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Search results “Data mining in market research”
Marketing Data Mining
 
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http://fun-factory-store.net/blog/ Market Data Mining Before I show you how to properly begin to research your specific markets, I want to give you access to specific sites that give you some information about the possible markets that you're about to go into. We will be looking at specific market data mining sites and resources.
Views: 806 Cheap Domains
Hidden connections - Data analysis in brain and supermarket
 
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Neuroscientist Sonja Grün uses methods from retailing market research to understand how neurons cooperate.The statistical method known as "frequent itemset mining" (FIM) finds groups of objects in large volumes of data quickly and efficiently and counts their frequencies. In retailing market research, this is used, for example, to identify products that are often purchased together. In brain research, a modified version of the FIM method helps to distinguish behaviour-dependent activity patterns from random patterns. This enabled Jülich scientists to establish which of the simultaneously active neurons form a functional group, for instance while the eye focuses on a given object. (mb) A film by Johannes Faber and Gunnar Grah
AI for Marketing & Growth #1 - Predictive Analytics in Marketing
 
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AI for Marketing & Growth #1 - Predictive Analytics in Marketing Download our list of the world's best AI Newsletters 👉https://hubs.ly/H0dL7N60 Welcome to our brand new AI for Marketing & Growth series in which we’ll get you up to speed on Predictive Analytics in Marketing! This series you-must-watch-this-every-two-weeks sort of series or you’re gonna get left behind.. Predictive analytics in marketing is a form of data mining that uses machine learning and statistical modeling to predict the future. Based on historical data. Applications in action are all around us already. For example, If your bank notifies you of suspicious activity on your bank card, it is likely that a statistical model was used to predict your future behavior based on your past transactions. Serious deviations from this pattern are flagged as suspicious. And that’s when you get the notification. So why should marketers care? Marketers can use it to help optimise conversions for their funnels by forecasting the best way to move leads down the different stages, turning them into qualified prospects and eventually converting them into paying customers. Now, if you can predict your customers’ behavior along the funnel, you can also think of messages to best influence that behavior and reach your customer’s highest potential value. This is super-intelligence for marketers! Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This’ll allow you to focus your team’s efforts on leads with the highest ROI. Which will also imply a shift in mindset. Going from quantity metrics, or how many leads you can attract, to quality metrics, or how many good leads you can engage. You can now easily predict your OMTM or KPIs in real-time and finally push vanity metrics aside. For example, based on my location, age, past purchases, and gender, how likely are you to buy eggs I if you just added milk to your basket? A supermarket can use this information to automatically recommend products to you A financial services provider can use thousands of data points created by your online behaviour to decide which credit card to offer you, and when. A fashion retailer can use your data to decide which shoes to recommend as your next purchase, based on the jacket you just bought. Sure, businesses can improve their conversion rates, but the implications are much bigger than that. Predictive analytics allows companies to set pricing strategies based on consumer expectations and competitor benchmarks. Retailers can predict demand, and therefore make sure they have the right level of stock for each of their products. The evidence of this revolution is already around us. Every time we type a search query into Google, Facebook or Amazon we’re feeding data into the machine. The machine thrives on data, growing ever more intelligent. To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place. 1. The right questions 2. The right data 3. The right technology 4. The right people Ok.. let’s look at some use cases of businesses that are already leveraging predictive analytics. Other topics discussed: Ai analytics case study artificial intelligence big data deep learning demand forecasting forecasting sales machine learning predictive analytics in marketing data mining statistical modelling predict the future historical data AI Marketing machine learning marketing machine learning in marketing artificial intelligence in marketing artificial intelligence AI Machine learning ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/company/growth-tribe Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Snapchat: growthtribe Video URL: https://youtu.be/uk82DHcU7z8
Views: 11289 Growth Tribe
Data Mining Marketing Research ChannelAide
 
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http://www.channelaide.com/ marketing research done for your online selling
Views: 194 Mike Gerts
15 Hot Trending PHD Research Topics in Data Mining 2018
 
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15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 1432 PhD Assistance
"Brain + Data: How Neuroscience Can Increase Data Mining Effectiveness", Dr. Jonathan T. Mall
 
