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Data Science for Business: Data Mining Process and CRISP DM
 
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This lesson provides an introduction to the data mining process with a focus on CRISP-DM. This video was created by Cognitir (formerly Import Classes). Cognitir is a global company that provides live training courses to business & finance professionals globally to help them acquire in-demand tech skills. For additional free resources and information about training courses, please visit: www.cognitir.com
Views: 14774 Cognitir
The Data Science / Data Analytics / CRISP-DM Cycle
 
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I have data lying around. How do I start - and continue - if I want to build a predictive model based on these data? Well, it is not really like following a cooking recipes with precise steps. It is more like adjusting the steps here and there, going back and starting again with different parameters or maybe even more drastically anew with different algorithms. This video explains this general iterative process.
Views: 6562 KNIMETV
Meta S. Brown (Keynote): CRISP-DM; The dominant process for data mining
 
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CRISP-DM stands for Cross-Industry Standard Process for Data Mining, is an industry-proven way to guide your data mining efforts. This talk covers this dominant process, what it is, how it is developed, where it is today and why it's time for you to get involved.
Views: 5373 PyData
Introduction to the CRISP DM data mining methodology - webinar recording
 
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When you’re starting out in data mining or predictive analytics it’s all too easy just to jump straight in and hope for the best, but projects approached in this way commonly fail. To maximize your chances of success, you need to apply a structured project management methodology to your data mining plan. The most commonly used such methodology is CRISP DM (cross industry process for data mining). The CRISP DM approach is widely used, robust and well-proven as well as being intuitive and simple to understand. We recommend that all our clients use CRISP DM as the basis of their data mining and predictive analytics projects. Time and again we see that the most successful client projects are those which apply it effectively. Hence, we produced this free webinar which provides a quick introduction to the methodology and some practical advice on how to apply it to your own projects.
Views: 1319 Smart Vision Europe
Advanced Analytics with R and Tableau : CRISP-DM Methodology  | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2AmNZec]. The Cross Industry Standard Process for Data Mining or CRISP-DM model as it is known, is a process model that provides a fluid framework for devising, creating, building, testing, and deploying machine learning solutions. • Business understanding/data understanding • Business understanding phases For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 437 Packt Video
CRISP-DM & SEMMA
 
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Views: 222 Tasya Octavian
Data Collection and Preprocessing | Lecture 6
 
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Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 1527 Leo Isikdogan
Introduction to CRISP-DM
 
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Check out all of Udacity's courses at https://www.udacity.com/courses
Views: 3735 Udacity
The Complete Data Science Project Management using CRISP-DM methodology
 
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Discount Offer for first 25 subscribers till 15 October. Enroll for complete course less than 12$ (over 92% discount) on udemy through this link: https://www.udemy.com/data-science-project-management/?couponCode=YOUTUBE 100% refund within 30 days, if you are not satisfied with the course. Lifetime Access and Online Instructor support through discussion form This course is to enable learners to successfully manage a data science project. It is process oriented and explains CRISP-DM methodology. CRISP-DM, stands for Cross Industry Standard Process for Data Mining, and it is the most widely used, holistic framework for data science projects. This course takes you through the data mining activities in the context of Project Management. The project explains inputs and outputs of all activities helping effective project management of a data science project. As per the Project Management best practices it guides you to engage the right stakeholders to help setting Data Mining Success criteria to achieve the business goals. Machine Learning and Model building activities using Python or R are an important activities in any data science project. However, there are several other activities that are part of any Data Science project. The data needs to be prepared for application of machine learning techniques. There are lot of steps involved in preparing a data-set which would be suitable for achieving the business goal of the data science project. In this course, we are going to take a broader look and identify how each activity of CRISP-DM fits together towards achieving business outcomes of a data science project. The course lists the monitoring, reporting and user training needs during the execution of data science project. The data science project needs to conclude with deployment of data mining results and review of lessons learned. All the above activities are sequenced in this course along with their purpose and elaborate details for end to end execution of a data science project.
Views: 62 HiTech Squad
Machine Learning as a Component in a Data Mining Process
 
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I will use the CRISP-DM process model for data mining, as our initial network, which will then be expanded with each new book.
Views: 4109 MachineLearningGod
Colin Shrearer: Crisp DM
 
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CRISP-DM - the standard model for analytics progress
CRISP-DM: how to bring analytics to production
 
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An agile way to bring analytics to production in a business-oriented and systematic way is CRISP-DM model.
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #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: 71889 Last moment tuitions
What is Data Mining?
 
