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data mining in banking
 
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Views: 107 nurul husna
8 Data Science Projects in Banking & Finance
 
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Financial data analysis is as much a broad area as Finance. You can use it for managing/mitigating different types of financial risk, taking decisions on investment, managing portfolio, valuing assets etc. Below are a few beginner level projects you can try working on. 1- Build a Credit Scorecard Model - Credit scorecards are basically used to assess credit worthiness of customers. Use German Loan data-set (publicly available credit data) to build credit scorecard for customers. The data set has historical data on default status of 1000 customers and the different factors that are possibly correlated with the customer’s chances of defaulting such as salary age, marital status etc. and attributes of the loan contract such as term, APR rate etc. Build a classification model (using techniques like Logistic Regression, LDA, Decision Tree, Random Forest, Boosting, Bagging) to classify good and bad customers (default and non default customers) and use the model to score new customers in future and lend to customers that have a minimum score. Credit scorecards are heavily used in the industry for taking decisions on grating credit, monitoring portfolio, calculating expected loss etc. 2- Build a Stock Price Forecasting Model - These models are used to predict price of a stock or an index for a given time period in future. You can download stock price of any of the publicly listed companies such as Apple, Microsoft, Facebook, Google from Yahoo finance. Such data is known as uni-variate time series data. You can use ARIMA (AR, MA, ARMA, ARIMA) class of models or use Exponential Smoothing models. 3- Portfolio Optimization Problem - Assume you are working as an adviser to a high net worth individual who wants to diversify his 1 million cash in 20 different stocks. How would you advise him? you can find 20 least correlated stocks (that mitigates the risk) using correlation matrix and use optimization algorithms (OR algos) to find out how you would distribute 1million among these 20 different stocks. 4- Segmentation modelling - Financial services are increasingly becoming tailored made. Doing so helps banks in targeting customers in a in a more efficient way. How do banks do so? They use segmentation modelling to cater differently to different segments of customers. You need historical data on customer attributes & data on financial product/services to build a segmentation model. Techniques such as Decision Trees, Clustering are used to build segmentation models. 5- Revenue Forecasting - Revenue forecasting can be done using statistical analysis as well (apart from the conventional accounting practices that companies follow). You can take data for factors affecting revenue of a company or a group of companies for a set of periods of equal interval (monthly, Quarterly, Half year, annual) to build a regression model. make sure you correct for problem of auto-correlation as the data has time series component and the errors are likely to be correlated (that violates assumptions of regression analysis) 6- Pricing Financial Products : You can build models to price financial products such as mortgages, auto loans, credit card transactions etc. (pricing in this case would be charging right interest rate to account for the risk involved, earn profit from the contract and yet be competitive in the market). You can also build models to price forward, future, options, swaps (relatively more complicated though) 7- Prepayment models - Prepayment is a problem in loan contracts for banks. Use loan data to predict customers could potentially prepay. You can build another model in parallel to this to know if a customer prepays, when is he likely to prepay in the life time of the loan (time to prepay). You may also build a model to know how much loss the company would incur if a section of the portfolio of customer prepay in future. 8 - Fraud Model - These models are being used to know if a particular transaction is a fraudulent transaction. Historical data having details of fraud and non-fraud transactions can be used to build a classification model that would predict chances of fraud happening in a transaction. Since we normally have high volume of data, one can try not just relatively simpler models like Logistic Regression or Decision trees but also should try more sophisticated ensemble models. ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Views: 5213 Analytics University
Demo: IBM Big Data and Analytics at work in Banking
 
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Visit http://ibmbigdatahub.com for more industry demos. Banks face many challenges as they strive to return to pre-2008 profit margins including reduced interest rates, unstable financial markets, tighter regulations and lower performing assets. Fortunately, banks taking advantage of big data and analytics can generate new revenue streams. Watch this real-life example of how big data and analytics can improve the overall customer experience. To learn more about IBM Big Data, visit http://www.ibm.com/big-data/us/en/ To learn more about IBM Analytics, visit http://www.ibm.com/analytics/us/en/
Views: 99325 IBM Analytics
How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 238079 Thales Sehn Körting
An Example Application of Data Mining
 
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Have a look at one of our decision support systems powered by our data mining algorithms.
Bank Fraud Prevention & Detection - The Case for Data Analytics
 