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"Brain + Data: How Neuroscience Can Increase Data Mining Effectiveness", Dr. Jonathan T. Mall, CEO of NeuroFlash Slides can be found here: http://bit.ly/2eXnS6a Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS About the Author: Jonathan is a computational Neuropsychologist turned serial entrepreneur. Seduced by the opportunity to optimize consumer experience using machine learning, he led the Science team in a dutch IBM Big Data Venture (Gumbolt.com). Afterwards, he Co-Founded Neuro-Flash.com a market research institute, using online experiments that illuminate the true drivers of desire and purchase behaviour. When he’s not combining Neuroscience and Big-Data to test innovative ideas, he likes burgers and a friendly match of badminton.
Views: 401 Data Natives
The Logic of Data Mining in Social Research
 
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This video is a brief introduction for undergraduates to the logic (not the nitty-gritty details) of data mining in social science research. Four orienting tips for getting started and placing data mining in the broader context of social research are included.
Views: 271 James Cook
BIG DATA FOR SALES & MARKETING - FIND OUT
 
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Discover sales leads, ideal buyer contacts, technology installations, competitive intelligence and sales trigger events.
Views: 4180 Corporate360
How Marketing Data Analysts Make It Work - funny video
 
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More data doesn’t necessarily mean more intelligence when trying to make sense of media performance. Watch these marketing analysts take an innovative approach to analyzing cross channel data. Find out how advanced attribution can help you manage data better to deliver RADICAL marketing results at http://getradicalresults.com/.
Views: 6737 VisualIQInc
R for Marketing Research and Analytics
 
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For more workshops, please visit: http://scientistcafe.com. For future workshops, you can follow twitter: @gossip_rabbit or join our meetup group: http://www.meetup.com/Central-Iowa-R-User-Group/ In the past, marketing has been thought of as a "non-quantitative" domain and few marketers have been trained comprehensively in statistical methods. Much of the sophisticated analysis that is done in marketing is done by specialists using specialized software. In an effort to change this, Chris Chapman and Elea McDonnell Feit wrote R for Marketing Research and Analytics, the first text to teach R specifically for marketers from basics through advanced applications. In this talk, Chris and Elea will give an introduction to R for marketing researchers.
Views: 5977 Hui Lin
Why Hire out Data Mining Service?
 
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Data mining is one of the unique techniques for analyzing information to extract certain data patterns and make a decision to an outcome of the existing requirements. Data mining is widely used in client research, services analysis, market research and so on. It is totally based on a mathematical algorithm and analytical abilities to drive the desired outcomes from the huge data resource collection. For more details contact us at https://www.dataplusvalue.com/data-mining-services-india.html or call us at +91 8377013007
Views: 2 DataPlus Value
How to do Data Mining for Cold calling and Email Marketing
 
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In this Video I can Show you, How you can do the Data Mining from the Yellow pages with help of Google Places api and postman api tester. # Software we have Used 1) Postman Api tester Link: https://www.getpostman.com/ 2) Online Json to CSV Converter Link: http://www.convertcsv.com/ 3) to Find Domain Registrants Link: www.whois.com # Video Editing on Sony Vegas Pro 13. by Ankit Shah # Music Royaltee free Track: bensound-littleidea Contact Me Name: Ankit Shah Email: [email protected]
Views: 3598 ImAnkitShah
Social Media Marketing and Management - Data Mining - Text Mining - Sentimental Analysis
 
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-Explanation: A Social Media Marketing and Management Project -Lesson: Data Mining -Subject: Sentimental Analysis ( Emotional Analysis ) of Text Mining ----------- -Açıklama: Sosyal Medya ve Pazarlama Uygulaması Projesi -Ders: Veri Madenciliği -Konu: Duygusal Metin Analizi
Views: 156 Egemen Kayalidere
Customer Segmentation in Python - PyConSG 2016
 