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Brief overview of what Data Mining is and of the CRISP-DM process model for Data Mining projects.
Views: 25 What is
Data Preparation
 
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Check out all of Udacity's courses at https://www.udacity.com/courses
Views: 2020 Udacity
CRISP DM - Sharing Bike System
 
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The description of project
Views: 101 zhengyu1310
DATA SCIENCE ENTENDER O NEGÓCIO. CRISP-DM #FASE1
 
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Entender o Negócio: foca em entender o objetivo do projeto a partir de uma perspectiva de negócios, definindo um plano preliminar para atingir os objetivos. Clique no sino para receber as notificações. ***NÃO ESQUEÇA DE SE INSCREVER*** Deixe seu comentário! Se gostou do vídeo, dê o seu like e compartilhe! :) Sandeco nas redes sociais: Instagram: http://www.instagram.com/sandeco Twitter: http://www.twitter.com/sandeco Github: http://www.github.com/sandeco CRISP-DM é a abreviação de CRoss Industry Standard Process for Data Mining[1], que pode ser traduzido como Processo Padrão Inter-Indústrias para Mineração de Dados. É um modelo de processo de mineração de dados que descreve abordagens comumente usadas por especialistas em mineração de dados para atacar problemas.
Views: 1352 CANAL SANDECO
CRISP DM
 
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Descripcion de fases de la metodologia CRISP-DM
Views: 793 Héctor Pérez
DATA MINING CONCEPTS AND TECHNIQUES
 
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Data Mining
Views: 156 ARUN RAJ M
Create Recommendation and Prediction Data Product in Five Minutes
 
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DSTK - Data Science Toolkit offers Data Science softwares to help users in data mining and text mining tasks. DSTK follows closely to CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK helps you to create recommendation and prediction application based on your data and allow you to reuse and distribute the application. You can create your software to interface with the application. For more information, visit: http://dstk.tech
Views: 46 SVBook
Gource for sentiment on a data science project using CRISP-DM (1080p HD)
 
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DMATRIX presentation on how our data science team collaborated over a 2 month project in text mining. 47 days worth of Slack text data is captured. Includes most of the 25 activities of CRISP-DM plus extras for text mining in data cleaning. Re-appropriated gource for visualizing collaboration and sentiment using indico.io over slack messages. User role icons used from DataCamp. https://www.datacamp.com/ https://slack.com/ https://indico.io/
Views: 108 Martin Lehmann
10 Data Preparation
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 8328 RapidMiner, Inc.
Gource for sentiment on a data science project using CRISP-DM
 
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DMATRIX presentation on how our data science team collaborated over a 2 month project in text mining. 47 days worth of Slack text data is captured. Includes most of the 25 activities of CRISP-DM plus extras for text mining in data cleaning. Re-appropriated gource for visualizing collaboration and sentiment using indico.io over slack messages. User role icons used from DataCamp. https://www.datacamp.com/ https://slack.com/ https://indico.io/
Views: 164 Martin Lehmann
077. От исследований к продакшену: TDD, CRISP DM, контроль версий – Арсений Анисимович
 
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- Как организовать эффективное взаимодействие бизнеса, разработчиков и DS? - Как версионировать данные и возможно ли это? - Существует ли test driven development в Data Science? - Как (можно) хранить модели и состояния датасетов? * 21 октября 2018 г. в московском офисе Яндекса прошла встреча сообщества Open Data Science. Мы испытали новый формат: программа не была определена заранее, а составлялась по запросам аудитории. Мы собрали больше 500 заявок от участников и ответили на самые популярные и интересные вопросы. Арсений Анисимович MS в области обработки естественного языка (NLP). Lead Research Scientist в компании QL Tech и Machine Learning Engineer в Brave Software. Опыт в анализе данных, 9 лет, включая сферы семантического поиска, вопросно-ответных систем, систем рекомендации и обучения с подкреплением. Скачать слайды: https://yadi.sk/i/Ggt20PW360Ogfg Посмотреть записи других выступлений можно на странице мероприятия: https://events.yandex.ru/events/ds/21-oct-2018/
Tugas CRISP DM model K-Nearest Neighbor menggunakan RapidMiner
 
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Tugas CRISP DM Menggunakan model klasifikasi K-Nearest Neighbor Aditya Nugraha Azwari - J1F115001 Dita Amara - J1F115027 Zulkifli - J1F115228
DATA SCIENCE AVALIAÇÃO. CRISP-DM #FASE5
 
