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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: 59925 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: 163300 YaleUniversity
Analytical Techniques Used For Big Data Visualization ll Data Analytics ll Explained in Hindi
 
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Views: 6444 5 Minutes Engineering
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: 54289 White Crane Education
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: 748953 Dr Nic's Maths and Stats
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 usโ€ฆ. 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: 42480 Measureschool
Top Data Analytics Skills You Should Know (Career Insights)
 
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Thanks to the digital revolution, analytics is sweeping across industries in a huge way. Mastering certain data analytics skills can enable you to chart a successful career in this lucrative and rapidly changing domain. Data analytics is changing the way we live - from that app you use to navigate to work everyday or the cabs you hail through your phone, or the platforms you order food from, to the online shopping you find yourself doing on weekends. All of this activity generates massive amounts of data. This is where companies who have created these products come in. Analytics is changing the way we also do business. Deriving insights from large volumes of data to enable better decision-making and an even better customer experience has become the norm for competitive firms these days. Which is why being a data analyst in this world pays off well. Through this UpGrad Careers-In-Shorts Series, Rohit Sharma, Program Director at UpGrad, takes you through all you need to know about data analytics - the most promising career of tomorrow! The first one here is about the 4 core skills that will help you transition to the field of data analytics - a career of the future. Want to Be a Data Analyst? Here are Top Skills & Tools to Master: https://blog.upgrad.com/want-to-be-a-data-analyst-here-are-top-skills-tools-to-master/?utm_source=YouTube&utm_medium=Organic_Social&utm_campaign=YouTube_Video&utm_term=YouTube_Video_Data&utm_content=YouTube_Video_Data_Analytics_Skills_Blog_Link Transition to one of the coolest jobs in industry. Enroll now to be an expert Data Analyst: https://upgrad.com/data-science/?utm_source=YouTube&utm_medium=Organic_Social&utm_campaign=YouTube_Video&utm_term=YouTube_Video_Data&utm_content=YouTube_Video_Data_Analytics_Skills UpGrad takes pride in constantly churning out content that is contemporary, written by subject matter experts and delves into the world of Data Science, Big Data, Digital Marketing, Entrepreneurship, Product Management, Machine Learning and Artificial Intelligence, Software Development on regular basis. Stay on top of your industry by interacting with us on our social channels: Follow us on Instagram: https://instagram.com/upgrad_edu Like us on Facebook: https://www.facebook.com/UpGradGlobal Follow us on Twitter: https://www.twitter.com/upgrad_edu Follow us on LinkedIn: https://in.linkedin.com/company/ueducation
Views: 46760 upGrad
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Letโ€™s go! For more videos like this, Iโ€™d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data youโ€™ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and Chinaโ€™s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - itโ€™s at https://www.csdojo.io/moredata
Views: 231064 CS Dojo
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: 1550916 ExcelIsFun
Data Analysis in Excel Tutorial
 
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Data Analysis using Microsoft Excel using SUMIF , CHOOSE and DATE Functions
Views: 97512 TEKNISHA
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
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 833469 Claus Ebster
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) ยฉ Kent Lรถfgren, Sweden
Views: 723953 Kent Lรถfgren
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: 30036 Sean John Thompson
SPSS: How To Perform Quantitative Data Analyses For Bachelor's Research? 5 Basic Analysis Methods
 
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1. Descriptives: 1:32 2. T test: 2:52 3. Correlation: 4:41 4. Chi square: 5:39 5. Linear regression: 6:45 This video discusses the basic statistical analytical procedures that are required for a typical bachelor's thesis. Five stats are highlighted here: descriptives, T test, correlation, Chi square, and linear regression. For requirements on reporting stats, please refer to the appendix of your research module manuals -- Frans Swint and I wrote an instructional text on APA reporting of stats. There is no upper limit in terms of how advanced your stats should be in your bachelor's dissertation. This video covers the basic procedures and is not meant to replace the instructions of your own research supervisor. Please consult your own research advisor for specific questions regarding your data analyses. Please LIKE this video if you enjoyed it. Otherwise, there is a thumb-down button, too... :P โ–ถ Please SUBSCRIBE to see new videos (almost) every week! โ—€ โ–ผMY OTHER CHANNEL (MUSIC AND PIANO TUTORIALS)โ–ผ https://www.youtube.com/ranywayz โ–ผMY SOCIAL MEDIA PAGESโ–ผ https://www.facebook.com/ranywayz https://nl.linkedin.com/in/ranywayz https://www.twitter.com/ranywayz Animations are made with Sparkol. Music files retrieved from YouTube Audio Library. All images used in this video are free stock images or are available in the public domain. The views expressed in this video are my own and do not necessarily reflect the organizations with which I am affiliated. #RanywayzRandom #SPSS #Research
Views: 5861 Ranywayz Random
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: 12708 Simplilearn
What Techniques Do Business Analysts Use?
 
