Data Tools Tutorial | Big Data Training | Simplilearn
 
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This Big Data Tools Tutorial will explain what is Big Data?, Big Data challenges and some of the popular Big Data tools involed in Big Data processing and management. The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop technologies and various Big Data tools have emerged to solve the challenges in Big Data environment. There are a lot of Big Data tools, all of them help in some or the other way in saving time, money and in covering business insights. This video will talk about such tools used in Big Data management. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 7541 Simplilearn
BroadE: Statistical methods of data analysis
 
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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: 6835 Broad Institute
Basic Statistics and Data Analysis Tools
 
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Data download: http://www.windengineering.byg.dtu.dk/download The video introduces basic methods in statistics and three Matlab scripts that can be used to analyse measured data for example from wind tunnel testing. The scripts allow basic signal processing (detrending and digital filtering), assessment of probability and spectral densities (Matlab signal processing toolbox required!), the collection of maximum and minimum extremes from sub-series for extreme value analysis, correlation between two time series and the calculation of the joint probability density function. The video is used for education at the Technical University of Denmark (DTU) in course 11374 "Seismic and Wind Engineering" and for preparation of wind tunnel testing in civil engineering. For further information see www.windengineering.byg.dtu.dk or contact the author under [email protected]
Views: 12195 Holger Koss
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: 347031 Ann K. Emery
Regression Analysis | Data Science Tutorial | Simplilearn
 
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In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Regression-Data-Science-DtOYBxi4AIE&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 3504 Simplilearn
Module 1: Data Analysis in Excel
 
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This video is part of the Analyzing and Visualizing Data with Excel course available on EdX. To sign up for the course, visit: http://aka.ms/edxexcelbi
Views: 387652 DAT206x
23C3: An Introduction to Traffic Analysis
 
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Speaker: George Danezis Attacks, Defences and Public Policy Issues... This talk will present an overview of traffic analysis techniques, and how they can be used to extract data from 'secure' systems. We will consider both state of the art attacks in the academic literature, but also practical attacks against fielded systems. A lot of traditional computer security has focused on protecting the content of communications by insuring confidentiality, integrity or availability. Yet the meta data associated with it - the sender, the receiver, the time and length of messages - also contains important information in itself. It can also be used to quickly select targets for further surveillance, and extract information about communications content. Such traffic analysis techniques have been used in the closed military communities for a while but their systematic study is an emerging field in the open security community. For more information visit: http://bit.ly/23c3_information To download the video visit: http://bit.ly/23c3_videos
Views: 2742 Christiaan008
Major in Psychological Methods and Data Analysis
 
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In the English-taught major in Psychological Methods and Data Analysis, you focus on the application of modern and more advanced methods in psychology. You explore applications used in working as a data analyst at, for example, test-and consultancy companies in health care, marketing, human resource management, and public administration.
Views: 1081 TilburgUniversity
Basic maths used in learning Analytics techniques
 
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In this video you will learn some basic math concepts which are important to know before starting to learn analytical techniques For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected] Find all free videos & study packs available with us here: http://analyticsuniversityblog.blogspot.in/ SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop
Views: 4148 Analytics University
Predictive Modelling Techniques | Data Science With R Tutorial
 
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This lesson will teach you Predictive analytics and Predictive Modelling Techniques. Watch the New Upgraded Video: https://www.youtube.com/watch?v=DtOYBxi4AIE After completing this lesson you will be able to: 1. Understand regression analysis and types of regression models 2. Know and Build a simple linear regression model 3. Understand and develop a logical regression 4. Learn cluster analysis, types and methods to form clusters 5. Know more series and its components 6. Decompose seasonal time series 7. Understand different exponential smoothing methods 8. Know the advantages and disadvantages of exponential smoothing 9. Understand the concepts of white noise and correlogram 10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc 11. Understand all the analysis techniques with case studies Regression Analysis: • Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables. • It predicts the value of a dependent variable based on one or more independent variables • Coefficient explains the impact of changes in an independent variable on the dependent variable. • Widely used in prediction and forecasting Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=Predictive-Analytics-0gf5iLTbiQM&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice. Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing. As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice. Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 202527 Simplilearn
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://github.com/datasciencedojo/meetup/tree/master/business_data_analysis_with_excel **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/H0f8xWx0 See what our past attendees are saying here: https://hubs.ly/H0f8xGd0 -- 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: 43533 Data Science Dojo
Qualitative Data Analysis - Coding & Developing Themes
 