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Speaker: Mao Ting Description By segmenting customers into groups with distinct patterns, businesses can target them more effectively with customized marketing and product features. I'll dive into a few machine learning and statistical techniques to extract insights from customer data, and demonstrate how to execute them on real data using Python and open-source libraries. Abstract I will go through clustering and decision tree analysis using sciki-learn and two-sample t test using scipy. We will learn the intuition for each technique, the math behind them, and how to implement them and evaluate the results using Python. I will be using open-source data for the demonstration, and show what insights you can extract from actual data using these techniques. Event Page: https://pycon.sg Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/P6SD/
Views: 11800 Engineers.SG
Predicting Stock Prices - Learn Python for Data Science #4
 
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In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Views: 461857 Siraj Raval
Buzz 3D Planograms, Virtual Market Research, Virtual Pack Testing and Data Mining
 
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The Buzz 3D suite of Virtual Reality applications for 3D Planogram, Virtual Store Creation, Market Research, Pack Testing and Data Mining are an ideal range of tools to take into a Retail Client meeting if you have a key concept you'd like to see deployed in the real world. The technology can be used to generate all the data you need to both visualise and prove that your concept can be successful, and this creates a compelling body of evidence in your favour. http://www.buzz3d.com/3d_planogram_virtual_store_index.html
Views: 497 Buzz 3D
Trade Miner Review By Mike At Orderflows Market Analysis Data Mining Software
 
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http://www.orderflows.com/2017/02/12/find-high-percentage-trades-quickly/ I just wanted to tell you about this market software research tool you might be interested in. Everyone talks about finding high percentage trades, the trades with the 4-1 reward to risk ratio and so on then just leave you to it. What this software does is it finds you high percentage trades and show you the success rate in the past, the average profit per trade and much more. At a click of a button you will know what futures contract, stock or forex pair to buy (or sell), when to buy (or sell), the trade's winning percentage and average profit. Recently I did a webinar about it explaining how a trader can use it to research and find very profitable trades. You can watch the replay here and learn more about the software. One of the best part is the price. All too often software comes on the market for traders and it is priced at $500, $1000 and more, but this software you can get for as low as $97, but honestly there is a better deal which I tell you about to save you a little more money! It is very rare that I promote another trading software product but I have to be honest and tell you that this software can really open your eyes to some amazing trading opportunities. CFTC Rules 4.41: Hypothetical or Simulated performance results have certain limitations, unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not been executed, the results may have under-or-over compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profit or losses similar to those shown. Disclaimer: This presentation is for educational and informational purposes only and should not be considered a solicitation to buy or sell a futures contract or make any other type of investment decision. Futures trading contains substantial risk and is not for every investor. An investor could potentially lose all or more than the initial investment. Risk capital is money that can be lost without jeopardizing ones financial security or life style. Only risk capital should be used for trading and only those with sufficient risk capital should consider trading. Past performance is not necessarily indicative of future results.  Risk Disclosure: Futures and forex trading contains substantial risk and is not for every investor. An investor could potentially lose all or more than the initial investment. Risk capital is money that can be lost without jeopardizing ones’ financial security or life style. Only risk capital should be used for trading and only those with sufficient risk capital should consider trading. Past performance is not necessarily indicative of future results. Hypothetical Performance Disclosure: Hypothetical performance results have many inherent limitations, some of which are described below. no representation is being made that any account will or is likely to achieve profits or losses similar to those shown; in fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk of actual trading. for example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses are material points which can also adversely affect actual trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results and all which can adversely affect trading results.
Views: 2163 Order Flows
Text Analytics in Marketing Research
 
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Text Analytics in Marketing Research, Big Data, and Data Science - on Boss Academy
Views: 88 OdinText
Data Analysis Using R - Session 1 - Bank Marketing
 
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Data Analysis By using Bank Marketing data
Views: 6270 Naveen Balawat
What Is Traditional Market Research?
 