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Join BKD for an informative session exploring data analytics and how it can be used to detect some of the most common fraud schemes affecting banks and other financial institutions. Interested in becoming a BKD Client? Contact a #TrustedAdvisor here: https://bit.ly/2zsU6jO Find us online! Twitter: https://bit.ly/2QY6rTV LinkedIn: https://bit.ly/2DwGGYp Facebook: https://bit.ly/2Igq2e3 Glassdoor: https://bit.ly/2QVdzR0
Views: 15076 BKD CPAs & Advisors
Big Data Use Cases | Banking Data Analysis Using Hadoop | Big Data Case Study Part 1
 
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Big Data Use Cases: Banking Data Analysis Using Hadoop | Hadoop Tutorial Part 1 A leading banking and credit card services provider is trying to use Hadoop technologies to handle an analyse large amounts of data. Currently the organization has data in the RDBMS but wants to use the Hadoop ecosystem for storage, archival and analysis of large amounts of data. let’s get into the tutorial, Welcome to online Big Data training video conducted by Acadgild. This is the series of tutorial consists of real world Big Data use cases. In this project, you will be able to learn, • Understand the Project Requirement • What exactly the project is talking about • From where the data is coming • How the data is getting loaded into Hadoop, and • The different analysis that is performed with the Data Go through the entire video to understand the Big Data problems with finance departments and how to track the data. Enroll for big data and Hadoop developer training and certification to become successful Developer, https://acadgild.com/big-data/big-data-development-training-certification?utm_campaign=enrol-bigdata-usecase-part1-iQrao1C7juk_medium=VM&utm_source=youtube For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 28239 ACADGILD
What Banking Leaders Should Know About Big Data & Analytics
 
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Big data isn’t a new topic, but it’s traditionally been limited to fraud prevention and risk management. Join us for a complimentary webinar that will offer new ways to use big data and analytics to help your institution get ahead of the curve amid heightened regulatory pressures and fierce competition. Upon completion of this webinar, participants will be able to: * Identify new trends and applications of big data in banking * Apply a framework for implementing analytics at your institution * Discuss how analytics can help accomplish your strategic objectives
Views: 126 BKD CPAs & Advisors
Data Mining in Finance - How is Data Mining Affecting Society?
 
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Title of Project/Presentation: Data Mining in Finance - How is Data Mining Affecting Society? Individual Subtopic: Finance Abstract of Presentation/Paper: In today’s society a vast amount of information is being collected daily. The collection of data has been deemed useful and is utilized by many sectors to include finance, health, government, and social media. The finance sector is vast and is implemented in things such as: financial distress prediction, bankruptcy prediction, and fraud detection. This paper will discuss data mining in finance and its association with globalization and ethical ideologies. Description of tools and techniques used to create the presentation: Power Point http://screencast-o-matic.com/
Views: 1401 Gregory Rice
Data Analysis Using R - Session 1 - Bank Marketing
 
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Data Analysis By using Bank Marketing data
Views: 9317 Naveen Balawat
How data mining works
 
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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[8] (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.[9] 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: 636 Technology mart
Credit Card Default - Data Mining
 
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A data mining project as part of requirements for Applied Data Mining at Rockhurst University. This presentation explores the mining of data utilizing R programming. Methods used are Decision Tree and Linear Regression models to predict the outcome of whether a customer will default on their next monthly credit card payment.
Views: 2194 Jonathan Walker
Gaurang Panchal - Data Mining/Machine Learning Project
 
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Dataset: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing# Overview: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. This dataset consists of client information of a bank; 41188 records with 20 inputs, ordered by date (from May 2008 to November 2010). Aim: The classification goal is to predict if the client will subscribe (yes/no) a term deposit. The data includes information about the clients and marketing calls. Together with this data there is a record of whether the clients are currently enrolled for a term deposit. All of the variables should be considered and modeled to produce classification to accurately predict an entry for a client. Attribute Information: Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') # related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. # other attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') # social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')
Views: 595 Gaurang Panchal
SAS Tutorials For Beginners | SAS Training | SAS Tutorial For Data Analysis | Edureka
 
