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: 5185 tutor2u
A data model is like the foundation for your house, get it right and everything else goes better. Join the Power BI desktop team in this session to learn about the key steps, and best practices, you need to take to ensure a good data model.
Views: 105150 Microsoft Power BI
• Counselling Guruji is our latest product & a well-structured program that answers all your queries related to Career/GATE/NET/PSU’s/Private Sector etc. You can register for the program at: https://goo.gl/forms/ZmLB2XwoCIKppDh92 You can check out the brochure at: https://www.google.com/url?q=http://www.knowledgegate.in/guruji/counselling_guruji_brochure.pdf&sa=D&ust=1553069285684000&usg=AFQjCNFaTk4Pnid0XYyZoDTlAtDPUGcxNA • Link for the complete playlist of DBMS is: https://www.youtube.com/playlist?list=PLmXKhU9FNesR1rSES7oLdJaNFgmuj0SYV • Links for the books that we recommend for DBMS are: 1.Database System Concepts (Writer: Avi Silberschatz · Henry F.Korth · S. Sudarshan) (Publisher: McGraw Hill Education) https://amzn.to/2HoR6ta 2.Fundamentals of database systems (Writer:Ramez Elmsari,Shamkant B.Navathe) https://amzn.to/2EYEUh2 3.Database Management Systems (Writer: Raghu Ramkrishnan, JohannesGehrke) https://amzn.to/2EZGYph 4.Introduction to Database Management (Writer: Mark L. Gillenson, Paulraj Ponniah, Alex Kriegel, Boris M. Trukhnov, Allen G. Taylor, and Gavin Powell with Frank Miller.(Publisher: Wiley Pathways) https://amzn.to/2F0e20w • Check out our website http://www.knowledgegate.in/ • Please spare some time and fill this form so that we can know about you and what you think about us: https://goo.gl/forms/b5ffxRyEAsaoUatx2 • Your review/recommendation and some words can help validating our quality of content and work so Please do the following: - 1) Give us a 5-star review with comment on Google https://goo.gl/maps/sLgzMX5oUZ82 2) Follow our Facebook page and give us a 5-star review with comments https://www.facebook.com/pg/knowledgegate.in/reviews 3) Follow us on Instagram https://www.instagram.com/mail.knowledgegate/ 4) Follow us on Quora https://www.quora.com/profile/Sanchit-Jain-307 • Links for Hindi playlists of other Subjects are: TOC: https://www.youtube.com/playlist?list=PLmXKhU9FNesSdCsn6YQqu9DmXRMsYdZ2T OS: https://www.youtube.com/playlist?list=PLmXKhU9FNesSFvj6gASuWmQd23Ul5omtD Digital Electronics: https://www.youtube.com/playlist?list=PLmXKhU9FNesSfX1PVt4VGm-wbIKfemUWK Discrete Mathematics: Relations:https://www.youtube.com/playlist?list=PLmXKhU9FNesTpQNP_OpXN7WaPwGx7NWsq Graph Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesS7GpOddHDX3ZCl86_cwcIn Group Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesQrSgLxm6zx3XxH_M_8n3LA Proposition:https://www.youtube.com/playlist?list=PLmXKhU9FNesQxcibunbD82NTQMBKVUO1S Set Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesTSqP8hWDncxpCj8a4uzmu7 Data Structure: https://www.youtube.com/playlist?list=PLmXKhU9FNesRRy20Hjr2GuQ7Y6wevfsc5 Computer Networks: https://www.youtube.com/playlist?list=PLmXKhU9FNesSjFbXSZGF8JF_4LVwwofCd Algorithm: https://www.youtube.com/playlist?list=PLmXKhU9FNesQJ3rpOAFE6RTm-2u2diwKn • About this video: This video discuss two types of database OLTP and OLAP. What is online transaction processing and what is online analytical processing. Properties of OLTP, Properties on OLAP, type of data in olap, type of data in oltp, what is historical data, where OLTP is used, where OLAP is used, Why we need OLTP and OLAP, Difference between OLTP and OLAP in dbms is discussed. OLAP features: i)stores historical data ii)It is subject oriented iii) It is useful in decision making iv)Used by CEO’s, General managers, high officials of company OLTP features: i)stores current data ii) It is application oriented iii)It is useful for day to day operations iv)Used by clerks, managers and employees of company database tutorial in hindi, definition of data in dbms, components of dbms in hindi,difference between oltp and olap, types of data in dbms dbms tutorials for gate, dbms for beginners in hindi, 3-tier architecture of dbms in hindi,dbms for net,knowledge gate dbms,advantage of dbms, disadvantage of file in dbms, DBMS blueprint, DataBase Management system,database,DBMS, RDBMS, Relations, Table, Query, Normalization, Normal forms,Database design,Relational Model,Instance,Schema,Data Definition Language, SQL queries, ER Diagrams, Entity Relationship Model,Constraints,Entity,Attributes,Weak entity, Types of entity,DataBase design, database architecture, Degree of relation,Cardinality ratio,One to many relationship,Many to many relationships,Relational Algebra,Relational Calculus, Tuples, Natural Join, Join operations,Database Architecture,database Schema, Keys in DBMS, Primary keys, Candidate keys, Foreign keys,Data redundancy, Duplicacy in data, Data Inconsistency, Normalization, First Normal Form,Second Normal Form, third normal forms, Boye codd's normal form,1NF,2NF,3NF,BCNF, Normalization rules, Decomposition of relation, Functional Dependency,Partial Dependency, Multivalued dependency,Indexing,Hashing, B tree,B+ tree,Ordered Indexing,Select operation,Join operations, Natural joins, SQL commands,File structure in DBMS,Primary Indexing,Clustered Indexing,Concurrency control protocols,
Views: 88744 KNOWLEDGE GATE