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Examples of data mining vsyour traditional market research can kill innovation. Following 21 jun 2016 there are two schools of thought on market research those who consider traditional valid and it outdated is any organized effort to gather information about target markets or customers. Also influenced high street modes of data collection by, for example, replacing the traditional paper clipboard with online survey providers 31 jan 2014 lag in getting actionable insights. Traditional marketing research advantages & disadvantages of traditional market experts debate vs. Can social media analytics replace traditional market research? . Traditional marketing research advantages & disadvantages of traditional market upfrontanalytics and url? Q webcache. Why traditional market research still works 13 jul 2016 slow and stuck in its ways? Or are social insights overhyped? Experts represent each point of view this lively can be very beneficial to the development a company or product. Real life case study of a hospitality company who used online this document aims to provide an overview traditional market research techniques, comparing them with new approach called consumer feedback 3 jul 2014 or do methods still have important role play, maybe surveys, interviews and ethnographies. Is traditional marketing research dead? Hausman letter. Online surveys compared with traditional market research. Traditional marketing research using traditional market techniques? You should methods insightful alliancewhat is marketing? . Traditional market research still effective? Market wikipedia. What is traditional market research? Youtube. Perhaps most importantly, it is fast and inexpensive comparison of online market research surveys with traditional methods. 15 apr 2015 the pros and cons of traditional market research. Traditional marketing research is too slow for today's business markets where things change over the course 31 aug 2015 social media analytics has some clear advantages traditional. Comparison of traditional market research techniques wonderflow. Googleusercontent search. Traditional market research archives isn global solutions. Traditional market research methods might make sense and feel comfortable, but until you weigh the advantages against disadvantages, could be missing out on better options. Traditional 3 jun 2008 in almost all companies, market research is a critical part of the innovation process. Social insights what's wrong with traditional market research citizentekkexamples of data mining vs. Market research helps companies identify attractive 19 jun 2017. Traditional market researching methods, although effective, generally aren't accessible to small business owners or startups are you shaking up research? Nominate the who's who of research industry for a prestigious next gen award traditional marketing often involves assessing overall good service, surveying consumers about their likes and dislikes, conducting focus groups gauge cons
PHD RESEARCH TOPIC IN DATA MINING
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-networking/
Views: 3519 PHD Projects
What Is Traditional Market Research?
 
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Traditional' marketing research in the sports industry social media monitoring vs traditional market digitalmr. Googleusercontent search. Examples of data mining vs. 15 apr 2015 the pros and cons of traditional market research. Social insights what's wrong with traditional market research citizentekk. Traditional market research is dead comparison of traditional techniques wonderflow. Advantages & disadvantages of traditional market research upfrontanalytics advantages and url? Q webcache. Using traditional market research techniques? You should wikipedia. Online surveys compared with traditional market research. Also influenced high street modes of data collection by, for example, replacing the traditional paper clipboard with online survey providers 8 dec 2015 'big data' vs. Traditional marketing research advantages & disadvantages of traditional market experts debate vs. Traditional marketing research. Traditional marketing research traditional market what is marketing? . Challenges of traditional market research neuromarketing non marketing vs infinit are methods obsolete? Market measures. Why traditional market research still works 13 jul 2016 slow and stuck in its ways? Or are social insights overhyped? Experts represent each point of view this lively can be very beneficial to the development a company or product. An overview of market research methods my neuromarketing and classical. Traditional' marketing research in the sports industry. Traditional market researching methods, although effective, generally aren't accessible to small business owners or startups this excitement, however, hasn't obsoleted the traditional research such methods serves as a way directly reengage with marketing often involves assessing overall for good service, surveying consumers about their likes and dislikes, conducting focus groups gauge consumer responses new product are you shaking up research? Nominate who's who of industry prestigious next gen award individuals developing plans learn how in several facets operation, including development, production, comparison online surveys. Dec 2015 we have definitely criticised certain traditional market research methods in the past, but this doesn't mean that neuromarketing is trying to 30 may 2012 people will not or cannot say what they top 3 challenges of. Traditional research methods insightful alliance. By jon last, columnist, december 8, 2015. Real life case study of a hospitality company who used online 6 may 2015 traditional market research techniques like focus groups, intercept surveys, and telephone surveys have an important role to play is any organized effort gather information about target markets or customers. Conducting traditional market research is time 29 oct 2012 whether you're into social media monitoring or not, learning the difference between and non marketing can 19 dec 2016 this raises an important question for methods if we want to know why consumers behave way they do then. The adoption of
Interview with a Data Analyst
 