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This is a short practical guide to Qualitative Data Analysis
Views: 98367 James Woodall
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] 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: 127492 edureka!
Basic Statistics & Quantitative Analysis I
 
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This session will provide information regarding descriptive statistics that are often used when reviewing assessment data. We will cover the statistics available in the Baseline reporting site and we will use example situations to identify which statistics should be used to answer the questions being asked. We will also provide an overview regarding levels of measurement that can help determine what types of statistics you are able to run on your data. - See more at: http://www2.campuslabs.com/support/training/basic-statistics-quantitative-analysis-i-5/#sthash.FDO5HA6i.dpuf
Views: 34026 Campus Labs
Python for Data Analysis | Python for Data Visualisation | Python Tutorial | Learn Python
 
01:06:57
#Python | Learn Data Visualisation and Data Analytics techniques using Python in a hands-on example. Know the basics of Python and how it can be used in Data analytics. Access 100s of hours of similar high-quality FREE learning content at http://greatlearningforlife.com Learn More: https://goo.gl/ufKJsH Know about our analytics programs: PGP-Business Analytics: https://goo.gl/UpQETw PGP-Big Data Analytics: https://goo.gl/9tv7Ay Business Analytics Certificate Program: https://goo.gl/9b9poE #DataVisualisation #DataAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 389196 Great Learning
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters
 
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CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters What IS DATA ANALYTICS? Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources. Types of data analytics applications : At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis. Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. At the application level, BI and reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves. Keywords: being a data analyst, big data analyst, business analyst data warehouse, data analyst, data analyst accenture, data analyst accenture philippines, data analyst and data scientist, data analyst aptitude questions, data analyst at cognizant, data analyst at google, data analyst at&t, data analyst australia, data analyst basics, data analyst behavioral interview questions, data analyst business, data analyst career, data analyst career path, data analyst career progression, data analyst case study interview, data analyst certification, data analyst course, data analyst in hindi, data analyst in india, data analyst interview, data analyst interview questions, data analyst job, data analyst resume, data analyst roles and responsibilities, data analyst salary, data analyst skills, data analyst training, data analyst tutorial, data analyst vs business analyst, data mapping business analyst, global data analyst bloomberg, market data analyst bloomberg
Views: 22723 THE MIND HEALING
Business Analytics with Excel | Data Science Tutorial | Simplilearn
 
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Business Analytics with excel training has been designed to help initiate you to the world of analytics. For this we use the most commonly used analytics tool i.e. Microsoft Excel. The training will equip you with all the concepts and hard skills required to kick start your analytics career. If you already have some experience in the IT or any core industry, this course will quickly teach you how to understand data and take data driven decisions relative to your domain using Microsoft excel. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Excel-W3vrMSah3rc&utm_medium=SC&utm_source=youtube For a new-comer to the analytics field, this course provides the best required foundation. The training also delves into statistical concepts which are important to derive the best insights from available data and to present the same using executive level dashboards. Finally we introduce Power BI, which is the latest and the best tool provided by Microsoft for analytics and data visualization. What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 18261 Simplilearn
Big Data Analysis - tools and methods
 
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A five-day summer course for working professionals. The course will bring you in the forefront of the newest tools and methods based on cutting edge research and experience. Big Data is omnipresent from industries to government and is frequently considered a completely new approach to problem solving. While the possibilities are often exaggerated, Big Data does indeed introduce new opportunities and challenges. Link: http://copenhagensummeruniversity.ku.dk/
Data Analysis Techniques
 
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In this video, Tejal Parekh - Senior Analyst at Fractal Analytics explains the techniques which help in understanding the market & its dynamics.
Views: 4467 Fractal Analytics
Introduction to Multivariate Data Analysis
 
