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: 3403 tutor2u
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: 17089 Growth Tribe
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: 4046 Cardinal Path
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: 41928 Last Minute Tutorials
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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 201111 Last moment tuitions
Louise Keely is the Intellectual Capital Director at The Cambridge Group where she is involved in management consulting and developing growth strategies for clients that are driven by a superior understanding of profitable demand. In this video, she shares how they have been able to find consumers more accurately and actively using Salford Systems' data mining tools.
Views: 629 Salford Systems
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: 7058 Hui Lin
Data will underpin everything in 2018. The use of data to make business decisions, not just marketing ones, will become the norm in 2018. Understanding how to analyse data, the meaning behind data and knowing what to do with this knowledge will become an important asset in the business of the future.
Views: 526 Peter Spinda
Lattice is pioneering the predictive applications market for marketing and sales. Lattice helps companies grow revenue across the entire customer lifecycle with data-driven marketing and sales applications that make complex data science easy to use. By combining thousands of buying signals with advanced predictive analytics in a suite of secure cloud applications, Lattice helps companies of all sizes including Citrix, DocuSign and HireVue to increase conversion rates by more than three times. Lattice is backed by NEA and Sequoia Capital with headquarters in San Mateo, CA. Learn more at www.lattice-engines.com and follow @Lattice_Engines.
Views: 833 Lattice Engines
This Big Data Video will help you understand how Amazon is using Big Data is ued in their recommendation syatems. You will understand the importance of Big Data using case study. Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspectives, business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. 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=Amazon-BigData-S4RL6prqtGQ&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: http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Amazon-BigData-S4RL6prqtGQ&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: 28600 Simplilearn
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: www.dataminingbook.com twitter.com/gshmueli 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 Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 2924 Galit Shmueli
APPLICATIONS OF DATA MINING IN BANKING AND FINANCE
Views: 433 Mehar Ahamed
Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 109236 LearnEveryone
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: 29464 Tatvic Analytics
Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Nowadays, there is an ever-increasing migration of people to urban areas. Health care services is one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Consequently, cities around the world are investing heavily in digital transformation in an effort to provide healthier ecosystem for people. In such transformation, millions of homes are being equipped with smart devices (e.g. smart meters, sensors etc.) which generate massive volumes of fine-grained and indexical data that can be analyzed to support smart city services. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. We propose the use of frequent pattern mining, cluster analysis and prediction to measure and analyze energy usage changes sparked by occupants’ behavior. Since people’s habits are mostly identified by everyday routines, discovering these routines allows us to recognize anomalous activities that may indicate people’s difficulties in taking care for themselves, such as not preparing food or not using shower/bath. Our work addresses the need to analyze temporal energy consumption patterns at the appliance level, which is directly related to human activities. For the evaluation of the proposed mechanism, this research uses the UK Domestic Appliance Level Electricity dataset (UK-Dale) - time series data of power consumption collected from 2012 to 2015 with time resolution of six seconds for five houses with 109 appliances from Southern England. The data from smart meters are recursively mined in the quantum/data slice of 24 hours, and the results are maintained across successive mining exercises. The results of identifying human activity patterns from appliance usage are presented in details in this paper along with accuracy of short and long term predictions.
Views: 550 jpinfotechprojects
This is a powerpoint/video compilation I made for a project in my Systems Engineering class. It is a tutorial of Data Mining in the Retail Industry and includes a trip I took to Harris Teeter to prove the importance of Market Basket Analysis in the real world.
Views: 7575 bgood717
Data mining is one of the key hidden gems inside of Analysis Services but has traditionally had a steep learning curve. In this session, you'll learn how to create a data mining model to predict who is the best customer for you and learn how to use other algorithms to spend your marketing model wisely. You'll also see how to use Time Series analysis for budget and forecast prediction. Finally, you'll learn how to integrate data mining into your application through SSIS or custom coding.
Views: 9844 PASStv
This talk was recorded at Europe's first Computational Social Science conference at the University of Warwick in June 2014, hosted by the Data Science Lab at Warwick Business School (http://www.datasciencelab.co.uk). ABSTRACT | Mobile phones are increasingly equipped with sensors, such as accelerometers, GPS receivers, proximity sensors and cameras, which, together with social media infromation can be used to sense and interpret people behaviour in real-time. Novel user-centered sensing applications can be built by exploiting the availability of these technologies. Moreover, data extracted from the sensors can also be used to model and predict people behaviour and movement patterns, providing a very rich set of multi-dimensional and linked data, which can be extremely useful, for instance, for marketing applications, real-time support for policy-makers and health interventions. In this talk I will discuss some recent projects in the area of large-scale scale data mining and modelling of mobile data, with a focus on human mobility prediction and epidemic spreading containment. I will also overview other possible practical applications of this work, in particular with respect to the emerging area of anticipatory computing and the challenges ahead for the research community. BIOGRAPHY | Dr. Mirco Musolesi is a Reader in Networked Systems and Data Science at the School of Computer Science at the University of Birmingham. He received a PhD in Computer Science from University College London in 2007. Before joining Birmingham, he held research positions at Dartmouth College and Cambridge and a Lectureship at the University of St Andrews. His research interests lie at the interface of different areas, namely ubiquitous computing, large-scale data mining, and network science.