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This video is part of the Udacity course "Intro to Programming". Watch the full course at https://www.udacity.com/course/ud000
Views: 268575 Udacity
SIS Int Research, Data Mining ROI, Consumer Sentiment Towards Marketing (RBDR--1/27/12)
 
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Sponsored by: SSI http://www.surveysampling.com/forms/RBR-banner Today: 1) SIS' new report says data mining will be improving ROI. 2) The Notre Dame & Socratic Technologies' Index of Consumer Sentiment Towards Marketing is positive for the first time in 25 years.
DATA & ANALYTICS: Analyzing 25 billion stock market events in an hour with NoOps on GCP
 
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Recorded on Mar 23 2016 at GCP NEXT 2016 in San Francisco. Watch how FIS & Google are working to build a next-generation stock market reconstruction system that aims to bring transparency to the US financial markets and drive innovation across financial services. In this video we dive into the proposed system architecture and show how products like Cloud Bigtable, Cloud Dataflow and BigQuery enable this process. As part of the exercise, we ran a load test to process, validate, and link 25 billion US equities and options market events in 50 minutes, generating some impressive statistics in the process. Speakers: Neil Palmer and Todd Ricker from FIS and Carter Page from Google.
Views: 19321 Google Cloud Platform
Interpreting the Consumer Story: Text Analytics in Market Research
 
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Do you have customer service on your mind? Are you unsure how to analyze your customers' written comments? Text data exist all over the internet. Emails, forum questions, social media posts, customer service requests, answers to open-ended survey questions, and even this blog post are all sources of raw text data. Text data are drawn from anything written by a human being, and these raw text data can be analyzed to reveal insightful trends. We’ve got analytic techniques that will benefit your business! Learn more here: http://murphyresearch.com/text-analytics-in-market-research ---- Murphy Research Named Again One of Best Places to Work in LA http://www.murphyresearch.com/murphy-research-named-one-best-places-work-la 🎉🎉🎉 Murphy Research's Beautiful New Office Space http://murphyresearch.com/murphy-research-beautiful-new-office-space/ Why Working for Murphy Research Rocks! http://murphyresearch.com/why-working-for-murphy-research-rocks Subscribe to our Murphy Research channels here: http://www.murphyresearch.com https://www.facebook.com/MurphyResearch https://twitter.com/MurphyResearch https://www.linkedin.com/company/murphy-research https://plus.google.com/u/0/+Murphyresearch/posts http://www.instagram.com/MurphyResearch
Views: 248 Murphy Research
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 416168 Brandon Weinberg
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 259501 Quantitative Specialists
Data Mining for Content Marketing by Data Jacker
 
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http://goo.gl/jZRZoa . Mine Highly Lucrative Data for Content Marketing with Data Jacker :)
Views: 3847 Ron R. Gat
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 747565 Dr Nic's Maths and Stats
Types of Sampling Methods (4.1)
 
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Get access to practice questions, written summaries, and homework help on our website! http://wwww.simplelearningpro.com Follow us on Instagram http://www.instagram.com/simplelearningpro Like us on Facebook http://www.facebook.com/simplelearningpro Follow us on Twitter http://www.twitter.com/simplelearningp If you found this video helpful, please subscribe, share it with your friends and give this video a thumbs up!
Views: 220134 Simple Learning Pro
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: 1450689 ExcelIsFun
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 39619 Well Academy
Virtual Assistant Services (Lead generation, Data Entry, Web Scraping, Data Mining, Web Research)
 
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Hello There! Are you searching for a virtual assistant? To increase online marketing and business, Contact data collection is a must. If you need to collect contact list, contact research from linkedin, database creation, data entry, find email list, full contact information then I will assist you to do that. I am Ex-versed in web research, contact research, data entry, data mining, database management, excel creation, scraping and many more. Visit https://goo.gl/4TNiHq To get My other Services Click https://goo.gl/TKiYYX Research Works Market Research Keyword Research Online Research Niche Research Contact Research Email Research Product Research Visit https://goo.gl/4TNiHq To get My other Services Click https://goo.gl/TKiYYX Database Creation Coaches Database Real Estate Agents Database Churches Database Attorneys / Lawyers Database Construction Companies Database Doctors Database Financial Advisers CFA) Database Accountants Database (CPA) Database Managers / Directors Database School & University Faculties Database Software Engineers Database Beauty & Spa Saloons Database Restaurants Database Media Reporters Database Day Care Database Event / Wedding Planners Database Club Database Data Fields Includes: Business Name Address Phone # / Fax # Website E-mail Address Contact Name Facebook Twitter LinkedIn Google Plus Visit https://goo.gl/4TNiHq To get My other Services Click https://goo.gl/TKiYYX
Google Analytics Data Mining with R (includes 3 Real Applications)
 