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Brad Swarbrick, Vice President of Business Development at CAMO Software, gives a shor tintroduction to multivariate data analysis, discusses some of its applications and how these powerful analytical tools are being used to improve products and manufacturing processes in a wide range of industries. Brad Swarbrick is a pharmaceutical industry specialist for CAMO Software with over 20 years experience in the application of chemometrics techniques to spectroscopic analysers and process control systems. He was part of the Pfizer Global Process Analytical Technology (PAT) group in Australia and developed the NIR spectroscopy and PAT programs for Sigma Pharmaceuticals, Australia's largest pharmaceuticals manufacturer. For the past 3 years Brad has been based in Europe, during which time he has helped a number of leading manufacturers realize major process and quality improvements in the pharmaceutical, agricultural, chemical and downstream oil & gas industries across Europe, North America and Asia. Brad has a B.Sc (Hons) in Science and Mathematics, majoring in Chemometrics. He is the Chair of the Community of Practice in PAT, regional board director of the Australian ISPE (International Society for Pharmaceutical Engineering) affiliate, and was a member of the ASTM E55 sub-committee on PAT. Brad has been an invited expert speaker in a wide range of global conferences on PAT, NIR and Quality by Design (QbD), and has authored a number of whitepapers and peer-reviewed journal articles as well as the popular reference book Multivariate Data Analysis for Dummies. In addition to his work at CAMO, Brad has recently taken the role of pharmaceutical editor for the prestigious Journal of NIR Spectroscopy.
Views: 35014 Camo Analytics
10 Super Neat Ways to Clean Data in Excel
 
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Learn how to clean data in Excel using different ways and techniques. Data forms the backbone of any analysis that you do in Excel. And when it comes to data, there are tons of things that can go wrong – be it the structure, placement, formatting, extra spaces, and so on. Excel can be an amazing tool for data analysis. But we hardly get the data that can be used right away. And a bad data leads to bad analysis. In this video, I will show you 10 simple ways to clean data in Excel. The following topics are covered in this video: -- Get Rid of Extra Spaces -- Select and Treat All Blank Cells -- Convert Numbers Stored as Text into Numbers -- Remove Duplicates -- Highlight Errors -- Change Text to Lower/Upper/Proper Case -- Spell Check -- Delete all Formatting -- Use Find and Replace to Clean Data in Excel Read the full tutorial here: https://trumpexcel.com/clean-data-in-excel/ -~-~~-~~~-~~-~- Find Amazing Online Excel Tips and Tricks: https://trumpexcel.com/ -~-~~-~~~-~~-~- Let's Connect: Google+ ► https://plus.google.com/+Trumpexcel Facebook ► https://www.facebook.com/Trumpexcel Twitter ► https://twitter.com/TrumpExcel Pinterest ► https://in.pinterest.com/trumpexcel/ TrumpExcel Channel: https://www.youtube.com/c/trumpexcel
Views: 245506 Trump Excel
What Are The Methods Of Data Analysis In Research?
 
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Quantitative research now available sage methods. You also need to consider data interpretation and analysis. Methodology chapter of your dissertation should include discussions about the methods data analysis. Develop a research proposal methodology data analysis bcpsresearch methods and procedures used by qualitative. Approach to de synthesizing data, informational, and or factual elements answer research questions. According to shamoo and resnik (2003) various analytic procedures provide a way of drawing inductive inferences from data distinguishing the signal (the 14 jul 2015 analysis introduction. Each method has their own techniques. Collecting data is only one part of the story. It's much more difficult to define the research problem; Develop and implement a sampling plan; Conceptualize, operationalize test your measures; And develop design structure. By the time you get to analysis of your data, most really difficult work has been done. Introduction to research methods and data analysis learnonline. Data analysis has two prominent methods qualitative research and quantitative. The methods for aims 2 and 4 can be described as qualitative research, whereas the 3 5 are quantitative research. Hypothesis tests are used in everything from science and research to business economic. You have to explain in a brief manner how you are going analyze the primary data will collect employing methods explained this chapter. In qualitative research, you are either exploring the application of a theory or model in different context hoping for to emerge from data. Sampling strategies, data analysis techniques and research ethics of. All are varieties of data analysis. If you have done this work well, the in table 2, method for aim 1 was a literature review, which we already discussed. Most important methods for statistical data analysisanalysing qualitative research. Methods of data collection and analysis the open university. Ut faculties concepts of data analysissome tips for analysis. Data analysis, interpretation and presentation uio. Madhu bala, indira gandhi national open university. What i will not do to teach every bit and pieces of statistical analysisdata analysis the concept. Data analysis techniques & methods video lesson transcript data study academy. Method of data analysis is the process systematically applying statistical and or logical techniques to describe illustrate, condense recap, evaluate. It's much more difficult to define the research problem, develop and implement a sampling plan, design structure, predictive analytics focuses on application of statistical models for forecasting or classification, while text applies statistical, linguistic, structural techniques extract classify information from textual sources, species unstructured data. Because the proposed study contains both qualitative and quantitative components, an overview of sampling strategies, data analysis techniques research ethics when doing dissertation at undergra
Introduction to SPSS for data analysis: overview of SPSS
 
