Home
Search results “Data mining techniques for marketing”
Data Mining (Introduction for Business Students)
 
04:21
This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 6930 tutor2u
Introduction to data mining and architecture  in hindi
 
09:51
#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 278971 Last moment tuitions
Purdue University - Data Mining Techniques for Bank Direct Marketing - RShiny App
 
20:06
https://github.com/karmapatel8495/bank_marketing https://github.com/karmapatel8495/marketing_shiny_app Modern businesses are always looking for leaner and more efficient ways of marketing. The rate of conversion achieved via traditional marketing strategies such as mass marketing are just not good enough for certain industries. Banking is one such industry. In banking, direct marketing techniques have long replaced more traditional ones. Direct marketing is a method of contacting customers and potential customers personally, rather than having an indirect medium between the company and the consumer, such as magazine ads or billboards that are seen by the general public. Direct marketing can take many forms, including mail, telephone calls, emails, brochures, and coupons. For our project, we are using a publicly available dataset. containing real-time data about one such direct marketing campaign (phone calls) of a Portuguese banking institution.
Views: 52 KPTech
Applying Data Science Methods for Marketing Lift
 
57:06
Data science can deliver transformational business insights by bringing together statistics, mathematics, computer science, machine learning, and business strategy. A variety of data science techniques are available which allow marketers to surface insights from large swathes of data, but which technique is right for your business and where do you start? In this on-demand webinar, our experts go over a broad range of data science techniques, and expose how major global brands are using them for valuable business insights including:customer lifetime value for customer segmentation and activation, forecasting and predictive analytics with machine learning, and natural language processing for digital marketing optimization
Views: 4744 Cardinal Path
Using Data Mining Techniques For Improving The Effectiveness Of Sales And Marketing
 
01:25
To Get this Using Data Mining Techniques For Improving The Effectiveness Of Sales And Marketing project. Call 9030333433 or visit our website http://takeoffprojects.com/
Views: 66 takeoff edu
Data mining applications and techniques
 
07:23
1.Business Sector 2.Marketing and Retailing sector 3.Bio informatics 4.Climatology 5.Banking and Finance 6.Security and Data Integrity 7.E-commerce 8.Forensic and Criminal Investigation 9.Goverment Records 10.Cloud computing
Views: 858 Karthiga Ganesan
Last Minute Tutorials | Data mining | Introduction | Examples
 
04:13
Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 59396 Last Minute Tutorials
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
10:36
#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 345801 Last moment tuitions
Sampling Techniques [Data Mining](HINDI)
 
04:30
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 15136 5 Minutes Engineering
Ⓘ EN. Data mining techniques & customer relationship management through existing Wi-Fi.
 
02:33
Data mining techniques & customer relationship management through existing wireless access networks (Wi-Fi)
Views: 200 spotyy.social.WiFi
Le Data Mining en 35 Leçons - Session 24 : Techniques de Segmentation
 
08:26
Cette nouvelle session de la série de didacticiels "Le Data Mining en 35 Leçons avec STATISTICA" présente différentes techniques de segmentation, c’est-à-dire des méthodes de classification non-supervisée dont l’objectif consiste à répartir les observations en différents groupes relativement homogènes, possédant des caractéristiques communes. Le processus de construction de la segmentation est expliqué pas-à-pas dans STATISTICA au travers des techniques des k-moyennes, de l’Espérance Maximisation (EM) et des réseaux de neurones (cartes de Kohonen) en couvrant les options d’analyse et l’interprétation des résultats...
Views: 3109 Statistica France
Data Mining Course
 
03:24
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: 549 Experfy
THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING
 
00:36
Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html ********************************************* Computer Applications: An International Journal (CAIJ), Vol.4, No.1/2/3/4, November 2017 DOI:10.5121/caij.2017.4401 THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING Yuvika Priyadarshini Researcher, Jharkhand Rai University, Ranchi. ABSTRACT The aim of this study is to identify the extent of Data mining activities that are practiced by banks, Data mining is the ability to link structured and unstructured information with the changing rules by which people apply it. It is not a technology, but a solution that applies information technologies. Currently several industries including like banking, finance, retail, insurance, publicity, database marketing, sales predict, etc are Data Mining tools for Customer . Leading banks are using Data Mining tools for customer segmentation and benefit, credit scoring and approval, predicting payment lapse, marketing, detecting illegal transactions, etc. The Banking is realizing that it is possible to gain competitive advantage deploy data mining. This article provides the effectiveness of Data mining technique in organized Banking. It also discusses standard tasks involved in data mining; evaluate various data mining applications in different sectors KEYWORDS Definition of Data Mining and its task, Effectiveness of Data Mining Technique, Application of Data Mining in Banking, Global Banking Industry Trends, Effective Data Mining Component and Capabilities, Data Mining Strategy, Benefit of Data Mining Program in Banking
Views: 58 aircc journal
How to do Data Mining for Cold calling and Email Marketing
 