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This SAS Tutorial is specially designed for beginners, it starts with Why Data Analytics is needed, goes on to explain the various tools in Data Analytics, and why SAS is used among them, towards the end we will see how we can install SAS software and a short demo on the same! In this SAS Tutorial video you will understand: 1) Why Data Analytics? 2) What is Data Analytics? 3) Data Science Analytics Tools 4) Why SAS? 5) What is SAS? 6) What SAS Solves? 7) Components of SAS 8) How can we practice Base SAS? 9) Demo Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete SAS Training playlist here: https://goo.gl/MMLyuN #SASTraining #SASTutorial #SASCertification How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course The SAS training course is designed to provide knowledge and skills to become a successful Analytics professional. It starts with the fundamental concepts of rules of SAS as a Language to an introduction to advanced SAS topics like SAS Macros. - - - - - - - - - - - - - - Why Learn SAS? The Edureka SAS training certifies you as an ‘in demand’ SAS professional, to help you grab top paying analytics job titles with hands-on skills and expertise around data mining and management concepts. SAS is the primary analytics tool used by some of the largest KPOs, Banks like American Express, Barclays etc., financial services irms like GE Money, KPOs like Genpact, TCS etc., telecom companies like Verizon (USA), consulting companies like Accenture, KPMG etc use the tool effectively. - - - - - - - - - - - - - - Who should go for this course? This course is designed for professionals who want to learn widely acceptable data mining and exploration tools and techniques, and wish to build a booming career around analytics. The course is ideal for: 1. Analytics professionals who are keen to migrate to advanced analytics 2. BI /ETL/DW professionals who want to start exploring data to eventually become data scientist 3. Project Managers to help build hands-on SAS knowledge, and to become a SME via analytics 4. Testing professionals to move towards creative aspects of data analytics 5. Mainframe professionals 6. Software developers and architects 7. Graduates aiming to build a career in Big Data as a foundational step Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/sas-training Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Sidharta Mitra, IBM MDM COE Head @ CTS , says, "Edureka has been an unique and fulfilling experience. The course contents are up-to-date and the instructors are industry trained and extremely hard working. The support is always willing to help you out in various ways as promptly as possible. Edureka redefines the way online training is conducted by making it as futuristic as possible, with utmost care and minute detailing, packaged into the a unique virtual classrooms. Thank you Edureka!"
Views: 55213 edureka!
What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean?
 
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What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean? EVOLUTIONARY DATA MINING meaning - EVOLUTIONARY DATA MINING definition - EVOLUTIONARY DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes." For instance, a banking institution might want to predict whether a customer's credit would be "good" or "bad" based on their age, income and current savings. Evolutionary algorithms for data mining work by creating a series of random rules to be checked against a training dataset. The rules which most closely fit the data are selected and are mutated. The process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm. Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned, which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results. If data comes from more than one database, they can be integrated, or combined, at this point. When dealing with large datasets, it might be beneficial to also reduce the amount of data being handled. One common method of data reduction works by getting a normalized sample of data from the database, resulting in much faster, yet statistically equivalent results. At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset. The training dataset will be used to let rules evolve which match it closely. The test dataset will then either confirm or deny these rules. Evolutionary algorithms work by trying to emulate natural evolution. First, a random series of "rules" are set on the training dataset, which try to generalize the data into formulas. The rules are checked, and the ones that fit the data best are kept, the rules that do not fit the data are discarded. The rules that were kept are then mutated, and multiplied to create new rules. This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible. When this rule is obtained, it is then checked against the test dataset. If the rule still matches the data, then the rule is valid and is kept. If it does not match the data, then it is discarded and the process begins by selecting random rules again.
Views: 250 The Audiopedia
Data Intensive Banking and Finance
 
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Few industries are as data-intensive as the banking and financial industry. This presents valuable opportunities for the industry to leverage big data and analytics to detect and prevent fraud and better understand customers for improved marketing and customer satisfaction. To learn more about Reliable Software’s solutions for the banking and financial industry, visit www.rsrit.com/banking-and-financial.
How Big Data Is Used In Amazon Recommendation Systems | Big Data Application & Example | Simplilearn
 
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This Big Data Video will help you understand how Amazon is using Big Data is ued in their recommendation syatems. You will understand the importance of Big Data using case study. Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspectives, business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 31926 Simplilearn
Data analytics and machine learning in Banking by Bhushan Sonkusare
 
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Mr. Bhushan Sonkusare, V.P, Publicis. Sapient is enlightening the audience, on "How AI and machine learning are set to transform the banking industry in the future" at 4th International Data Science Summit.
Making Predictions with Data and Python : Predicting Credit Card Default | 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/2eZbdPP]. Demonstrate how to build, evaluate and compare different classification models for predicting credit card default and use the best model to make predictions. • Introduce, load and prepare data for modeling • Show how to build different classification models • Show how to evaluate models and use the best to make predictions For the latest Big Data and Business Intelligence video 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: 31843 Packt Video
BANK GoodCredit Machine Learning BANKING-ML- PR-0015 - #Data #Science #Live #Project
 