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This KnowledgeKnuggetโ„ข (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 KnowledgeKnuggetโ„ข you will learn what business analysis techniques and tools are most commonly used around the world based on surveys of actual business analysts. This KnowledgeKnuggetโ„ข 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: 300625 BA-EXPERTS
Statistics For Data Science | Data Science Tutorial | Simplilearn
 
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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: 35382 Simplilearn
Regression Analysis | Data Science Tutorial | Simplilearn
 
06:50
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: 5445 Simplilearn
Signal Processing and Machine Learning Techniques for Sensor Data Analytics
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- 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: 15681 MATLAB
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: โ€ข Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables. โ€ข It predicts the value of a dependent variable based on one or more independent variables โ€ข Coefficient explains the impact of changes in an independent variable on the dependent variable. โ€ข 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: 211389 Simplilearn
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: 12503 Holger Koss
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
 
01:13:27
( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course โ€œData Scienceโ€. 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: 104704 edureka!
Business Data Analysis with Excel
 
01:46:44
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 โ€“ no programming or statistics required! Dave will cover the following during the presentation: โ€ข The types of business data and why business data is a unique analytical challenge. โ€ข Requirements for robust business data analysis. โ€ข Using histograms, running records, and process behavior charts to analyze business data. โ€ข The rules of trend analysis. โ€ข How to properly compare business data across time, organizations, geographies, etc.Where you can learn more about the tools and techniques. *Excel spreadsheets can be found here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Business%20Data%20Analysis%20with%20Excel **Find out more about David here: https://www.meetup.com/data-science-dojo/events/236198327/ -- Learn more about Data Science Dojo here: https://hubs.ly/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: 48979 Data Science Dojo
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. ๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python ๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท 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] ๐Ÿผ https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python ๐Ÿ๐Ÿผ 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 ๐Ÿ 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 ] ๐Ÿ”ด https://youtu.be/03OJrdbhor0 Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics] https://youtu.be/sNg8VnMOAfw ๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท๐Ÿ”ท *** 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 ๐Ÿ“Œ๐Ÿ“Œ๐Ÿ“Œ๐Ÿ“Œ๐Ÿ“Œ๐Ÿ“Œ๐Ÿ“Œ๐Ÿ“Œ๐Ÿ“Œ๐Ÿ“Œ
Views: 1211 TheEngineeringWorld
Qualitative Data Analysis - Coding & Developing Themes
 
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This is a short practical guide to Qualitative Data Analysis
Views: 122672 James Woodall
84. PMP | Perform Quantitative risk analysis tools and techniques used
 
05:10
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 projectโ€™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: 2639 Kavin Kumar
Introduction to Statistics..What are they? And, How Do I Know Which One to Choose?
 
39:54
This tutorial provides an overview of statistical analyses in the social sciences. It distinguishes between descriptive and inferential statistics, discusses factors for choosing an analysis procedure, and identifies the difference between parametric and nonparametric procedures.
Views: 231603 The Doctoral Journey
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: 415020 DAT206x
How to tabulate, analyze, and prepare graph from Likert Scale questionnaire data using Ms Excel.
 
13:16
This video describes the procedure of tabulating and analyzing the likert scale survey data using Microsoft Excel. This video also explains how to prepare graph from the tabulated data. Photo courtesy: http://littlevisuals.co/
Views: 107397 Edifo
Ordinal Scale Data Analysis Techniques
 
27:02
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
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: 30352 Simplilearn
Simple Data Analysis for Teachers Using Excel
 
04:50
Exploring some basic data analysis in excel
Views: 47059 Jon Jasinski
Basic maths used in learning Analytics techniques
 
15:26
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: 4471 Analytics University
Data Analysis Techniques
 
04:55
This training seminar aims to provide those involved in analysing numerical data with the understanding and practical capabilities needed to convert data into information via appropriate analysis, and then to represent these results in ways that can be readily communicated to others in the organisation. Read More: http://glomacs.ae/seminars/data-analysis-techniques
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: 7161 Broad Institute
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: 72088 Analytics University
Solving Data Interpretation Problems- Tricks, Techniques, Visualization and Imagination
 