07:10
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: 4308 ImAnkitShah
Customer Segmentation in Python - PyConSG 2016
 
34:53
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: 20282 Engineers.SG
Predicting Stock Prices - Learn Python for Data Science #4
 
07:39
In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 619459 Siraj Raval
Quick Data Analysis with Google Sheets | Part 1
 
11:40
Spreadsheet software like Excel or Google Sheets are still a very widely used toolset for analyzing data. Sheets has some built-in Quick analysis features that can help you to get a overview on your data and very fast get to insights. #DataAnalysis #GoogleSheet #measure 🔗 Links mentioned in the video: Supermetrics: http://supermetrics.com/?aff=1014 GA Demo account: https://support.google.com/analytics/answer/6367342?hl=en 🎓 Learn more from Measureschool: http://measureschool.com/products GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear Our tracking stack: Google Analytics: https://analytics.google.com/analytics/web/ Google Tag Manager: https://tagmanager.google.com/ Supermetrics: http://supermetrics.com/?aff=1014 ActiveCampaign: https://www.activecampaign.com/?_r=K93ZWF56 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool
Views: 21838 MeasureSchool
IXXO Web Mining Software - Big Data and Market Intelligence
 
03:25
How to create an interactive map of the Big Data ecosystem with IXXO Web Mining Software Discover more on http://www.ixxo-software.com
Views: 287 IxxoWebMining
Soil Classification Using Data Mining Techniques: A Comparative Study | Final Year Projects 2016
 
09:52
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 210 Clickmyproject
Data Mining to Improve Direct-Mail Campaigns
 
01:30
Mass Marketing Manager Tim Troutman discusses how the Charlotte Rescue Mission in North Carolina uses the statistical algorithms in JMP Pro software to reach the right donor with the right offer. Hitting the right targets is critical, and JMP software has played a big role in helping hone the rescue mission's accuracy. Troutman believes that JMP Pro offers a new software option for people who need automated techniques for data mining and predictive modeling. "I can do a lot of things with the click of a button that used to take me a couple of hours to write the script for. The new tools in JMP Pro are great," he says. http://www.jmp.com/software/success/charlotte_rescue.shtml
Views: 258 JMPSoftwareFromSAS
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
22:01
Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 349921 CS Dojo
Decision Tree Solved | Id3 Algorithm (concept and numerical) | Machine Learning (2019)
 
17:43
Decision Tree is a supervised learning method used for classification and regression. It is a tree which helps us by assisting us in decision-making! Decision tree builds classification or regression models in the form of a tree structure. It breaks down a data set into smaller and smaller subsets and simultaneously decision tree is incrementally developed. The final tree is a tree with decision nodes and leaf nodes. A decision node has two or more branches. Leaf node represents a classification or decision. We cannot do more split on leaf nodes. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data. #codewrestling #decisiontree #machinelearning #id3 Common terms used with Decision trees: Root Node: It represents entire population or sample and this further gets divided into two or more homogeneous sets. Splitting: It is a process of dividing a node into two or more sub-nodes. Decision Node: When a sub-node splits into further sub-nodes, then it is called decision node. Leaf/ Terminal Node: Nodes do not split is called Leaf or Terminal node. Pruning: When we remove sub-nodes of a decision node, this process is called pruning. You can say opposite process of splitting. Branch / Sub-Tree: A sub section of entire tree is called branch or sub-tree. Parent and Child Node: A node, which is divided into sub-nodes is called parent node of sub-nodes whereas sub-nodes are the child of parent node. How does Decision Tree works ? Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables. Advantages of Decision Tree: 1. Easy to Understand: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Its graphical representation is very intuitive and users can easily relate their hypothesis. 2. Useful in Data exploration: Decision tree is one of the fastest way to identify most significant variables and relation between two or more variables. With the help of decision trees, we can create new variables / features that has better power to predict target variable. It can also be used in data exploration stage. For e.g., we are working on a problem where we have information available in hundreds of variables, there decision tree will help to identify most significant variable. 3 Decision trees implicitly perform variable screening or feature selection. 4. Decision trees require relatively little effort from users for data preparation. 5. Less data cleaning required: It requires less data cleaning compared to some other modeling techniques. It is not influenced by outliers and missing values to a fair degree. 6. Data type is not a constraint: It can handle both numerical and categorical variables. Can also handle multi-output problems. ID3 Algorithm Key Factors: Entropy- It is the measure of randomness or ‘impurity’ in the dataset. Information Gain: It is the measure of decrease in entropy after the dataset is split. Ask me A Question: [email protected] Music: https://www.bensound.com For Decision Trees slides comment below 😀
Views: 6033 Code Wrestling
Measurement scale in hindi
 
