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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: 59251 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: 154013 YaleUniversity
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 813002 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: 39182 Measureschool
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: 26164 Sean John Thompson
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: 47534 White Crane Education
Analytical Techniques Used For Big Data Visualization ll Data Analytics ll Explained in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 6189 5 Minutes Engineering
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: 4327 Analytics University
Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics]
 
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IN this Exploratory Data Analysis Tutorial, We perform predictive analytics with python by analyzing Election data from 2 candidates. Pandas data Analysis Techniques are used to learn about patterns in the election data. This is a Part of Python with Statistics Tutorial series. 🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷 Python Graph Visualization, Statistics For Data Analytics [ Python Bar Graph Example Tutorial ] https://youtu.be/3KofFIhtjNE Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI Python Describe Statistics, Exploratory Data Analysis Using Pandas & NumPy [Descriptive Statistics] https://youtu.be/6SeJH0p7n44 Data Visualization In Python, [ Plots Of Two Variables ] Statistics & Data Analysis With Python 🐍 https://youtu.be/uufMAMUEAaQ Python Graph Visualization, Exploratory Data Analysis With Pandas & Matplotlib [ Python Statistic ] https://youtu.be/Eb9eD4aNS7o Python Data Visualization [ Graphing Categorical Data ] Pandas Data Analysis & Statistics Tutorial https://youtu.be/M1h0pPFVy0E Exploratory Data Analysis In Python, Email Analytics With Pandas [ Predictive Analytics Python ] 🔴 https://youtu.be/03OJrdbhor0 Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics] https://youtu.be/sNg8VnMOAfw 🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g 📌📌📌📌📌📌📌📌📌📌
Views: 752 TheEngineeringWorld
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; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Text and video (including audio) © Kent Löfgren, Sweden
Views: 690495 Kent Löfgren
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: 9839 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: 46161 Data Science Dojo
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: 293273 BA-EXPERTS
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: 1188 TilburgUniversity
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: 1515289 ExcelIsFun
84. PMP | Perform Quantitative risk analysis tools and techniques used
 
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Lets learn about the important Tools and Techniques used in Perform Quantitative Risk Analysis in PMI PMP PMBOK based project management tutorial: Here are some Data Gathering and Representation Techniques for your reference which is part of perfom quantitative risk analysis. interviewing: Interviewing techniques draw on experience and historical data to quantify the probability and impact of risks on project objectives. The information needed depends upon the type of probability distributions that will be used. For instance, information would be gathered on the optimistic ,pessimistic, and most likely scenarios for some commonly used distributions. Here is an Example of three point estimates for cost. Probability distributions: Continuous probability distributions, which are used extensively in modelling and simulation, represent the uncertainty in values such as durations of schedule activities and costs of project components. Discrete distributions can be used to represent uncertain events, such as the outcome of a test or a possible scenario in a decision tree. Here are the two widely used continuous distribution for your reference. Beta Distribution and ,Triangular Distribution Here are the perform quantitative risk analysis another tools Quantitative Risk Analysis and Modeling Techniques. Sensitivity analysis: Sensitivity analysis helps to determine which risks have the most potential impact on the project. It helps to understand how the variations in project’s objectives correlate with variations in different uncertainties Expected monetary value analysis: Expected monetary value (EMV) analysis is a statistical concept that calculates the average outcome when the future includes scenarios that may or may not happen (i.e., analysis under uncertainty). Modeling and simulation: A project simulation uses a model that translates the specified detailed uncertainties of the project into their potential impact on project objectives. Simulations are typically performed using the Monte Carlo technique. Expert Judgment
Views: 2356 Kavin Kumar
Signal Processing and Machine Learning Techniques for Sensor Data Analytics
 
42:46
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 ------------------------------------------------------------------------- An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment. In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively. Finally, we demonstrate the use of automatic C/C++ code generation from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.
Views: 13562 MATLAB
Big Data Analytics for beginners
 
02:29
Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately – an effort that's slower and less efficient with more traditional business intelligence solutions.
Views: 84888 Hariharan
4) Next Generation Sequencing (NGS) - Data Analysis
 