Views: 291 Data Science Lab
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 971 The Audiopedia
Patrick Kane - Founder & CEO, Priori Data presents at App Promotion Summit Berlin 2014 on the topic of 'Data-Driven App Marketing: Using Analytics To Achieve Success' covering: - What Is The Data Telling Us About The App And Mobile Game Market In 2014? - Key Insights Into App Download Volumes And Velocity Across The App Stores - How To Use Data Mining And Analysis To Drive App Marketing Campaigns "Hi, everybody. Good morning. Let's see if this works. Yes. So welcome to Berlin and welcome to the App Promition Summit. James, Matthew, and the rest of the organizers, thank you very much for the opportunity to speak here. I think we're all looking forward to a great day. Someone had the bright idea to start us all off in the morning with data. So I hope everyone has their coffees and we won't get too detailed here, but hopefully we can set the stage a little bit for some of the developments that we're seeing in the marketplace and carry that throughout the rest of the day to some of the topics that James mentioned. So my name is Patrick. I'm the CEO and founder of Priori Data. Just a few words on us before we kick off. We're based in Berlin, actually not too far away from here. Just a quick show of hands. How many people in the audience today are actually Berlin-based or German-based, just to get an understanding? Significant. Fantastic. Okay. We were founded a year ago or so, about a year and a half ago. We operate market intelligence information for the mobile app economy. A lot of times people refer to that as app store analytics. We actually launched as a spin-off of another Berlin-based company, Xyo.net, which is active in the app discovery field. Some of you might be familiar with it. We launched essentially as a company doing custom data mining for consultancies and hedge funds. In March of last year, we launched our first software product, analytics product. In July of this year, we really reached out to the developer community, the publisher community for the first time. I'll mention that in a second. I'm actually pleased to say that today, we are launching the next version of our analytics platform. There are a lot of very tired eyes at Priori Data headquarters, myself included. It's been a lot of work, but it's a big day for us. Before we get into the data, just a couple words here. I wanted to point this out. We take a little bit of a different approach with mobile market data than some of the other companies in the space. If you are a publisher, a developer, you're looking to move beyond kind of tracking rank data in the stores, you want to understand how the quantified metrics look from downloads to revenues to users, we want to help you get a better understanding of your marketplace and we want to do that in a fair data exchange way. So we're working with a few hundred top publishers. Some of them are mentioned on this page, many of them are not, but we'd love to talk to you about this if this is interesting for you. So the agenda for today is twofold. First of all, I just want to set the stage with some of the general market trends that we're noticing in the marketplace, and then as we move through the presentation, give a little bit of a framework for how you might be able to think about using market data to strategically plan your performance, your benchmarking, your forecasting with your apps and games. So starting out at a very macro level, this is global data, this is year over year growth in downloads on a monthly basis. What we see for Android is a significant slowing in the year over year growth and downloads. For iOS, it has been this year essentially flat versus the prior year. Now we're still talking about billions of downloads a month. For iOS, it's about two billion. Android is double that. So we're not talking about a decline in the market per se, but it's absolutely a changing dynamic and it's one that people should be aware of and it's one that we're following, obviously, on a monthly basis. If the demand environment is slowing in terms of growth, we also want to look at the supply environment, so how many new apps are actually coming into the marketplace. We see, on a year over year basis, about 2 to 3% net of attrition every month. That's a couple thousand new apps coming on per month, per platform, a few hundred per day." More at: http://www.businessofapps.com/
Views: 635 App Promotion Summit
Complete Premium video at: http://fora.tv/conference/hsm_wif_2010 Andreas Weigend, former Chief Scientist for Amazon.com, discusses what he calls the "social data revolution." He explains that personal data collection is growing at such an exponential rate, that it's now 10,000 times more efficient than the KGB was 20 years ago. To view more highlights from the HSM World Innovation Forum 2010 series, visit http://www.youtube.com/view_play_list?p=88C0567991E989D6 ----- The world's greatest thought leaders in the field convene at the World Innovation Forum to provide actionable insights into the central issues at the heart of innovation today -- Marketing, Web 2.0, Health Care, Social Media, Design, Technology, Education, Green. Former Chief Scientist at Amazon.com Andreas Weigend on marketing and web 2.0: Marketing in the web 2.0: Beyond cutting costs and optimizing business processes What are the implications for new business models products and services? A world of abundance: Making the most of quantitative and qualitative data Social networks and the new uses of data: The power of social recommendations and behavioral targeting Lessons from the inside: What we can learn from Amazon Andreas Weigend, Amazon.com's Chief Scientist until January 2004, is a leading behavioral marketing expert. His career as a scientist, data strategist and quantitative methods innovator has enabled him to bridge the gap between industry and academia. As the Chief Scientist of Amazon.com, he developed data mining techniques including session-based marketing, and designed applications ranging from heuristic cross-selling to customer network and lifecycle analysis. Weigend currently teaches the graduate course Data Mining and Electronic Commerce at Stanford University.