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A fase mais crítica do CRISP-DM. Avaliação: é construído um modelo que parece ter grande qualidade de uma perspectiva de análise de dados. No entanto, é necessário verificar se o modelo atinge os objetivos do negócio. Clique no sino para receber as notificações. ***NÃO ESQUEÇA DE SE INSCREVER*** Deixe seu comentário! Se gostou do vídeo, dê o seu like e compartilhe! :) Sandeco nas redes sociais: Instagram: http://www.instagram.com/sandeco Twitter: http://www.twitter.com/sandeco Github: http://www.github.com/sandeco CRISP-DM é a abreviação de CRoss Industry Standard Process for Data Mining[1], que pode ser traduzido como Processo Padrão Inter-Indústrias para Mineração de Dados. É um modelo de processo de mineração de dados que descreve abordagens comumente usadas por especialistas em mineração de dados para atacar problemas.
Views: 596 CANAL SANDECO
The Problem Solving Framework
 
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Check out all of Udacity's courses at https://www.udacity.com/courses
Views: 3572 Udacity
Типичные ошибки на каждом этапе CRISP-DM и как их избежать. Иван Гуз (Авито)
 
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CRISP-DM помогает структурировать процесс проведения исследований и очень прост в понимании и освоении. Однако за кажущейся простотой скрывается большое количество подводных камней, которые не так очевидны, и столкновение с которыми зачастую приводит к провалу DM-проектов. В своем докладе Иван расскажет про многолетний опыт использования CRISP-DM в Авито, основные ошибки, которые были совершены и как с ними боролись. Будет презентован топ-10 ошибок, который слушатели могли бы легко запомнить и применять в своей работе. Самые страшные ошибки, которые допускают DS. Встреча в офисе Авито 24.04.2018 http://ai-community.com
Views: 264 AvitoTech
Part 2 | Data Analytics for Beginners | Analytics Lifecycle
 
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This video on Data Analytics Life-cycle gives you a closer look into the Data Analytics process flow i.e. how in a step by step logical manner a Data Analytics project is approached. This is a simple overview where we have used examples from day to day life. Most important takeaway here is the thought process that it is not about the technical knowledge alone, but more about the practical and logical thinking that makes one successful in this field.
Views: 7263 Six Sigma Pro SMART
Predictive Analytics with Python and R
 
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REVA University Business Analytics - Session covers CRISP-DM framework, Hands-On session on Linear Regression modeling
How to Conduct Data Analysis Process Systematically
 
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A tutorial by Dr. Ceni Babaoglu. website: www.cenibabaoglu.com Table of Contents: 00:28 - Outline 00:54 - Initial Analysis 09:37 - Exploratory Analysis 13:15 - Modeling 14:21 - Evaluation 14:54 - Improving the model
Views: 2450 Ceni Babaoglu
Full CRISP DM Cycle: S3   Redshift   Tableau & Pyspark    S3
 
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First Update on Retail Customer Analytics Project
Views: 23 Jason Lee
Data Understanding
 
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Check out all of Udacity's courses at https://www.udacity.com/courses
Views: 24638 Udacity
Ds4100 Final Project Presentation
 
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Describing using CRISP DM to analyzing wine quality base on chemical component.
Views: 10 Emmy Huang
Analyzing the Software Development Life-Cycle using Data-Mining Techniques
 
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by Andreas Platschek At: FOSDEM 2017 One of the major challenges for certification in the SIL2LinuxMP project, isto show that Linux does not only define a development process, but alsofollows it. To this end (and far beyond!) the meta-data of commits to theLinux kernel are analyzed. The talk covers everything from gathering the data, to distributing it toevery one in the project while keeping it the data up-to-date and of courseour first analysis results. Each of these phases contain their own set ofproblems that needed to be considered, leading to a framework called DLCDM(Development Life-Cylce Data-Mining) that is introduced for the first timeduring this talk. One of the major challenges for certification in the SIL2LinuxMP project, isto show that Linux does not only define a development process, but alsofollows it. To this end (and far beyond!) the meta-data of commits to theLinux kernel are analyzed. There are several intended outputs we hope to getout of this analysis, some examples are: - Competence of persons involved (IEC 61508-1, 6.2.13/6.2.14) - Dependencies amongst developers (Independence of persons doing code reviews) - Identify patches that did not get enough review (based on patch complexity, experience of author, reviews, etc.) - Automatic notification of patches in our configuration - Bug analysis (based on Fixes: tag) - Subsystem dependencies and conflicts The talk covers everything from gathering the data, to distributing it toevery one in the project while keeping it the data up-to-date and of courseour first analysis results. Each of these phases contain their own set ofproblems that needed to be considered, leading to a framework called DLCDM(Development Life-Cylce Data-Mining) that is introduced for the first timeduring this talk. Room: UD2.120 (Chavanne) Scheduled start: 2017-02-04 12:00:00
Views: 214 FOSDEM
Advanced Analyics - Machine Learning
 