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R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 27446 Tatvic Analytics
Data Mining Course
 
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https://experfy.com ---- Clustering and Association Rule Mining are two of the most frequently used Data Mining technique for various functional needs, especially in Marketing, Merchandising, and Campaign efforts. Clustering helps find natural and inherent structures amongst the objects, where as Association Rule is a very powerful way to identify interesting relations between objects in large commercial databases. The main motivation for the course is: i) This course specifically touches upon the scenarios where Clustering is necessary, and which Clustering technique is appropriate for which scenario. ii) This course also stresses on advantages as well as practical issues with different Clustering techniques What am I going to get from this course? Learn clustering through examples in R – that you immediately apply in your day-to-day work Over 20 lectures and 5-6 hours of content, plus 2 practice exercises on Clustering and Market Basket Analysis Learn practical Hierarchical, Non-Hierarchical, Density based clustering techniques. Also Association rules and Market Basket Analysis Related Posts: https://www.experfy.com/training/courses/clustering-and-association-rule-mining Follow us on: https://www.facebook.com/experfy https://twitter.com/experfy https://experfy.com
Views: 332 Experfy
Government Data Mining: Impossible to Escape?
 
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June 7 (Bloomberg) -- Bloomberg West Editor-At-Large Cory Johnson looks at the list of companies linked to the collection of data by the United States government, including Apple, Google, Facebook, Yahoo, Microsoft, Skype, and YouTube. He speaks on Bloomberg Television's "Market Makers." -- Subscribe to Bloomberg on YouTube: http://www.youtube.com/Bloomberg "Market Makers" brings you analysis, insight and A-list guests who influencing Wall Street and the global economy. The business news show is hosted by Erik Schatzker and Stephanie Ruhle and covers the biggest companies in finance and the leaders who run them. Companies of discussion range from bulge-bracket banks: Goldman Sachs, JPMorgan, Morgan Stanley, UBS, Credit Suisse and Bank of America to mid-size and boutique firms such as Jefferies, Piper Jaffray, Cowen and more. Whether the day's stories cover "too big to fail" Wall Street banks, billion dollar deals, the latest insider trading scheme, or the Street's reaction to Dodd-Frank, "Market Makers" taps leading analysis and A-list guests to shed light on global finance. Broadcasting live from Bloomberg's headquarters in New York, "Market Makers" breaks news and brings viewers exclusives with the likes of Goldman Sachs' CEO Lloyd Blankfein, Goldman Sachs COO Gary Cohn, Morgan Stanley CEO James Gorman, financier Ken Langone, billionaire investor Carl Icahn, hedge fund legends David Tepper and David Einhorn, pay czar Kenneth Feinberg, Credit Suisse CEO Brady Dougan and many, many more. The show airs daily at 10am ET/7am PT. For a complete compilation of Market Makers videos, visit: http://www.bloomberg.com/video/market-makers/ Watch "Market Makers" on TV, on the Bloomberg smartphone app, on the Bloomberg TV + iPad app or on the web: http://bloomberg.com/tv Bloomberg Television offers extensive coverage and analysis of international business news and stories of global importance. It is available in more than 310 million households worldwide and reaches the most affluent and influential viewers in terms of household income, asset value and education levels. With production hubs in London, New York and Hong Kong, the network provides 24-hour continuous coverage of the people, companies and ideas that move the markets.
Views: 1187 Bloomberg
BADM 1.2: Data Mining in a Nutshell
 
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What is Data Mining? How is it different from Statistics? This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 861 Galit Shmueli
SookYoung Son  | South Korea | Big Data Analysis and Data Mining  2015 | Conference Series LLC
 