10:33
Thank you friends to support me Plz share subscribe and comment on my channel and Connect me through Instagram:- Chanchalb1996 Gmail:- [email protected] Facebook page :- https://m.facebook.com/Only-for-commerce-student-366734273750227/ Unaccademy download link :- https://unacademy.app.link/bfElTw3WcS Unaccademy profile link :- https://unacademy.com/user/chanchalb1996 Telegram link :- https://t.me/joinchat/AAAAAEu9rP9ahCScbT_mMA
Views: 94179 study with chanchal
A survey Big Data social media using data mining techniques | | Final Year Projects 2016 - 2017
 
09:30
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 166 Clickmyproject
Sampling Techniques [Hindi]
 
06:22
The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population
Views: 158113 Manager Sahab
KDD2016 paper 283
 
02:18
Title: When Social Influence Meets Item Inference Authors: Hui-Ju Hung*, The Pennsylvania State University Hong-Han Shuai, Academia Sinica De-Nian Yang, Academia Sinica Liang-Hao Huang, Academia Sinica Wang-Chien Lee, The Pennsylvania State University Jian Pei, Simon Fraser University Ming-Syan Chen, National Taiwan University Abstract: Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 2243 KDD2016 video
Data mining process
 
00:34
An extreme arm of analytics is “predictive analytics” It’s core lies in data mining - which is nothing but a process used to extract usable data from a larger set of any raw data. This technique is heavily used in thousands of tools available today in digital marketing landscape. You have experienced automatic predictions yourself in Flipkart - the way more similar products are shown to you marked as “based on your history”
Views: 83 Saurabh Srivastava
Data science in marketing (part 2)
 
07:37
Data science is one of the most powerful tools that marketeers can use. In this video we discuss about some of the statistical and machine learning techniques that can be used in order to improve performance and maximise revenue. This video has been produced by the Tesseract Academy (http://tesseract.academy), the best source for decision makers who want to learn about deep technical topics such as data science, analytics, machine learning and blockchain. For more related content, also make sure to visit The Data Scientist: http://thedatascientist.com
Views: 18 Stylianos Kampakis
Direct Marketing and Data Mining Part 5
 
12:27
Aaron Davis of Datalab USA gave this presentation at the 2009 Salford Data Mining Conference in San Diego, CA.
Views: 66 Salford Systems
Google Analytics Data Mining with R (includes 3 Real Applications)
 
53:31
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: 31193 Tatvic Analytics
Data Mining for Lead Generation & Local Marketing by Data Jacker
 
03:10
http://goo.gl/jZRZoa . Data Jacker ... Mine Highly Lucrative Data for Lead Generation & Local Marketing with Data Jacker :)
Views: 4147 Ron R. Gat
Final Year Projects 2015 | Soil Classification Using  Data Mining  Techniques
 
07:34
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 650 MyProjectBazaar
Direct Marketing and Data Mining Part 2
 
09:43
Aaron Davis of Datalab USA gave this presentation at the 2009 Salford Data Mining Conference in San Diego, CA.
Views: 158 Salford Systems
Types of Sampling Methods (4.1)
 
04:50
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: 336942 Simple Learning Pro
Data Mining for Content Marketing by Data Jacker
 
05:16
http://goo.gl/jZRZoa . Mine Highly Lucrative Data for Content Marketing with Data Jacker :)
Views: 3860 Ron R. Gat
ITECH1100  Impact of Data Mining in Snack Food Industry 30372457
 
02:12
Made by Prabin Bhattarai. Image Sources: https://www.potatochipsmachinery.com/news/Pakistan-potato-chips-market-analysis.html https://insideanalysis.com/data-mining-and-beyond/ http://ap.fftc.agnet.org/ap_db.php?id=607 https://www.slideshare.net/Euromonitor/challenges-and-opportunities-for-food-manufacturers References: The Economic Times. (2019). Definition of Data Mining | What is Data Mining ? Data Mining Meaning - The Economic Times. [online] Available at: https://economictimes.indiatimes.com/definition/data-mining [Accessed 2 Jun. 2019].
Views: 9 Prabin Bhattarai
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
06:20
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: 960387 Dr Nic's Maths and Stats
Customer Relationship Management(CRM) Lecture 1 - E-Commerce Lectures
 
13:30
Customer Relationship Management(CRM), Customer Life Cycle, Customer Extension Techniques, Marketing Applications of CRM E-Commerce and M-Commerce Video Lectures in Hindi/English #ECMC #CRM
Data mining without discrimination
 
03:52
Discussion on data mining and how to prevent it being discriminatory
Views: 164 MVIcommunity