This video lesson fully explains the concepts of Business Intelligence, OLAP, MDX and and how they apply to Excel 2013. At http://ExcelCentral.com you can view over 850 free Excel video lessons just like this one. All in full HD with vari-speed and human-transcribed subtitles providing the perfect Excel learning environment. You can also track your progress through the course and print a certificate upon completion. Separate videos are provided for Excel 2007, Excel 2010 and Excel 2013. The lesson begins with an explanation of OLAP and its purpose. You'll learn about OLAP Cubes and how they are divided into Dimensions, Measure and Hierarchies to create a multidimensional data structure. You'll also learn about how the MDX query language is used to extract values from OLAP cubes. This lesson also explains the concept of Business Intelligence and how it applies to OLAP. This video comes from the Data Model, OLAP, MDX and BI session (Session 6 in our Excel 2013 Expert Skills free video training course). This session includes the following video lessons: ▪ Lesson 6-1: Understand primary and foreign keys (11m 27s) http://excelcentral.com/excel2013/expert/lessons/06010-understand-primary-key-foreign-key-relationships.html ▪ Lesson 6-2: Create a simple data model (6m 31s) http://excelcentral.com/excel2013/expert/lessons/06020-create-a-simple-data-model.html ▪ Lesson 6-3: Understand OLAP, MDX and Business Intelligence (10m 17s) http://excelcentral.com/excel2013/expert/lessons/06030-what-is-business-intelligence-and-an-olap-cube.html ▪ Lesson 6-4: Use the GETPIVOTDATA function (4m 31s) http://excelcentral.com/excel2013/expert/lessons/06040-use-the-getpivotdata-function.html ▪ Lesson 6-5: Use the CUBEVALUE function to query an OLAP cube (5m 40s) http://excelcentral.com/excel2013/expert/lessons/06050-use-the-cubevalue-function-to-query-an-olap-cube.html ▪ Lesson 6-6: Convert CUBEVALUE functions to include MDX expressions (5m 48s) http://excelcentral.com/excel2013/expert/lessons/06060-convert-cubevalue-functions-to-include-mdx-expressions.html ▪ Lesson 6-7: Understand OLAP pivot table limitations (10m 52s) http://excelcentral.com/excel2013/expert/lessons/06070-understand-olap-pivot-table-limitations.html ▪ Lesson 6-8: Create an asymmetric OLAP pivot table using Named Sets (4m 57s) http://excelcentral.com/excel2013/expert/lessons/06080-create-an-asymmetric-olap-pivot-table-using-named-sets.html ▪ Lesson 6-9: Understand many-to-many relationships (11m 5s) http://excelcentral.com/excel2013/expert/lessons/06090-understand-many-to-many-relationships.html ▪ Lesson 6-10: Create an OLAP pivot table using a many-to-many relationship (12m 47s) http://excelcentral.com/excel2013/expert/lessons/06100-create-an-olap-pivot-table-using-a-many-to-many-relationship.html You can watch any of the 850 Excel video lessons, free and without any required registration at http://excelcentral.com/excel2013/expert/tutorials/default.html.
Views: 259264 ExcelCentral.com
#olap #oltp #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: 124870 Last moment tuitions
Data mining concepts Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.Data mining Data mining involves six common classes of tasks: Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam". Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets. Summarization – providing a more compact representation of the data set, including visualization and report generation.
Views: 631 Technology mart
Description – Dimensional modeling is set of guidelines to design database table structure for easier and faster data retrieval. It is widely accepted technique. Big benefits of using dimensional modeling is its simplicity and faster query performance. In this tutorial, we will talk about Dimensional modeling in Data Warehouse and will see how it is different from ER modeling. We will understand the concept and then we will look into process to design dimensional models. Subscribe - https://www.youtube.com/channel/UCKKcA2ZYYsXb5IUbOKKcRJQ About AroundBI : We are trying to create an educational platform with an aim to provide tutorials in a very simple and understandable way.
Views: 21799 aroundBI
What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning - TEXT MINING definition - TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence. The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
Views: 2677 The Audiopedia
Oracle Advanced Analytics (OAA) Database Option leverages Oracle Text, a free feature of the Oracle Database, to pre-process (tokenize) unstructured data for ingestion by the OAA data mining algorithms. By moving, parallelized implementations of machine learning algorithms inside the Oracle Database, data movement is eliminated and we can leverage other strengths of the Database such as Oracle Text (not to mention security, scalability, auditing, encryption, back up, high availability, geospatial data, etc.. This YouTube video presents an overview of the capabilities for combing and data mining structured and unstructured data, includes several brief demonstrations and instructions on how to get started--either on premise or on the Oracle Cloud.
Views: 2684 Charlie Berger
So, let get this straight... we have gone to the trouble of defining our Dimension Hierarchies to ensure that they are nested correctly and display correctly but they still wont work correctly for aggregation? YEP! This little tid bit is missed by so many self-taught people! What are Dimension Attribute Relationships ? They are the roadmap as to how aggregations are structured in cubes. Without Attribute Relationships, Analysis services would not know how to perform fast and accurate aggregations. For example, using the Date Hiearchy we have been using... How does Analysis services know to obtain the total year sales it only needs to add two numbers together Semester 1 & Semester 2. How does Analysis Services know to add the first 6 months into semester 1 and the second lot of 6 into semester 2? It doesn't! That's the whole point of Attribute Relationships this tells Analysis Services specifically the levels of aggregation and also it can then work out how best to calculate the values above. Remember from earlier we are wanting to view data from the most generic to most specific and to get that kind of optimisation we need to define the "roadmap" of how the aggregations get stored. That's what Attribute Relationships is all about!