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Need to use SPSS for a project/dissertation? Start your journey here. In this 1st video in the series SPSS for Newbies I present an overview of SPSS, and tell you how to use my other videos. Guide to getting started with SPSS. 0:46 What does SPSS do, and can I use Excel instead? 1:13 Does it matter what version of SPSS I use? 2:02 Who uses SPSS? 3:14 How to use SPSS - (what SPSS looks like, point and click; syntax) 5:48 Managing output in SPSS 10:28 How to download a free trial of SPSS for Windows and mac 13:12 Where to go next for help/ How to analyze my data in SPSS?
Views: 338488 Phil Chan
SPSS for questionnaire analysis:  Correlation analysis
 
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Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation. 0:00 Introduction to bivariate correlation 2:20 Why does SPSS provide more than one measure for correlation? 3:26 Example 1: Pearson correlation 7:54 Example 2: Spearman (rhp), Kendall's tau-b 15:26 Example 3: correlation matrix I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation. Watch correlation and regression: https://youtu.be/tDxeR6JT6nM ------------------------- Correlation of 2 rodinal variables, non monotonic This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative. Good luck
Views: 495260 Phil Chan
TIME SERIES ANALYSIS THE BEST EXAMPLE
 
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QUANTITATIVE METHODS TIME SERIES ANALYSIS
Views: 187124 Adhir Hurjunlal
Statistical techniques in topological data analysis (English audio)
 
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Andrew Blumberg presents Statistical techniques in topological data analysis Andrew Blumberg, UT Austin
Views: 1263 INEGIInforma
5 Analytics Techniques You Must Learn Today | Cluster Analysis | Regression Analysis -Great Learning
 
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#Analytics | Know the 5 techniques in analytics which are dominating in the analytics industry and analytics job market. Learn the fundamentals and uses of analytics techniques. The video explains the fundamentals of cluster analysis, regression analysis, conjoint analysis, factor analysis and multiple discriminant analysis. Learn More about our programs: PGP- Business Analytics: https://goo.gl/dWja4e PGP-Big Data Analytics: https://goo.gl/qsg8fo Business Analytics Certificate Program: https://goo.gl/5AnwYy #ClusterAnalysis #RegressionAnalysis #GreatLakes #GreatLearning About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/
Views: 375 Great Learning
Data Analytics Overview | Data Science With Python Tutorial
 
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The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants. Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization. Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS. As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis. Python for Data Science Certification Training: http://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Introduction-Python-Data-Science-ZH13ZXh1_-w&utm_medium=SC&utm_source=youtube Who should take this course? There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. Analytics professionals who want to work with Python 2. Software professionals looking for a career switch in the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in Analytics and Data Science 5. Experienced professionals who would like to harness data science in their fields 6. Anyone with a genuine interest in the field of Data Science For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 22745 Simplilearn
Fraud Analysis and Detection: Using Benfords Law and Other Effective Techniques
 
01:52:35
NASACT, in conjunction with the Association of Government Accountants and the Association of Local Government Auditors, is pleased to announce the latest in its series of training events addressing timely issues in government auditing and financial management. Would you like to know how to mine data as part of a fraud investigation? If so, this highly interactive webinar will show you how to conduct fraud investigations using data analytics. You will also gain an understanding of the concepts behind Benford's Law and how to apply statistical tools when reviewing financial records for fraudulent activity. The training includes an interactive demonstration of Benford's Law using data provided by participants. You will also learn about other real world methods to identify outliers that could indicate fraud or performance issues. These techniques have been used by the Oregon Audits Division to help put many fraudsters behind bars. The training will include demonstrations of how to use both ACL and Excel in applying these techniques. Concepts and techniques presented in this webinar can be applied by auditors, comptrollers or treasurers – essentially anyone wishing to detect fraud in government payments or programs.
Views: 2673 NASACT
What Are The Methods Of Data Analysis?
 