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Looking for #Live #Projects on #Data #Science Datamites is providing Data Science courses in Bangalore along with live projects. You can choose either classroom training for certified Data Scientist or ONLINE training. You can learn, Machine learning, Datamining, Deep Learning, AI (Artificial Intelligence), Business Statistics, Tableau along with course. You can choose R / Python programming languages to do this course,. If you are looking for more details about data science course in Bengaluru please visit: https://datamites.com/ You can also learning AI training in Bangalore. For details visit: https://datamites.com/artificial-intelligence-training/courses-bangalore/ All the best, For machine learning courses in Bangalore: https://datamites.com/machine-learning-training/courses-bangalore/ Machine learning training in Hyderabad: https://datamites.com/machine-learning-training/courses-hyderabad/ Machine learning in Pune: https://datamites.com/machine-learning-training/courses-pune/
Views: 2982 DataMites
Big Data in Banking
 
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In this video from the 2013 National HPCC Conference, Bradford Spiers from Bank of America presents: Big Data in Banking. "To some people, Big Data in Banking might relate them to calls from their credit card when a charge seems unusual. To others, it might mean calculations behind low-latency trading. Initially, it seemed to mean just simple Hadoop. Now we see specialization according to the problem we are solving. This talk will discuss different types of Big Data seen in Banking and how one might tie them together to form viable workflows that solve our business and infrastructure challenges." Learn more at: http://hpcc-usa.org
Views: 8051 RichReport
Mining Financial Modeling & Valuation Course - Tutorial | Corporate Finance Institute
 
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Mining Financial Modeling & Valuation Course - Tutorial | Corporate Finance Institute Enroll in our Full Course to earn a certificate and advance your career: http://courses.corporatefinanceinstitute.com/courses/mining-industry-financial-model-valuation Master the art of building a financial model for a mining asset, complete with assumptions, financials, valuation, sensitivity analysis, and output charts. In this course we will work through a case study of a real mining asset by pulling information from the Feasibility Study, inputting it into Excel, building a forecast, and valuing the asset. -- FREE COURSES & CERTIFICATES -- Enroll in our FREE online courses and earn industry-recognized certificates to advance your career: ► Introduction to Corporate Finance: https://courses.corporatefinanceinstitute.com/courses/introduction-to-corporate-finance ► Excel Crash Course: https://courses.corporatefinanceinstitute.com/courses/free-excel-crash-course-for-finance ► Accounting Fundamentals: https://courses.corporatefinanceinstitute.com/courses/learn-accounting-fundamentals-corporate-finance ► Reading Financial Statements: https://courses.corporatefinanceinstitute.com/courses/learn-to-read-financial-statements-free-course ► Fixed Income Fundamentals: https://courses.corporatefinanceinstitute.com/courses/introduction-to-fixed-income -- ABOUT CORPORATE FINANCE INSTITUTE -- CFI is a leading global provider of online financial modeling and valuation courses for financial analysts. Our programs and certifications have been delivered to thousands of individuals at the top universities, investment banks, accounting firms and operating companies in the world. By taking our courses you can expect to learn industry-leading best practices from professional Wall Street trainers. Our courses are extremely practical with step-by-step instructions to help you become a first class financial analyst. Explore CFI courses: https://courses.corporatefinanceinstitute.com/collections -- JOIN US ON SOCIAL MEDIA -- LinkedIn: https://www.linkedin.com/company/corporate-finance-institute-cfi- Facebook: https://www.facebook.com/corporatefinanceinstitute.cfi Instagram: https://www.instagram.com/corporatefinanceinstitute Google+: https://plus.google.com/+Corporatefinanceinstitute-CFI YouTube: https://www.youtube.com/c/Corporatefinanceinstitute-CFI
Introduction to FOREX Data Mining
 
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In this public webinar you will get an introduction to FOREX Data Mining with WEKA using several algorithms and sample data.
Bioinformatics part 2 Databases (protein and nucleotide)
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.[9] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 102531 Shomu's Biology
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 210796 Well Academy
Data Mining Software in Healthcare
 
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This is a brief discussion of data mining software with an emphasis on the healthcare field.
Views: 4302 Joshua White
【TOSHIBA】「Data mining」Productivity improvement at the manufacturing site
 
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Using Artificial Intelligence--or AI--to analyze “Big Data”and automatically identify the causes of manufacturing failures. Productivity improves dramatically.
Detecting E Banking Phishing Websites Using Associative Classification
 
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System uses efficient data mining algorithms to detect and warn user about e banking phishing websites.
Views: 7191 Nevon Projects
StatQuest: Principal Component Analysis (PCA), Step-by-Step
 
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Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is. In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained. There is a minor error at 1:47: Points 5 and 6 are not in the right location If you are interested in doing PCA in R see: https://youtu.be/0Jp4gsfOLMs For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
What is Bitcoin Mining?
 