Views: 25911 PCTeachME
Step-by-step instructions for calculating the correlation coefficient (r) for sample data, to determine in there is a relationship between two variables.
Views: 502612 Eugene O'Loughlin
This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 5770 SlideTalk
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 8209 The Audiopedia
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters What IS DATA ANALYTICS? Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources. Types of data analytics applications : At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis. Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. At the application level, BI and reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves. Keywords: being a data analyst, big data analyst, business analyst data warehouse, data analyst, data analyst accenture, data analyst accenture philippines, data analyst and data scientist, data analyst aptitude questions, data analyst at cognizant, data analyst at google, data analyst at&t, data analyst australia, data analyst basics, data analyst behavioral interview questions, data analyst business, data analyst career, data analyst career path, data analyst career progression, data analyst case study interview, data analyst certification, data analyst course, data analyst in hindi, data analyst in india, data analyst interview, data analyst interview questions, data analyst job, data analyst resume, data analyst roles and responsibilities, data analyst salary, data analyst skills, data analyst training, data analyst tutorial, data analyst vs business analyst, data mapping business analyst, global data analyst bloomberg, market data analyst bloomberg
Views: 29154 THE MIND HEALING
Customer Relationship Management Through Data Mining
Views: 34 Ricko Tutor Anda
What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning - DATA WAREHOUSE definition - DATA WAREHOUSE 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 In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place and are used for creating analytical reports for knowledge workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting. The typical Extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. The main source of the data is cleansed, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. Mere congregation of data to single database so a single query engine can be used to present data is an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. Add value to operational business applications, notably customer relationship management (CRM) systems. Make decision–support queries easier to write. Optimized data warehouse architectures allow data scientists to organize and disambiguate repetitive data. The environment for data warehouses and marts includes the following: Source systems that provide data to the warehouse or mart; Data integration technology and processes that are needed to prepare the data for use; Different architectures for storing data in an organization's data warehouse or data marts; Different tools and applications for the variety of users; Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes. In regards to source systems listed above, Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases"....
Views: 1668 The Audiopedia
People may know what a healthy romantic relationship looks like, but most don’t know how to get one. Psychologist and researcher Joanne Davila describes how you can create the things that lead to healthy relationships and reduce the things that lead to unhealthy ones using three evidence-based skills – insight, mutuality, and emotion regulation. Share this with everyone who wants to have a healthy relationship. Dr. Joanne Davila is a Professor of Psychology and the Director of Clinical Training in the Department of Psychology at Stony Brook University. She received her PhD in Clinical Psychology from UCLA. Dr. Davila’s expertise is in the area of romantic relationships and mental health in adolescents and adults, and she has published widely in this area. Her current research focuses on romantic competence among youth and emerging adults, the development of relationship education programs, the interpersonal causes and consequences of depression and anxiety, and well-being and relationship functioning among lesbian, gay, and bisexual individuals. Dr. Davila is a Fellow in the Association for Psychological Science and the Incoming Editor (2016-2022) for the Journal of Consulting and Clinical Psychology. Dr. Davila also is a licensed clinical psychologist who specializes in evidence-based interventions for relationship problems, depression, and anxiety. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 2681414 TEDx Talks
Deep Dhillon, former Chief Data Scientist at Alliance Health Networks (now at http://www.xyonix.com), presents a talk titled "Mining Unstructured Healthcare Data" to computational linguistics students at the University of Washington on May 8, 2013. Every day doctors, researchers and health care professionals publish their latest medical findings continuously adding to the world's formalized medical knowledge represented by a corpus of millions of peer reviewed research studies. Meanwhile, millions of patients suffering from various conditions, communicate with one another in online discussion forums across the web; they seek both social comfort and knowledge. Medify analyzes the unstructured text of these health care professionals and patients by performing a deep NLP based statistical and lexical rule based relation extraction ultimately culminating in a large, searchable index powering a rapidly growing site trafficked by doctors, health care professionals, and advanced patients. We discuss the system at a high level, demonstrate key functionality, and explore what it means to develop a system like this in the confines of a start up. In addition, we dive into details like ground truth gathering, efficacy assessment, model approaches, feature engineering, anaphora resolution and more. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 3821 zang0
This video explores some of OLAP's history, and where this solution might be applicable. We also look at situations where OLAP might not be a fit. Additionally, we investigate an alternative/complement called a Relational Dimensional Model. To Talk with a Specialist go to: http://www.intricity.com/intricity101/ www.intricity.com
Views: 378930 Intricity101
Views: 35 Ryo Eng
What is DATA EXPLORATION? What does DATA EXPLORATION mean? DATA EXPLORATION meaning - DATA EXPLORATION definition - DATA EXPLORATION 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 Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data. Data exploration is typically conducted using a combination of automated and manual activities. Automated activities can include data profiling or data visualization or tabular reports to give the analyst an initial view into the data and an understanding of key characteristics. This is often followed by manual drill-down or filtering of the data to identify anomalies or patterns identified through the automated actions. Data exploration can also require manual scripting and queries into the data (e.g. using languages such as SQL or R) or using Excel or similar tools to view the raw data. All of these activities are aimed at creating a clear mental model and understanding of the data in the mind of the analyst, and defining basic metadata (statistics, structure, relationships) for the data set that can be used in further analysis. Once this initial understanding of the data is had, the data can be pruned or refined by removing unusable parts of the data, correcting poorly formatted elements and defining relevant relationships across datasets. This process is also known as determining data quality. At this stage, the data can be considered ready for deeper analysis or be handed off to other analysts or users who have specific needs for the data. Data exploration can also refer to the adhoc querying and visualization of data to identify potential relationships or insights that may be hidden in the data. In this scenario, hypotheses may be created and then the data is explored to identify whether those hypotheses are correct. Traditionally, this had been a key area of focus for statisticians, with John Tukey being a key evangelist in the field. Today, data exploration is more widespread and is the focus of data analysts and data scientists; the latter being a relatively new role within enterprises and larger organizations.