07:04
For more information on Next Generation Sequencing analyses and for a list of the sources used, please visit: ➜ Knowledge Base: https://goo.gl/Ce0M4O What is covered in this video: ➜ Previous videos in our Next Generation Sequencing (NGS) series describe the theory and technology of NGS platforms (https://youtu.be/jFCD8Q6qSTM), and the steps of library preparation for sequencing on the Illumina platform (https://youtu.be/-kTcFZxP6kM). In this installment we describe some of the common formats of NGS raw data and software that can be used for downstream analysis. Watch the other videos in this series on NGS: ➜ Introduction: https://youtu.be/jFCD8Q6qSTM ➜ Sample Preparation: https://youtu.be/-kTcFZxP6kM ➜ Coverage & Sample Quality Control: https://youtu.be/PGAfwSRYv1g ➜ NGS Playlist: https://youtu.be/jFCD8Q6qSTM?list=PLTt9kKfqE_0Gem8hIcJEn7YcesuuKdt_n Connect with us on our social media pages to stay up to date with the latest scientific discoveries: ➜ Facebook: https://goo.gl/hc9KrG ➜ Twitter: https://goo.gl/gGGtT9 ➜ LinkedIn: https://goo.gl/kSmbht ➜ Google+: https://goo.gl/5bRNwC
23C3: An Introduction to Traffic Analysis
 
01:00:29
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: 2839 Christiaan008
Basic Statistics and Data Analysis Tools
 
53:55
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: 12374 Holger Koss
Techniques For Visual Data Representation ll Data Analytics ll Explained in Hindi
 
06:05
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 5871 5 Minutes Engineering
Choosing which statistical test to use - statistics help.
 
09:33
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: 715353 Dr Nic's Maths and Stats
The Complete Introduction to Business Data Analysis
 
02:15
The Complete Introduction to Business Data Analysis teaches you how to apply different methods of data analysis to turn your data into new insight and intelligence. The ability to ask questions of your data is a powerful competitive advantage, resulting in new income streams, better decision making and improved productivity. A recent McKinsey Consulting report has identified that data analysis is one of the most important skills required in the American economy at the current time. This course focuses on the following different methods of analysis. During the course you will understand why the form of analysis is important and also provide examples of using the analysis using Excel 2013. The methods of analysis covered include Comparison Analysis, Trend Analysis, Ranking Analysis, Interactive Dashboards, Contribution Analysis, Variance Analysis, Pareto Analysis, Frequency Analysis and Correlations The Complete Introduction to Business Data Analysis is designed for all business professionals who want to take their ability to turn data into information to the next level. If you are an Excel user then you will want to learn the easy to use techniques that are taught in this course. This course is presented using Excel 2013. Excel 2010 can be used for the majority of the training exercises. Small parts of the course do use Excel Power Pivot and Power View. Please note that this course does not include any complicated formulas, VBA or macros. The course utilizes drag and drop techniques to create the majority of the different data analysis techniques.
Views: 3068 Data Insight Training
Big Data Analysis - tools and methods
 
02:24
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/
BroadE: Statistical methods of data analysis
 
01:02:58
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: 6989 Broad Institute
Introduction to Data Science with R - Data Analysis Part 1
 
01:21:50
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 908731 David Langer
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/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube
Views: 391512 Great Learning
Qualitative Data Analysis - Coding & Developing Themes
 
10:39
This is a short practical guide to Qualitative Data Analysis
Views: 107948 James Woodall
Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Training | Edureka
 
40:38
( 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: 144580 edureka!
Statistics For Data Science | Data Science Tutorial | Simplilearn
 
20:17
Statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world. Statistics is generally regarded as one of the pillars of data science. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Statistics-Lv0xcdeXaGU&utm_medium=SC&utm_source=youtube 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: 30001 Simplilearn
Panel Data Analysis | Econometrics | Fixed effect|Random effect | Time Series | Data Science
 
58:44
This video is on Panel Data Analysis. Panel data has features of both Time series data and Cross section data. You can use panel data regression to analyse such data, We will use Fixed Effect Panel data regression and Random Effect panel data regression to analyse panel data. We will also compare with Pooled OLS , Between effect & first difference estimation For Analytics study packs visit : https://analyticuniversity.com Time Series Video : https://www.youtube.com/watch?v=Aw77aMLj9uM&t=2386s Logistic Regression using SAS: https://www.youtube.com/watch?v=vkzXa0betZg&t=7s Logistic Regression using R : https://www.youtube.com/watch?v=nubin7hq4-s&t=36s Support us on Patreon : https://www.patreon.com/user?u=2969403
Views: 66029 Analytics University
Predictive Modelling Techniques | Data Science With R Tutorial
 
03:10:36
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: 206345 Simplilearn
Basic Data Analysis in RStudio
 
25:56
This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis. You may also be interested in how to use tidyverse functionality for basic data analysis: https://youtu.be/xngavnPBDO4
Views: 123828 Ralf Becker
Module 1: Data Analysis in Excel
 
10:40
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: 400537 DAT206x
An Introduction to Linear Regression Analysis
 
05:18
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: 688050 statisticsfun
Data Collection Methods
 
02:10
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: 93759 Scott Crombie
Collection of Data| आकड़ों  का संकलन Part 1 of 5 by Vijay Adarsh | Stay Learning | (HINDI)
 