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Content analysis can be used when qualitative data has been collected through interviews; Focus mixed methods will involve analyzing the from both and quantitative approaches in study. There are differences between qualitative data in your research proposal, you will also discuss how conduct an analysis of. Googleusercontent search. You have to explain in a brief manner how you are going analyze the primary data will collect employing methods explained this chapter. Which is called content analysis. An overview of market research data analysis the balance. This post would be helpful while you do your dissertation. Method of to assess the status and trends subject matters investigated research methods designs data analysis procedures employed by educational researchers, this study surveyed articles published american journal (aerj), experimental education. This section will review the commonly used methods sources of quantitative data and techniques for recruiting participants check raw anomalies prior to performing your analysis;; Re perform important calculations, such as verifying columns that are formula driven;; Confirm main totals sum subtotals;; Check relationships between numbers should analysis. There are a variety of specific data analysis method, some which include mining, text the qualitative researcher, however, has no system for pre coding, therefore method identifying and labelling or coding needs to be developed that is bespoke each research. For every research question, describe the descriptive statistic that is appropriate for concepts of data analysissome tips analysis. Data analysis techniques & methods video lesson transcript. However, each type of qualitative research requires slightly different methods data analysis ethnography the method section outlines exactly which statistic will be used to answer question and or hypothesis. Data analysis has two prominent methods qualitative research and quantitative. Approach to de synthesizing data, informational, and or factual elements answer research questions. As the researcher works lives and trends. Yet, these steps are crucial to the ability make sense out of data and cogent insightful interpretation. Methodology chapter of your dissertation should include discussions about the methods data analysis. The strategy for data analysis and the timing of may be driven by overall rationale or purpose using mixed methods such as triangulation, complementarity, development. To complete this section, refer to the research questions and hypotheses. Students are asked to apply course techniques real world problems using data, as well think creatively and critically through 6 mar 2012 this presentation summarizes qualitative data analysis methods in a brief manner. Data analysis techniques & methods video lesson transcript study academy data. It's much more difficult to define the research problem, develop and implement a sampling plan, design structure, qualitative data analysis involves s
Data Collection Methods
 
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This video was completed as part of a Masters project in DCU. It is the Introduction to a series of videos on Data Collection Methods
Views: 89351 Scott Crombie
eMammal Academy - Data Analysis
 
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Tavis Forrester, a conservation biologist with the Smithsonian Conservation Biology Institute, describes some basic techniques for analyzing camera trap data in the classroom. The video includes a basic exercise for charting data and is intended to be used in conjunction with the eMammal middle school science curriculum.
Views: 375 Smithsonian eMammal
What Are The Different Types Of Data Analysis?
 
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Aggregate data analysis is used to study current or historical events 2 jan 2014 jeff bertolucci of information week has written a new article about what distinguishes the three types big analytics descriptive, predictive, and prescriptive. Be familiar with different methods for collecting and analysing qualitative databe quantitative data many kinds of study designs monitoring, evaluation research 11 jul 2017 learn about types analytics find out which one suits your business needs best descriptive, diagnostic, predictive or prescriptive a good analysis will go beyond mere description by engaging in several the listed below, but it be weak on sociological analysis, future orientation & critique assesses ideas another social phenomenon. He writes, the majority of raw data, particularly big doesn't offer a lot value in its unprocessed state. Data analysis methods a description of the two types data statistical tests. Common methods and data analysis techniques for both quantitative qualitative 3. Googleusercontent search. The 4 types of data analytics the blog by principa. Some of them are more basic in nature, such as descriptive, exploratory, inferential, predictive, and causal. It describes the different types of variables, scales measurement, and modeling with which these variables are analyzed. Discussion 14 jul 2015 a description of the two types data analysis 'as treated' and 'intention to treat' using hypothetical trial as an examplethis is study that follows sample population over long time period; This double blind trial; Participants are randomly allocated different treatments statistical tests there wide range. The five types of analytics information builders. Of course, by applying the right set overview. Gmost bivariate analysis in social research adds on another element determining relationships between the variables themselves session agenda. Types of data analytics to improve decision making sciencesoft. Why business analytics? Review the different types of analytics & common misconceptions. The chapter reviews the differences between nonexperimental and experimental research techniques tools for data analysisin 3 of statistics in a day different combinations numbers types variables are presented. Exploratory an approach to analyzing data sets find previously unknown relationships analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, social science domains. Propose holistic approach to expand enterprise analyticsvalue of integration and data quality analytics. Techniques and tools for data analysis. Interpret the results from 22 nov 2013 a simple summary for introduction to quantitative data analysis. Review the delivery methods for operational users. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive 8 feb 2016 the big revolution has given birth to differen

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