Views: 297 The Audiopedia
Feb.12 -- Google and Amazon are demanding a continuous stream of customer information from smart-home manufacturers, prompting privacy concerns. Bloomberg's Matt Day and John Borthwick, chief executive officer of Betaworks, discuss on "Bloomberg Technology."
Views: 1835 Bloomberg Technology
data dredging (data fishing) - Email Data Supply( http://www.emaildatasupply.com ) Data dredging, sometimes referred to as "data fishing" is a data mining practice in which large volumes of data are analyzed seeking any possible relationships between data. The traditional scientific method, in contrast, begins with a hypothesis and follows with an examination of the data. Sometimes conducted for unethical purposes, data dredging often circumvents traditional data mining techniques and may lead to premature conclusions. Data dredging is sometimes described as "seeking more information from a data set than it actually contains." Data dredging sometimes results in relationships between variables announced as significant when, in fact, the data require more study before such an association can legitimately be determined. Many variables may be related through chance alone; others may be related through some unknown factor. To make a valid assessment of the relationship between any two variables, further study is required in which isolated variables are contrasted with a control group. Data dredging is sometimes used to present an unexamined concurrence of variables as if they led to a valid conclusion, prior to any such study. Although data dredging is often used improperly, it can be a useful means of finding surprising relationships that might not otherwise have been discovered. However, because the concurrence of variables does not constitute information about their relationship (which could, after all, be merely coincidental), further analysis is required to yield any useful conclusions. For More Details visit: Email: [email protected] Website: http://www.emaildatasupply.com
Views: 131 Email Data Supply
Microsoft Power BI - Do it Yourself(DIY) Tutorial - Snowflake Schema & Merge Queries - DIY -11-of-50 by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Power BI - Do it Yourself Tutorial - Snowflake Schema & Merge Queries - DIY -11-of-50 In this video, we will talk about Data Modeling - Snowflake Schema & Merge Queries in Power BI. 1- Please refer to the video #9, where we set the context of implementing a project. 2- Use the PBIX file used in the video #10. 3- Click on the Recent Sources, select the MS Access file - and get the CUST_TYP_CATGRY table. 4- Create a join between D_CUST and CUST_TYP_CATGRY using CST_TYPE. 5- We would like to keep the Star Schema, and not the current SnowFlake. 6- Click on the Edit Queries. 7- From the Home menu on the top, select Merge Queries from the combine section. 8- Select CUST_TYP_CATGRY from the available list of tables and then select CST_TYPE columns from both the tables. 9- Notice that the relationship is not automatically detected in this case. Click on OK - then close and apply. 10- On the relationship view, select the CUST_TYP_CATGRY, right-click and select Hide in Report View. 11- Do the following Hands on exercises. Hands on - DIY #17 1- Download the file from this location https://goo.gl/33P87q 2- Use the same PBIX file as in the DIY#16. 3- Merge F_SHIPMENT and D_STAT Tables. 4- Then hide the D_STAT table from the Report View. Power BI, Do it Yourself Tutorial, Getting Started, Microsoft Power BI, Maps, Clustered & Stacked Visualization, Interactive Visualizations - Slicers, R Integration, Snowflake schema
Views: 5511 BharatiDWConsultancy
The newest data mining methods were incorporated into ESTARD Data Miner for carrying out automated data analysis. To work with this data mining tool you won't need SQL knowledge or long special trainings. The tool is a powerful end-to-end analytical solution: using it withing few clicks you will be able to discover hidden relations in data and to apply discovered knowledge for WHAT-IF analysis and searching for data patterns. Prediction becomes easy as never before. This data mining tool can be used knowledge discovery in various sectors including: * insurance industry * banking * finances * marketing campaigns * accounting & inventory management * healthcare * scientific researches * military sphere. Thanks to built in wizards and user-friendly interface unexperienced users need minimum time to start working with our data mining tool. ESTARD Software: https://secure.avangate.com/affiliate.php?ACCOUNT=HESTARD&AFFILIATE=25621&PATH=http%3A%2F%2Fwww.estard.com
Views: 862 Derrick Pride
Rough Set Theory | Indiscernibility | Set Approximation | Solved Example Rough Set Theory,Its Applications. Basic Concepts of Rough Sets. What is information Systems. How to find Indiscernibility. How to find Lower, Upper and Boundary Approximation of a Set. Link: https://drive.google.com/file/d/1B-Zp9OcB9HN1PSl9UiWhi-u9vdwSr4Nd/view?usp=sharing
Views: 7084 btech tutorial
What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning - PREDICTIVE ANALYTICS definition - PREDICTIVE ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions." In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization. Furthermore, the converted data can be used for closed-loop product life cycle improvement which is the vision of the Industrial Internet Consortium.