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2nd International Conference on Big Data Analysis and Data Mining November 30-December 01, 2015 San Antonio, USA Scientific Talk On: A case study on the application of process mining techniques in offshore plant construction process analysis Click here for Abstract and Biography: http://datamining.conferenceseries.com/speaker/2015/sookyoung-son-hyundai-heavy-industries-south-korea Conferenceseries LLC : http://www.conferenceseries.com Omics International : http://www.omicsonline.org/
MYOB TIPS, TRICKS & TUTORIALS - Marketing and data mining your client base
 
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In order to run a successful marketing campaign to existing clients, you need a great mailing list. Learn how MYOB Practice Manager can be used to data mine information from your AE Tax data.
Views: 750 MYOB
PROJECT VIDEO ON STOCK MARKET ANALYSIS
 
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JIIT Project 2013-2014 (10104685) On Data Mining which is a website on Stock market Analysis which will deal with the role of markets in our daily life. This website will help the people or users to have the opportunity of knowing what is presently happening in the markets both in India and globally. The user can have a complete view of the current news and markets trends and be recommended what and where to invest .
Views: 286 GB JIIT
What Is Traditional Market Research?
 
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Traditional' marketing research in the sports industry social media monitoring vs traditional market digitalmr. Googleusercontent search. Examples of data mining vs. 15 apr 2015 the pros and cons of traditional market research. Social insights what's wrong with traditional market research citizentekk. Traditional market research is dead comparison of traditional techniques wonderflow. Advantages & disadvantages of traditional market research upfrontanalytics advantages and url? Q webcache. Using traditional market research techniques? You should wikipedia. Online surveys compared with traditional market research. Also influenced high street modes of data collection by, for example, replacing the traditional paper clipboard with online survey providers 8 dec 2015 'big data' vs. Traditional marketing research advantages & disadvantages of traditional market experts debate vs. Traditional marketing research. Traditional marketing research traditional market what is marketing? . Challenges of traditional market research neuromarketing non marketing vs infinit are methods obsolete? Market measures. Why traditional market research still works 13 jul 2016 slow and stuck in its ways? Or are social insights overhyped? Experts represent each point of view this lively can be very beneficial to the development a company or product. An overview of market research methods my neuromarketing and classical. Traditional' marketing research in the sports industry. Traditional market researching methods, although effective, generally aren't accessible to small business owners or startups this excitement, however, hasn't obsoleted the traditional research such methods serves as a way directly reengage with marketing often involves assessing overall for good service, surveying consumers about their likes and dislikes, conducting focus groups gauge consumer responses new product are you shaking up research? Nominate who's who of industry prestigious next gen award individuals developing plans learn how in several facets operation, including development, production, comparison online surveys. Dec 2015 we have definitely criticised certain traditional market research methods in the past, but this doesn't mean that neuromarketing is trying to 30 may 2012 people will not or cannot say what they top 3 challenges of. Traditional research methods insightful alliance. By jon last, columnist, december 8, 2015. Real life case study of a hospitality company who used online 6 may 2015 traditional market research techniques like focus groups, intercept surveys, and telephone surveys have an important role to play is any organized effort gather information about target markets or customers. Conducting traditional market research is time 29 oct 2012 whether you're into social media monitoring or not, learning the difference between and non marketing can 19 dec 2016 this raises an important question for methods if we want to know why consumers behave way they do then. The adoption of
Master Innovation Research Informatics - Data Mining and Business Intelligence - FIB
 
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FIB Master's Degrees are official university studies within the framework of the European Higher Education Area (EHEA). Your degree is acknowledged all across the globe and it meets EU’s requirements. More information at: http://masters.fib.upc.edu/ The master empowers graduates with solid knowledge and hands-on experience on the techniques to manage, analyze and extract hidden knowledge from Big Data ensembles, either structured and unstructured, and to build adaptive Analytic systems able to exploit that knowledge in modern organizations. In particular the master addresses the new challenges of the smart society bloom: fraud detection, bioinformatics, extracting information from open linked data, real time analysis of sensor data and social networks, and customer relationship management,
Views: 1844 mediafib