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Video 3/3. Span the second great chasm between Descriptive/Diagnostic and Predictive/Prescriptive Analytics by focusing on tools and skills within your organization. Douglas McDowell, CEO of SolidQ North America takes us through the business understanding of Machine Learning through the CRISP-DM process and then outlines the overwhelming benefits Microsoft's Azure ML. Filmed in Tampa December 2014
Views: 113 SolidQ
Learn Predictive Modeling Techniques Without Programming and Do Data Mining With IBM SPSS Modeler
 
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http://bit.ly/LearnIBMSPSSModeler Learn Predictive Modeling Techniques Without Programming and How To Do Data Mining With IBM SPSS Modeler. IBM SPSS Modeler is a data mining workbench that helps you build predictive models quickly and intuitively, without programming. Analysts typically use SPSS Modeler to analyze data by doing data mining and then deploying models. Overview: This course introduces students to data mining and to the functionality available within IBM SPSS Modeler. The series of stand-alone videos, are designed to introduce students to specific nodes or data mining topics. Each video consists of detailed instructions explaining why we are using a technique, in what situations it is used, how to set it up, and how to interpret the results. This course is broken up into phases. The Introduction to Data Mining Phase is designed to get you up to speed on the idea of data mining. You will also learn about the CRISP-DM methodology which will serve as a guide throughout the course and you will also learn how to navigate within Modeler. The Data Understanding Phase addresses the need to understand what your data resources are and the characteristics of those resources. We will discuss how to read data into Modeler. We will also focus on describing, exploring, and assessing data quality. The Data Preparation Phase discusses how to integrate and construct data. While the Modeling Phase will focus on building a predictive model. The Evaluation Phase focuses how to take your data mining results so that you can achieve your business objectives. And finally the Deployment Phase allows you to do something with your findings. What are the requirements? This course is for anyone that would like to learn how to use IBM SPSS Modeler. This course is for anyone that would like to learn how to do Data Mining. No statistical or data mining background is necessary. What are you going to get from this course? Over 22 lectures and 4 hours of content! Data Mining and Advanced Analytics Defined Modeling Methods in Modeler CRISP-DM Overview General Modeler Orientation Reading Data Assessing Data Quality Integrating Data Constructing Data Modeling Evaluation Deployment What is the target audience? This course is for anyone that would like to learn how to use IBM SPSS Modeler. This course is for anyone that would like to learn how to do Data Mining. Enroll "IBM SPSS Modeler: Getting Started" Course Here: http://bit.ly/LearnIBMSPSSModeler For More Video Uploads In The Future Please Subscribe To This Youtube Channel by Clicking Subscribe Button Below! and Don't Forget To Giving Your "LIKE" For This Video! Please Click LIKE Button Below! Thanks For Watching The Video! See You Next Time! Note: All The Links In The Video Description Are Affiliate Links, So I Can Make Money If Visitor Purchase The Products!
Data mining, or CRISP-DM.
 
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CU Bookstore representing. I think that counts.
Views: 33 jimmy maximum
Data Science/Machine Learning Project Life cycle
 
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------------------------------------------------------- *About us* Applied AI course (AAIC Technologies Pvt. Ltd.) is an Ed-Tech company based out in Hyderabad offering on-line training in Machine Learning and Artificial intelligence. Applied AI course through its unparalleled curriculum aims to bridge the gap between industry requirements and skill set of aspiring candidates by churning out highly skilled machine learning professionals who are well prepared to tackle real world business problems. *Key highlights of Applied AI course* 1. Job guarantee or money back guarantee 2. Query resolution inside 24 hours 3. Personalized learning path for every course participant 4. 30 Practical Assignments 5. 15 end-to-end case studies based on real world problems across various industries 6. Mentor-ship for portfolio development, resume and interview preparation, and career counseling for every course participant For More information Please visit https://www.appliedaicourse.com/ For any queries you can either drop a mail to [email protected] or call us at +91 8106-920-029 or +91 6301-939-583 Facebook: https://www.facebook.com/appliedaicourse/ Soundcloud: https://soundcloud.com/applied-ai-course Twitter: https://twitter.com/appliedaicourse #AppliedAICourse#Datascience#MachineLearning#ProjectLifecycle
Views: 6654 Applied AI Course
Video presentation 2041 ISTEC 2014 - IPEA Conceptual model
 
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IPEA Conceptual model: Drawing a parallel between CRISP-DM Process model and SIMON’s decision making model
Views: 41 Neels Bezuidenhout
Max Cunha DS4100 Final Project Presentation
 
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A screencast presentation going through my CRISP-DM process for creating multiple regression models with fast cars!
Views: 16 Max Cunha