Views: 1620 The Audiopedia
Get the latest interview tips,Job notifications,top MNC openings,placement papers and many more only at Freshersworld.com(www.freshersworld.com?src=Youtube). The major role of a BA is – data mining, statistical analysis, predictive modelling and multivariate test. Business Analytics career in India has emerged as the preferred role of choice in IT and ITES industries. The Business Analyst is also required to support decision making roles with real-time analysis. Business Analysts also work closely with the senior management and provide support in data-driven decision making that impacts matters related to product development to marketing. There is a continued strong demand for business analysts especially in India where candidates can land opportunities in IT and ITES industries. According to one article, India has at least 1.2 million business analysts and by 2020 India is pegged to have the highest number of business analysts. Some of the major sectors where Business Analysts have a promising start are retail, banking, healthcare, ecommerce, hospitality, manufacturing etc. Kick-start a Business Analytics career in India One of the most promising career paths in IT today, the job description for business analysts sometimes converge analytics and project management roles. If you are interested in a Business Analyst role, a background in mathematics and engineering is a must, backed by good analytical and communication skills. However, there are those who believe candidates with a business background can also make a career in BA. By upskilling themselves with short online courses, these candidates can also get a good grip on business analytics and start a career. Skills required by Business Analytics candidate: A Business Analyst should be proficient in applied statistics, have knowledge of statistical suite such as SAS, R, SPSS, should know SQL, Hive, knowledge of testibg framework and a working knowledge of BI tools such as Qlik, Tableau, Spot fire among others. Skills may vary depending on the organization’s requirement. However, this is a basic knowledge framework required for making the cut. How can candidates with a business background start a Business Analytics career? While most engineers gravitate towards the data engineering and information management field, candidates with a business background can easily transition into the Business Analyst role. MBA holders can sharpen their skills by a) enrolling in analytics courses, b) participating in mentoring sessions and boot camps to lands their dream job. And though companies don’t expect deep knowledge of tools, a basic understanding can help in landing the right job. Job opportunities for Business Analysts: From leading financial institutions, to consultancies such as Deloitte, E&Y, and global retailers Target, Walmart and online leader Amazon require Business Analytics professionals. Top Employers: Some of the top Business Analytics companies to work for are: • Tata Consultancy Services • Cognizant • Accenture • GENPACT • Wipro • Infosys • IBM • Deloitte • HPE Some of the startups that provide an excellent opportunity to pursue Business Analytics career are Fractal Analytics, Mu Sigma Analytics and Absolut Data. Pay scale: One of the most sought after jobs, business analysts have a rare blend of business and analytical skills and are rewarded with good pay packages. The average salary of senior business analyst is INR 8,59,025 per year. The average salary of BA is INR 6,44,857 per year. Download our app today to manage recruitment whenever and where ever you want : Link :https://play.google.com/store/apps/details?id=com.freshersworld.jobs&hl=en ***Disclaimer: This is just a training video for candidates and recruiters. The name, logo and properties mentioned in the video are proprietary property of the respective organizations. The Preparation tips and tricks are an indicative generalized information. In no way Freshersworld.com, indulges into direct or indirect promotion of the respective Groups or organizations.
Views: 44915 Freshersworld.com Jobs & Careers
Recorded on 30 Oct 2013 at PASS Data Warehousing and Business Intelligence Virtual Chapter (PASS DW/BI VC) Data Lineage is the concept of enabling a client ability to analyze data in a NEW way yet still be able to see the original values, critical in Big Data. Ira Warren Whiteside and Victoria Stasiewicz will demonstrate how to do this in SSIS AND SSAS using Power Pivot, Power BI ,Office 365 and Power Query. We have several case studies for our current clients. Speakers: Ira Warren Whiteside, MDM Architect / BI Architect Ira has over 40 years of IT experience and has extensive knowledge of data warehousing. His roles have included management, Technical Team Lead, Business Analysis, Analytical Application Development, Data Wa rehousing Architecture, Data Modeling, Data Profiling, Data Mining, Text Mining, E-Commerce Implementation, Project Management, Decision Sup port/OLAP, Financial Application Development, E-Commerce, B2C, B2B with primary emphasis in Business Intelligence Applications and Data Warehousing Management. Mr. Whiteside has managed multi-million dollar projects from start to completion. His experience includes the planning, budgeting, project management/technical leadership and product management for large projects and software companies. In addition, Mr. Whiteside has been hands on, in that he has extensively used Microsoft SQL Server 2005/2012 tools, including SSIS, SSAS, SSRS (Microsoft Reporting Services) and Data mining. In addition Ira has authored and published various white papers, articles, provided numerous training seminars and presentations on the methodology required for data-driven application cogeneration in the Microsoft stack. Victoria Stasiewicz, Lead Data profiling Analyst and SSIS developer Victoria is a senior business analyst and data profiling analyst. She has extensive experience in the healthcare industry in analyzing and implementing the Sundial metric decomposition methodology as well as extensive experiencing in developing SSIS packages Join PASS DW/BI Virtual Chapter at http://bi.sqlpass.org Follow us on Twitter @PASSBIVC
ProM is an extensible framework that supports a wide variety of process mining techniques in the form of plug-ins. It is a must-have tool for data scientists interested in processes. See processmining.org for more information on process mining. Download ProM for free from http://www.promtools.org/.