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For more information: https://www.bitcoinmining.com and https://www.weusecoins.com What is Bitcoin Mining? Have you ever wondered how Bitcoin is generated? This short video is an animated introduction to Bitcoin Mining. Credits: Voice - Chris Rice (www.ricevoice.com) Motion Graphics - Fabian Rühle (www.fabianruehle.de) Music/Sound Design - Christian Barth (www.akkord-arbeiter.de) Andrew Mottl (www.andrewmottl.com)
Views: 6815906 BitcoinMiningCom
AI for Marketing & Growth #1 - Predictive Analytics in Marketing
 
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AI for Marketing & Growth #1 - Predictive Analytics in Marketing Download our list of the world's best AI Newsletters 👉https://hubs.ly/H0dL7N60 Welcome to our brand new AI for Marketing & Growth series in which we’ll get you up to speed on Predictive Analytics in Marketing! This series you-must-watch-this-every-two-weeks sort of series or you’re gonna get left behind.. Predictive analytics in marketing is a form of data mining that uses machine learning and statistical modeling to predict the future. Based on historical data. Applications in action are all around us already. For example, If your bank notifies you of suspicious activity on your bank card, it is likely that a statistical model was used to predict your future behavior based on your past transactions. Serious deviations from this pattern are flagged as suspicious. And that’s when you get the notification. So why should marketers care? Marketers can use it to help optimise conversions for their funnels by forecasting the best way to move leads down the different stages, turning them into qualified prospects and eventually converting them into paying customers. Now, if you can predict your customers’ behavior along the funnel, you can also think of messages to best influence that behavior and reach your customer’s highest potential value. This is super-intelligence for marketers! Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This’ll allow you to focus your team’s efforts on leads with the highest ROI. Which will also imply a shift in mindset. Going from quantity metrics, or how many leads you can attract, to quality metrics, or how many good leads you can engage. You can now easily predict your OMTM or KPIs in real-time and finally push vanity metrics aside. For example, based on my location, age, past purchases, and gender, how likely are you to buy eggs I if you just added milk to your basket? A supermarket can use this information to automatically recommend products to you A financial services provider can use thousands of data points created by your online behaviour to decide which credit card to offer you, and when. A fashion retailer can use your data to decide which shoes to recommend as your next purchase, based on the jacket you just bought. Sure, businesses can improve their conversion rates, but the implications are much bigger than that. Predictive analytics allows companies to set pricing strategies based on consumer expectations and competitor benchmarks. Retailers can predict demand, and therefore make sure they have the right level of stock for each of their products. The evidence of this revolution is already around us. Every time we type a search query into Google, Facebook or Amazon we’re feeding data into the machine. The machine thrives on data, growing ever more intelligent. To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place. 1. The right questions 2. The right data 3. The right technology 4. The right people Ok.. let’s look at some use cases of businesses that are already leveraging predictive analytics. Other topics discussed: Ai analytics case study artificial intelligence big data deep learning demand forecasting forecasting sales machine learning predictive analytics in marketing data mining statistical modelling predict the future historical data AI Marketing machine learning marketing machine learning in marketing artificial intelligence in marketing artificial intelligence AI Machine learning ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/company/growth-tribe Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Snapchat: growthtribe Video URL: https://youtu.be/uk82DHcU7z8
Views: 20946 Growth Tribe
Banking Churn Analysis using  IBM Watson Studio (previously called DSX Local)
 
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This demo is highlighting how machine learning can be used for analyzing banking churn data using IBM Data Science Experience (DSX) Local. The data is loaded from Hortonworks Data Platform. Further information about DSX Local can be found at https://ibm.co/2MEn98S Here is the reference to HDP: https://hortonworks.com/products/data-center/hdp/ Brunel is the visualization tool used to present data. Please review the following link for further information: https://github.com/Brunel-Visualization/Brunel/wiki
Views: 2215 IBM Analytics
Banking analytics: Uncover another dimension of insights
 