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Dr. Manishika Jain in this video focuses on solving data interpretation problems mainly finding way out for approximations, solving bar graphs, tables and pie charts by imagination and visualization. For more details and elaborate solutions to problems visit https://www.doorsteptutor.com/Exams/ Types of Questions @0:38 Themes for Trick Analysis @0:59 Doing Approximation โ€“ Game of Zeroโ€™s @3:11 Donโ€™t Simplify Fractions โ€“ Until Necessary @10:45 Average @14:11 Pie Diagram @15:56 #Tricks #Imagination #Fractions #Necessary #Interpretation #Approximation #Scatter #Visualization #Manishika #Examrace NTA NET Online Crash Course - https://www.doorsteptutor.com/Exams/UGC/Paper-1/Online-Crash-Course/ NTA NETMock papers - https://www.doorsteptutor.com/Exams/UGC/Paper-1/Online-Test-Series/ NET Practice questions - https://www.doorsteptutor.com/Exams/UGC/Paper-1/Questions/ NET Postal Course - https://www.examrace.com/NTA-UGC-NET/NTA-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-NTA-UGC-NET-Paper-I-Series.htm Examrace is number 1 education portal for competitive and scholastic exam like UPSC, NET, SSC, Bank PO, IBPS, NEET, AIIMS, JEE and more. We provide free study material, exam & sample papers, information on deadlines, exam format etc. Our vision is to provide preparation resources to each and every student even in distant corders of the globe. Dr. Manishika Jain served as visiting professor at Gujarat University. Earlier she was serving in the Planning Department, City of Hillsboro, Hillsboro, Oregon, USA with focus on application of GIS for Downtown Development and Renewal. She completed her fellowship in Community-focused Urban Development from Colorado State University, Colorado, USA. For more information - https://www.examrace.com/About-Examrace/Company-Information/Examrace-Authors.html data interpretation techniques data interpretation basics data interpretation pie chart data interpretation youtube data interpretation books data interpretation test data interpretation for ugc net data interpretation infosys
Views: 232125 Examrace
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 โ€“ 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+ โ–บ https://plus.google.com/+Trumpexcel Facebook โ–บ https://www.facebook.com/Trumpexcel Twitter โ–บ https://twitter.com/TrumpExcel Pinterest โ–บ https://in.pinterest.com/trumpexcel/ TrumpExcel Channel: https://www.youtube.com/c/trumpexcel
Views: 431535 Trump Excel
Data Science With Python | Python for Data Science | Python Data Science Tutorial | Simplilearn
 
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This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis. This Data Science with Python tutorial will cover the following topics: 1. What is Data Science? 2. Basics of Python for data analysis - Why learn Python? - How to install Python? 3. Python libraries for data analysis 4. Exploratory analysis using Pandas - Introduction to series and dataframe - Loan prediction problem 5. Data wrangling using Pandas 6. Building a predictive model using Scikit-learn - Logistic regression To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/ifQRpS Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into 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 Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-With-Python-mkv5mxYu0Wk&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 72426 Simplilearn
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 738220 statisticsfun
Fundamentals of Qualitative Research Methods: What is Qualitative Research (Module 1)
 
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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 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 1. Patton M. Qualitative Research and Evaluation Methods, 3rd edition. Sage Publishers; 2002. Curry L, Nembhard I, Bradley E. Qualitative and mixed methods provide unique contributions to outcomes research. Circulation, 2009;119:1442-1452. Crabtree, B. & Miller, W. (1999). Doing qualitative research, 2nd edition. Newbury Park, CA:Sage. Schensul S, Schensul J. and Lecompte M. 2012 Initiating Ethnographic research: A mixed Methods Approach, Altamira press. 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: 215003 YaleUniversity
Data Science In 5 Minutes | Data Science For Beginners | What Is Data Science? | Simplilearn
 
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This Data Science tutorial video will give you an idea on the life of a Data Scientist, steps involved in Data science project, roles & salary offered to a Data Scientist. Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data Science is basically dealing with unstructured and structured data. Data Science is a field that comprises of everything that is related to data cleansing, preparation, and analysis. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data. Now, let us get started and understand what is Data Science all about. Below topics are explained in this Data Science tutorial: 1. Life of a Data Scientist 2. Steps in Data Science project - Understanding the business problem - Data acquisition - Data preparation - Exploratory data analysis - Data modeling - Visualization and communication - Deploy & maintenance 3. Roles offered to a Data Scientist 4. Salary of a Data Scientist To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, youโ€™ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearnโ€™s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into 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 Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-X3paOmcrTjQ&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearnโ€™s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 181518 Simplilearn
4) Next Generation Sequencing (NGS) - Data Analysis
 
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For more information on Next Generation Sequencing analyses and for a list of the sources used, please visit: โžœ Knowledge Base: https://goo.gl/Ce0M4O What is covered in this video: โžœ 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: โžœ Introduction: https://youtu.be/jFCD8Q6qSTM โžœ Sample Preparation: https://youtu.be/-kTcFZxP6kM โžœ Coverage & Sample Quality Control: https://youtu.be/PGAfwSRYv1g โžœ 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: โžœ Facebook: https://goo.gl/hc9KrG โžœ Twitter: https://goo.gl/gGGtT9 โžœ LinkedIn: https://goo.gl/kSmbht โžœ Google+: https://goo.gl/5bRNwC
Basic Data Analysis in RStudio
 
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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: 131911 Ralf Becker
Gene expression analysis
 
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This molecular genetics lecture explains about gene expression analysis techniques using DNA chip technology and microarrays. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html Question source - www.indiabix.com
Views: 84676 Shomu's Biology
Spatial Data Mining I: Essentials of Cluster Analysis
 
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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 26502 Esri Events