Views: 7070 P2Mchannel
https://store.theartofservice.com/data-mining-high-impact-strategies-what-you-need-to-know-definitions-adoptions-impact-benefits-maturity-vendors.html In easy to read chapters, with extensive references and links to get you to know all there is to know about Data Mining right away, covering: Data mining, Able Danger, Accuracy paradox, Affinity analysis, Alpha algorithm, Anomaly detection, Apatar, Apriori algorithm, Association rule learning, Automatic distillation of structure, Ball tree, Biclustering, Big data, Biomedical text mining, Business analytics, CANape, Cluster analysis, Clustering high-dimensional data, Co-occurrence networks, Concept drift, Concept mining, Consensus clustering, Correlation clustering, Cross Industry Standard Process for Data Mining, Cyber spying, Data Applied, Data classification (business intelligence), Data dredging, Data fusion, Data mining agent, Data Mining and Knowledge Discovery, Data mining in agriculture, Data mining in meteorology, Data stream mining, Data visualisation, DataRush Technology, Decision tree learning, Deep Web Technologies, Document classification, Dynamic item set counting, Early stopping, Educational data mining, Elastic map, Environment for DeveLoping KDD-Applications Supported by Index-Structures, Evolutionary data mining, Extension neural network, Feature Selection Toolbox, FLAME clustering, Formal concept analysis, General Architecture for Text Engineering, Group method of data handling, GSP Algorithm, In-database processing, Inference attack, Information Harvesting, Institute of Analytics Professionals of Australia, K-optimal pattern discovery, Keel (software), KXEN Inc., Languageware, Lattice Miner, Lift (data mining), List of machine learning algorithms, Local outlier factor, Molecule mining, Nearest neighbour search, Neural network, Non-linear iterative partial least squares, Open source intelligence, Optimal matching, Overfitting, Principal component analysis, Profiling practices, RapidMiner, Reactive Business Intelligence, Receiver operating characteristic, Ren-rou, Sequence mining, Silhouette (clustering), Software mining, Structure mining, Talx, Text corpus, Text mining, Transaction (data mining), Weather data mining, Web mining, Weka (machine learning), Zementis Inc.
Views: 159 TheArtofService
Why is a Data Model so important? What is a packaged Data Model? How does a Data Model fit into a Data Warehousing project? This video addresses these basic questions and helps Business Users have realistic expectations about packaged models. To Talk with a Specialist go to: http://www.intricity.com/intricity101/ www.intricity.com
Views: 89579 Intricity101
Matthew L. Jones specializes in the history of science and technology, focused on early modern Europe and on recent information technologies. He chairs the Committee on the Core and Contemporary Civilization. A Guggenheim Fellow for 2012-13 and a Mellon New Directions fellow for 2012-15, he is researching Data Mining: The Critique of Artificial Reason, 1963-2005, a historical and ethnographic account of "big data," its relation to statistics and machine learning, and its growth as a fundamental new form of technical expertise in business and scientific research. Based on research funded by the National Science Foundation, he is finishing a philosophical, technical and labor history of calculating machines from Pascal to Babbage. His publications include: "Improvement for Profit: Calculating Machines and the Prehistory of Intellectual Property," in Mario Biagioli and Jessica Riskin, eds., Nature Engaged: Science in Practice from the Renaissance to the Present (Palgrave-MacMillan, forthcoming); The Good Life in the Scientific Revolution (University of Chicago Press, 2006); "Descartes's Geometry as Spiritual Exercise," Critical Inquiry, 28 (2001). In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 1087 TEDx Talks
Download Excel START File: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerPivot/15Video/015-MSPTDA-ComprehensiveIntroPowerPivot.xlsx Second Excel Start File: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerPivot/15Video/015-WhyDAXandNotStandardPivotTable.xlsx Download Zipped Folder with Text Files: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerPivot/15Video/015-TextFiles.zip Download Excel FINISHED File: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerPivot/015-FinishedDashboard-Finished.xlsx Download pdf Notes: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerPivot/015-MSPTDA-PowerPivotComprehensiveIntroduction.pdf Assigned Homework: Download Excel File with Instructions for Homework: Start Excel File: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerPivot/015-MSPTDA-Homework-Start.xlsx Zipped Data Folder: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerPivot/015-MSPTDA-HomeworkExcelDataFiles.zip Examples of Finished Homework: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerPivot/015-MSPTDA-Homework-Finished.xlsx This video teaches everything you need to know about Power Pivot, Data Modeling and building DAX Formulas, including all the gotchas that most Introductory videos do not teach you!!! Comprehensive Microsoft Power Tools for Data Analysis Class, BI 348, taught by Mike Girvin, Excel MVP and Highline College Professor. Topics: (00:15) Introduction & Overview of Topics in Two Hour Video 1. (04:36) Standard PivotTable or Data Model PivotTable? 2. (05:51) Excel Power Pivot & Power BI Desktop? 3. (12:31) Power Query to Extract, Transform and Load Data to Data Model – Get data from Text Files, Relational Database and Excel File. 4. (25:47) Build Relationships 5. (27:43) Introduction to DAX Formulas: Measures & Calculated Columns 6. (29:15) DAX Calculated Column using the DAX Functions, RELATED and ROUND 7. (31:20) Row Context: How DAX Calculated Columns are Calculated: Row Context 8. (33:49) We do not want to use Calculated Column results in PivotTable using Implicit Measures 9. (34:05) DAX Measure to add results from Calculated Column, using DAX SUM Function. 10. (35:29) Number Formatting for DAX Measures 11. (36:35) Data Model PivotTable 12. (39:31) Explicit DAX Formulas rather than Implicit DAX Formulas 13. (41:50) Show Implicit Measures 14. (45:00) Filter Context (First Look) How DAX Measures are Calculated 15. (50:14) Drag Columns from Fact Table or Dimension Table? 16. (53:30) Hiding Columns and Tables from Client Tool 17. (55:52) Use Power Query to Refine Data Model 18. (57:54) SUMX Function (Iterator Function). DAX Measure for Revenue using the SUMX Function to simulate Calculated Columns in DAX Measures 19. (01:01:00) Compare and Contrast Calculated Columns & Measures 20. (01:04:26) Why We Need a Date Table. Why we do NOT use the Automatic Grouping Feature for a Data Model PivotTable 21. (01:06:46) Build an Automatic Date Table in Excel Power Pivot. And then build Relationship. 