17:01
Collection of Data (आकड़ों का संकलन) Data are collected by individual research workers or by organization through sample surveys or experiments, keeping in view the objectives of the study. The data collected may be: 1) Primary Data 2) Secondary Data 1) Primary data Primary data means the raw data which has just been collected from the source and has not gone any kind of statistical treatment like sorting and tabulation. 2) Secondary Data Data which has already been collected by someone, may be sorted, tabulated and has undergone a statistical treatment. It is fabricated or tailored data. 🔴 Buy Now all Video Lectures - http://www.vijayadarsh.com 🔴 Join us on Facebook: https://www.facebook.com/VijayAdarshIndia 🔴 Join us on Google+: https://plus.google.com/u/0/+VIJAYADARSH 🔴 Website: http://www.vijayadarsh.com 🔴 E-mail: [email protected] 🔴 Contact: +91 9268373738 (Buy Now all Video Lectures) About Video Lectures: Video Lectures for Economics for Class 11th by Vijay Adarsh evolved as utility services for our own students. These are our classroom lectures which form a very good source of study material. LEARN AT YOUR OWN TIME : OWN SPEED : OWN PLACE StayLearning provides video lectures of Accountancy, Micro & Macro Economics, Mathematics, Income Tax, Corporate Accounting, Business Mathematics, Business Statistics, Cost Accounting, Financial Management (FM), for Class 9, 10, 11, 12, B.Com (Hons/Prog) – First Year (FY), Second Year (SY) and Third Year (TY), M.Com, MBA Examination by the best & renowned teachers. The Lectures Covers in full depth, the description of all the involved concepts. Studying through lectures largely reduces the need of individual tuition. Lectures can be use at a pace which suits us. Students can pause and rewind the lectures according to their need. Complete practice tests and solutions of every topic would also be provided. 🔴 Visit Now : http://www.vijayadarsh.com 🔴 Contact: +91 9268373738 (Buy Now all Video Lectures)
Views: 97714 StayLearning
(18E) Analysis of Likert Data
 
16:07
Session 18: Descriptive Statistics: Summarising and Visualising Data Fifth Video
Views: 43281 Anthony Kuster
Ways to represent data | Data and statistics | 6th grade | Khan Academy
 
08:18
Here are a few of the many ways to look at data. Which is your favorite? Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/dot-plot/e/intro-to-simple-data?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/dot-plot/v/frequency-tables-and-dot-plots?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-geometry-topic/cc-6th-polygons-in-the-coordinate-plane/v/constructing-polygon-on-coordinate-plane-example?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) 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 Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 417230 Khan Academy
Data Preprocessing Steps for Machine Learning & Data analytics
 
03:50
#Pandas #DataPreProcessing #MachineLearning #DataAnalytics #DataScience Data Preprocessing is an important factor in deciding the accuracy of your Machine Learning model. In this tutorial, we learn why Feature Selection , Feature Extraction, Dimentionality Reduction are important. We also learn about the famous methods which can be used for the purpose. Data Preprocessing is a very important step in Data Analytics which is ignored by many. To make your models accurate you have to ensure proper preprocessing as the Machine Learning model is highly dependent on data. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Python for Data Analysis book : http://amzn.to/2oDief8 Pattern Recognition and Machine Learning : http://amzn.to/2p6mD6R
Views: 11724 The SemiColon
Basic Statistics & Quantitative Analysis I
 
55:06
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: 34691 Campus Labs
Python Pandas ||  Data Analysis Fundamentals || Data Analytics || Python Programming
 
17:04
http://alphabench.com/data/data-analysis-python.html ***NOTE pandas_datareader is no longer able to download data from Yahoo. I recommend installing fix_yahoo_finance. See the tutorial: https://alphabench.com/data/python-fix-yahoo-finance-tutorial.html Or, Open a command window and enter the following: pip install fix_yahoo_finance Once complete, open Python or start a notebook and: import fix_yahoo_finance as fyf data = fyf.download(stock_symbol(s), start*, end*) *optional Video tutorial that discusses fundamental data analysis techniques using stock price data from Amazon. Makes use of Python 3.6 and several supporting libraries including Pandas, Pandas Datareader and Matplotlib. To install an environment similar to that used here install the Anaconda scientific platform, free download from: https://www.continuum.io/ The jupyter notebook used in this tutorial can be downloaded by visiting: https://nbviewer.jupyter.org/url/alphabench.com/data/Python-Basic-Data-Analysis.ipynb
Views: 7709 Matt Macarty
Business Analytics with Excel | Data Science Tutorial | Simplilearn
 
42:30
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: 23213 Simplilearn