Views: 3809 FORA.tv
Dan Stone, Product Manager at Google Analytics H2O World 2015, Day 2 Join the Movement: open source machine learning software from H2O.ai, go to Github repository https://github.com/h2oai Do you like this? Check out more talks on open source machine learning software at: http://www.slideshare.net/0xdata
Views: 2841 H2O.ai
In this video I'm explaining about data mining what is data mining and what is the use of data mining in marketing and business and why applications use our data and why hackers steal our data what is the use of our data Answers to all these queries in Malayalam. ഞാൻ ഇതുപോലുള്ള technology വാർത്തയുമായി ഇനിയും വരും കണ്ടതിനു നന്ദി തുടർന്നും ഇൗ ചാനെൽ സന്ദർശിക്കുക നമുക്ക് ഒരുമിച്ച് മുന്നേറാം മലയാളി എന്ന നിലയിൽ പരസ്പരം support ചെയ്യുക നമുക്ക് പൊളിക്കാം ബ്രോ. #DataMining #dataminingexplained #useOfDataMining നമുക്ക് ഒരുമിച്ച് ചേരാം WhatsApp group link-https://chat.whatsapp.com/9xLD2cBJ6btAxUmkP4ypn9 Facebook page link-https://www.facebook.com/Techno-Shanik-Malayalam Twitter-Take a look at Techno Shanik (@Shanik60645129): https://twitter.com/Shanik60645129?s=09 ഇൗ ചാനലിനെ കുറിച്ച് Techno Shanik enna ee channel technology മായി ബന്ധപ്പെട്ട് നിങ്ങൾക്ക് പഠിപ്പിക്കാൻ ആഗ്രഹിക്കുന്നു ദിവസം ഒരു വിഡിയോ എന്നതാണ് uploading രീതി Technology പഠിക്കൂ മലയാളത്തിൽ.
Views: 214 Techno Shanik [മലയാളം]
#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://goo.gl/to1yMH 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: 272519 Last moment tuitions
This iMacros Java script is used to extract or scrape targeted GitHub users or members email address and their Full name into spreadsheet (CSV file). wanna buy this script contact : [email protected] or https://fb.com/ZiaUrR3hman
Views: 455 Zia Ur Rehman
This is a brief insight into how Text Mining can be utilised across different industries. This video focuses on how Text Mining can be applied in the following industries: - Healthcare - Research - Corporate - Industry - Software - Publishing Text Mining is a flexible tool that can be utilised in near enough every industry. Interested and want to find out more? Go to http://www.textminingsolutions.co.uk Want to know the basics of Text Mining go to https://www.youtube.com/watch?v=zOcvi2R1FOA Follow Text Mining Solutions on: Facebook: https://www.facebook.com/TextMiningSolutions?fref=ts Twitter: https://twitter.com/Txt_Mining LinkedIn: https://www.linkedin.com/company/text-mining-solutions Music by: http://www.purple-planet.com
Views: 605 TxtMining
Data Mining, from Theory to Practice, Lecture of Prof. Mark Last, Head of the Data Mining and Software Quality Engineering Group, Ben-Gurion University of the Negev, "Data Mining Applications: from Winemaking to Counterterrorism" Data Mining for Business Intelligence - Bridging the Gap Ben-Gurion University of the Negev
Views: 498 BenGurionUniversity
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
Views: 530450 Siraj Raval
Data Mining Class Final Project for A. Chandley using Portugal Bank Marketing Dataset [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS. Available at: [pdf] http://hdl.handle.net/1822/14838 [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt
Views: 336 Adam Chandley
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: 1535722 ExcelIsFun
Louise Keely is the Intellectual Capital Director at The Cambridge Group where she is involved in management consulting and developing growth strategies for clients that are driven by a superior understanding of profitable demand. In this video, she shares how they have been able to find consumers more accurately and actively using Salford Systems' data mining tools.
Views: 200 Salford Systems
https://www.experfy.com/training/courses/clustering-and-association-rule-mining 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 Affinity analysis and association rule learning encompasses a broad set of analytics techniques. Of these, “market basket analysis” is perhaps the most famous example and has emerged as the next step in the evolution of retail merchandising and promotion. Follow us on: https://www.facebook.com/experfy https://twitter.com/experfy https://experfy.com
Views: 9735 Experfy
Dataset: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing# Overview: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. This dataset consists of client information of a bank; 41188 records with 20 inputs, ordered by date (from May 2008 to November 2010). Aim: The classification goal is to predict if the client will subscribe (yes/no) a term deposit. The data includes information about the clients and marketing calls. Together with this data there is a record of whether the clients are currently enrolled for a term deposit. All of the variables should be considered and modeled to produce classification to accurately predict an entry for a client. Attribute Information: Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') # related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. # other attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') # social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')
Views: 358 Gaurang Panchal