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Don’t settle for merely engaging customers at a basic level or tracking money habits; with IBM Analytics, another dimension of banking is on the horizon. Discover how the insights it provides can help you lead your customers exactly where they want to go. Learn more: http://ibm.co/technologyplatform. Also, don't forget to subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub: http://www.ibmbigdatahub.com https://www.youtube.com/user/ibmbigdata https://www.facebook.com/IBManalytics https://www.twitter.com/IBMbigdata https://www.linkedin.com/company/ibm-big-data-&-analytics https://www.slideshare.net/IBMBDA
Views: 285 IBM Analytics
||DATA MINING|| Social Media Behind The Scenes! ||CodeFantasy|| #fightforprivacy
 
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|-CODEFANTASY-| Coding as Your Fantasy ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Your Data is no more Private! Did you ever wonder how Whatsapp earns without publishing any ads on their platform? They tell that your data is Encrypted but seriously? it's Encrypted only for Third parties not for the Company! Watch the video to know how they are selling you and your data and share this as your Contribution for a Change #fightforprivacy We are the team that work and produce legendary tutorials, made and worked out by the processes of Scientific Learning. Please do subscribe and support us for amazing tutorials about to be published. The Name CODE_FANTASY is under copyright and cannot be used any where else!
Views: 467 Code_Fantasy
Congressman Sean Duffy Demands Info on Government Data Mining
 
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Congressman Sean Duffy of Wisconsin grilled the head of the Consumer Financial Protection Bureau on Thursday, September 12, 2013 at a House Committee Meeting. He demanded to know from CFPB head Richard Cordray how many Americans had their financial information data mined without their knowledge.
Views: 42919 Morgan Drexen
Job Roles For DATA ENTRY OPERATOR – Entry Level,DataBase,Arts,Science,WPM, Data Management
 
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Job Roles For DATA ENTRY OPERATOR : Know more about job roles and responsibility in DATA ENTRY . Coming to DATA ENTRY OPERATOR opportunities for freshers in India,Visit http://www.freshersworld.com?src=Youtube for detailed information,Job Opportunities,Education details and Career growth of DATA ENTRY OPERATOR. No matter what your educational background is, data entry operator jobs are available for all fresh candidates. People usually do not seek this position thinking that this is a low-level job. As a matter of fact, it is not lower than any other entry level position in corporate world. The main job of a data entry operator is to update, add and maintain data in a system or managing databases. The data entry operator is expected to insert or add data related to the company (both text and numerical) from a source file provided by the company. The candidate should also verify and sort the information as per given instruction. Other operational work includes generating routine reports and filing documents related to their work. Mostly, freshers with bachelor degree in arts and science are sought for this position. Even diploma candidates are opted for this position by many companies. Usually, candidates with professional degree, master degree or doctorate would not be sought for this position. The basic requirements are a) Knowledge and savvy in computer operation b) Expertise in MS-Office and other related software c) High typing speed – minimum market requirement is 40 WPM with 95% accuracy. d) Basic communication skills in English Usually, the candidates with good computer skill would be sought without regards to their educational background. Rotational shifts are rare and both male and female are sought. This job is also available in working-from-home option in some companies. There are short terms courses with certification for data entry offered by many institutions. Though it is not an essential certification, it would give a competitive edge over other candidates. Those who have working knowledge of Tally are sought for accountancy related data entry with a slightly higher pay. The same goes for those with commerce related educational background. With an increase in growth of BPO industry in India, there is a very high demand for data entry specialists. With one to three years experience in data entry, one can apply for jobs related to data management, document imaging, data mining, data processing and other related fields. If you want to grow in the same field, with three or more years of experience in data entry job, you can apply for senior data entry position or data analyzer positions. With more experience, you can apply for managerial positions like transaction processor, document processor and many others. Your scope is not restricted to back office operations. Candidates with a few years of experience in data entry can take up operational related jobs in KPO and customer service department. Yet, they would be considered as fresher in the new department. This job is for those who do not have a fancy degree and yet, want to take up corporate job. With this job, entry into corporate world becomes easy for all kinds of candidates. The academic excellence is not an important qualification for this job. Thus, candidates with backlog and those with moderate communication skill can apply for this position if, their typing skill is excellent. For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
JP Morgan adds 220 Banks to IIN, A Centralized Help Network, Ripple with XRP Superior
 