22. (01:11:00) Introduction to Time Intelligence DAX Functions. See TOTALYTD DAX Function 23. (01:13:47) Introduction to CALCULATE Function: Function that can “see” Data Model and can change the Filter Context. (01:18:00) Also see the ALL and DIVIDE DAX Functions. Create formula for “% of Grand Total”. Also learn about (01:21:30) Context Transition and the Hidden CALCULATE on all Measures. 24. (01:27:18) DAX Formula Benefits. 25. (01:28:00) Example of DAX Formula that is easier to author than if we tried to do it with a Standard Pivot Table or Array Formulas 26. (01:31:50) AVERAGEX Function (Iterator Function) to calculate Average Daily Revenue. 27. (01:34:00) Filter Context (Second Look) AVERAGEX Iterator Function 28. (01:36:16) Build Dashboard. Create multiple DAX Formulas. Create Multiple Data Model PivotTables and a Data Model Chart. 29. (01:38:38) Create Measures for Gross Profit and Gross Profit % 30. (01:41:27) Continue making more Data Model PivotTables. 31. (01:41:50) Make Data Model Pivot Chart. 32. (01:45:10) Conditional Formatting for Data Model PivotTable. 33. (01:46:22) DAX Text Formula for title of Dashboard 34. (01:47:50) CUBE Function to Convert Data Model PivotTable to Excel Spreadsheet Formulas. 35. (01:50:05) Adding New Data and Refreshing. 36. (01:50:40) Update Excel Power Pivot Automatic Date (Calendar) Table. Clue is the blank in the Dimension Table Filter. 37. (01:52:20) How to Double Check that a DAX Formula is yielding the correct answer? 38. (01:53:22) DAX Table Functions. See CALCULATETABLE DAX Function. 39. (01:55:07) DAX Studio to visualize DAX Table Functions, and to efficiently create DAX Formulas 40. (02:00:12) Existing Connections to import data from Data Model into an Excel Sheet (02:03:15) Summary
Views: 35441 ExcelIsFun
See Full #Data_Mining Video Series Here: https://youtu.be/t8lSMGW5eT0 In This Video You are gonna learn What is Data? What is Information? What is Database? What is Data Warehouse? »See Full #Data_Mining Video Series Here: https://youtu.be/t8lSMGW5eT0 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 ডেটা শব্দটি ল্যাটিন শব্দ ডেটাম এর বহুবচন। যার বাংলায় অর্থ দাঁড়ায় উপাত্ব। নাম্বার, লেটার, সিম্বল সবকিছুই ডেটা। ডেটাকে প্রসেস করলে যা পাওয়া যায় তা-ই মুলত ইনফরমেশন। ডেটা যেখানে রাখা হয় তাকেই ডেটাবেজ বলে। সাধারণত ডেটাবেজে টেবিল, কলাম , রো এর সাহায্যে অরগেনাইজড অবস্থায় ডেটা রাখা হয় । কয়েকটি ছোট ছোট ডেটাবেজ মিলে একটি ডেটা ওয়্যারহাউজ তৈরি করা হয়।
Views: 2185 BookBd
Most of the developers can't differentiate between ODS,Data warehouse, Data mart,OLTP systems and Data lakes. This video explains what exactly is an ODS, how is it different from the other systems. What are its properties that make it unique and if you have an ODS or a warehouse in your organisation
Views: 6661 Tech Coach
The best 5 crm analytics to measure crmsearch. Customer relationship management (crm) analytics gartner it in microsoft dynamics crm analytical meaning and its key featureshow predictive will improve business intelligence zoho. Solutions with the smartest crm analytics technologyadvicehow to make most of agile. Crm (customer relationship management) analytics comprises all programming that analyzes data about an enterprise's customers and presents it so better quicker business decisions can be made. These 4 crms offer analytics crm by agile empowers your sales and marketing teams to make informed decisions take action finding out who did what on 3 may 2016 can help track customer behavior, website traffic, social media engagement a wealth of other variables one the best ways accumulate data is through predictive tools, which extract information various aspects lifecycle. This information is used by businesses for direct marketing, site selection, and customer relationship management 17 aug 2011 infusing crm apps with new predictive analytics tools companies will be able to learn much more about their customers' buying habits advanced helps you easily create sales funnels, know your win loss rates, do predictions, gauge team performance, track oracle contact center telephony. What is crm analytics? Definition from techopedia. What is crm analytics? Definition from whatis searchcrm. What is crm analytics? Definition from techopedia what whatis searchcrm. Here are big data analytics enable to analyze crm patterns in seconds for intelligent pipeline management 4 mar 2014 using and find associations, recognize identify trends that allow a company shape customer. Googleusercontent search. Crm analytics analyze crm patterns in seconds birt. This data may come from your back office erp systems, crm system, social insights, or third party sources 25 nov 2014 as customers generate more with their online activity, the demand for solutions strong analytics has grown. Oracle's crm analytics, part of oracle business intelligence applications family, provides fact peoplesoft analytics closed loop visibility to customer behaviors and interactions throughout the entire lifecycle, giving you buzz in industry is about big data. Crm analytics can be considered a form of online analytical processing (olap) and may employ data mining in crm (customer relationship management) the term customer also called is used to describe an automated methodology about order make better business decisions are software tools interpret, flex act upon volumes information which otherwise remains buried unusable management (crm analytics) refers applications evaluate organization's facilitate streamline (crm) collect, organize synthesize consumer captured across organization help healthcare payers this chapter will cover different ways explore microsoft dynamics service has major role enhancing services answering all questions regarding satisfaction, quality cost process by from behav
Views: 8 Charline Hollar Tipz 2
What is DATA VISUALIZATION? What does DATA VISUALIZATION mean? DATA VISUALIZATION meaning - DATA VISUALIZATION definition - DATA VISUALIZATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information". A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables. Data visualization is both an art and a science. It is viewed as a branch of descriptive statistics by some, but also as a grounded theory development tool by others. The rate at which data is generated has increased. Data created by internet activity and an expanding number of sensors in the environment, such as satellites, are referred to as "Big Data". Processing, analyzing and communicating this data present a variety of ethical and analytical challenges for data visualization. The field of data science and practitioners called data scientists have emerged to help address this challenge. Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information". Indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention. Not limited to the communication of an information, a well-crafted data visualization is also a way to a better understanding of the data (in a data-driven research perspective), as it helps uncover trends, realize insights, explore sources, and tell stories. Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization.
Views: 3045 The Audiopedia
Source: MicroRooster.blogspot.com Format: A MicroStrategy Online Training Video blog. Description: A demo for fact creation, definition and extension. Facts are the building blocks of Metrics and Reports, a well defined fact that is reusable depends on the quality of the data model.
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Please view this video on Full Screen in High Definition - Thank you
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Brian Kokensparger of Creighton University Educational Data Mining uses mainstream data mining methods to work with educational data to accomplish educational objectives . Canvas offers attractive features to support educational data mining efforts. This session will present three ways to collect data from the Canvas LMS, as well as some examples of how Canvas data are already being used in data mining projects. Want to get your feet wet in data mining? This session will show you how.
Views: 674 CanvasLMS
To know more details on Microstrategy click here :- http://bigclasses.com/microstrategy-online-training.html ,visit: +91 800 811 4040 MicroStrategy Desktop • Microstrategy BI architecture • Report Query Execution flow • Folder components • Configuration Object • Schema Object • Public Object • Creating and saving Reports Microstrategy BI Server & Administration • Configuration of Server and Metadata • Configuring Project source • Creating projects • Start/Stop/Restart of the Server • Role of Meta data • Configuring Data Base Instance and Connection Microstrategy Architect • The Logical Data Model • Components of Model • Relating to Microstrategy objects • Over view of Facts • Over View of Attributes • Over View of Hierarchy • The physical Model/ Data warehouse Schema • Components of Model • Relating to Microstrategy objects • Overview of Table • Overview of Column • Overview of Start Schema • Over view of snow flake schema • Browsing warehouse catalogue • Importing table & their components • Creating Facts • Homogeneous & Heterogeneous Facts • Simple &Derived expressions • Creating bulk facts • Creating Attributes • Compound Homogeneous & Heterogeneous Attributes • Simple & Derived expressions • Defining Relationship • Creating bulk attributes • Creating Hierarchies • Creating Transformation Microstrategy Developer • Report Components • Creating metrics • Creating filters • Creating prompts • Report style manipulation • Auto style • Online Mode • Notes • Thresholds • Banding • Report Data Manipulation • Drilling • Page-by • Report Subscription • Report View modes • Graph and priorities • Creating and saving searches Advance MicroStrategy • Creating Advance filters • Creating Advanced metrics • Consolidation • Custom Groups • Defining VLDB Properties • Intelligent cubes • Creating free from SQL reports Microstrategy Report services & Dynamic Dashboards • Dashboards/Documents • Designing Dashboards/Documents • Adding datasets • Views modes of RSD • Displaying Images • Panel stack & Panel • Visual Insight Microstrategy web • Browsing Microstrategy web • Accessing objects via web Micro Strategy Mobile configuration Micro strategy Architecture: •How microstrategy works •How the ETL tool fetches the data and process and loads the data into the target system. •What is Metadata in Microstrategy? •What are the functions or process of Metadata? •How to configure Microstrategy •How to configure Metadata •What kind of information is stored in Meta data •How the information, objects or definitions stored in the Metadata •Creating schema, attributes, hierarchies objects •Configuring of Metadata •Roles of Meta data in Microstrategy • Repository of Microstrategy •Formulating the SQL query by Metadata •Configuration of Microstrategy •Logical modeling and physical modeling •Configuring projects •Designing the entire architecture •Application or Public object MSTR Developer •Connecting into any tier mode •Creating Dashboards, Reports, filters •Tuning the reports •Transformation of data Web component •Access the web •Configuring the web •Microstrategy Projects •Data analysis Once the data is residing into a Data warehouse the BI Tool will fetch the data from the Data warehouse and displays in the form of reports and dashboards as per the designs. When a query is performed in microstrategy by the dashboards, the query does not hits the Data warehouse directly but it configures a component called Metadata. What is Metadata, How does it work Metadata is repository in Microstrategy which stores and which records all the information and definition of the objects that is been created in the environment, whether any object is been created or modified or deleted, the particular definition of the object is recorded in the Meta data. Metadata will read all the definition and formulates the SQL query. The process of Microstrategy Any query through Microstrategy dashboard does not hits the Data warehouse for Microstrategy types of Objects 1.Schema Objects 2.Application of the public Objects What is Schema Object? An attribute is a Schema object which is pointing towards a specific table and a column. For regular Updates on Microstrategy please like our Facebook page:- Facebook:- https://www.facebook.com/bigclasses/ Twitter:- https://twitter.com/bigclasses LinkedIn:- https://www.linkedin.com/company/bigclasses Google+: https://plus.google.com/+Bigclassesonlinetraining Microstrategy Course Page:- http://bigclasses.com/microstrategy-online-training.html Contact us: - India +91 800 811 4040 USA +1 732 325 1626 Email us at: - [email protected]
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