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JP Morgan’s Interbank Information Network has grown to more than 220 banks.Today IIN is primarily used to exchange data relating to compliance for payments, as opposed to making the payments. The aim is to address problem payments that take much longer. Most international payments will still use SWIFT with its 11,000 banks. Although the SWIFT GPI upgrade enabled payment tracking, it doesn’t provide the communications needed to resolve compliance queries. https://www.ledgerinsights.com/jp-morgan-blockchain-network-banks/ Should you wish to support me, please watch the ads (without skipping) that are placed by Youtube in the videos. Or donate XRP using the XRP Tip Bot. My twitter handle is: @sentosumosaba or send XRP directly to: rPEPPER7kfTD9w2To4CQk6UCfuHM9c6GDY Required Destination tag 2930921 Thank you so much.
Views: 6567 crypto Eri
Oracle Big Data Analytics Demo mining remote sensor data from HVACs for better customer service
 
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Oracle Big Data Analytics Demo mining remote sensor data from HVACs for better customer service. Oracle Advanced Analytics's Data Mining GUI is used to mine data from remote devices to find problems and improve product customer service. In the scenario, Oracle's Big Data Appliance is positioned to be the initial data collector/aggregator and then the data that is loaded into the Oracle Database. We perform our data mining/predictive analytics on the data while it resides inside the Oracle Database thereby transforming the Database into an Analytical Database.
Views: 2705 Charles Berger
Data mining 7 Trailer
 
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This game on Steam store: https://store.steampowered.com/app/1042150/Data_mining_7/ Data mining 7 - casual colorful minimalist puzzle in which you have to collect all the files that are not corrupted to exit the closed circle. Data mining 7 - casual colorful minimalist puzzle in which you have to collect all the files that are not corrupted to exit the closed circle. The player's goal is to collect all data files, avoiding obstacles and traps, after which the previously closed pass will open to pass the level. In Data mining 7: - 50 levels - Explosions - Traps - Portals - Decelerators - Accelerators - Colorful art - Cool Soundtrack - Achievements Subscribe to our social networks: ¤ Our group on Steam: https://steamcommunity.com/groups/BlenderGames ¤ YouTube: https://www.youtube.com/channel/UCvHzKFgdeYsySm_ieEGPFcA ¤ Twitter: https://twitter.com/blender_games ¤ Facebook: https://www.facebook.com/BlenderGames ¤ Twitch: https://www.twitch.tv/blendergamez
Views: 32 Blender Games
Coursera Course "Process Mining: Data science in Action"
 
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To register visit https://www.coursera.org/course/procmin About the Course Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using a booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action". The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. Course Syllabus This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments. The course covers the three main types of process mining. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases. Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field. See To register visit https://www.coursera.org/course/procmin
Views: 9259 P2Mchannel
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 1020582 David Langer
BJP and Congress spar over hiring the services of controversial data mining firm Cambridge
 
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he BJP and the Congress today sparred over hiring the services of controversial data mining firm Cambridge Analytica, with the ruling party accusing its rival of "data theft" to woo voters ahead of 2019 Lok Sabha polls, a claim the opposition party rejected. The Congress also hit back alleging that the "BJP's factory of fake news has produced one more fake product", and accused it of hiring the firm's services in several elections, including in 2014 Lok Sabha polls. The trading of charges between the two parties came following the Facebook's admission last week that Cambridge Analytica used data that had been collected from 50 million users without their consent, an act of breach of privacy. BJP leader Ravi Shankar Prasad, also the Union law and information technology minister, cited several media reports which said the company would work for Congress chief Rahul Gandhi ahead of the next Lok Sabha polls, and asked how many times Gandhi had met Cambridge's now sacked CEO Alexander Nix. Claiming that the illegal use of people's data from social media could turn out to be the Congress' "biggest scam", BJP spokesperson Sambit Patra said the government would launch a probe into the matter. "Stealing data from social media is your (Congress) weapon. Cambridge Analytica is now Congress analytica," Patra alleged. Prasad alleged that the firm had been accused of using "sex, sleaze and fake news" to influence elections and asked if the Congress too planned to walk the same path. He asked Gandhi to explain the company's role in his social media outreach. Rejecting the allegations categorically, Congress spokesperson Randeep Surjewala said neither his party nor its president has used and hired the services of Cambridge Analytica. "The BJP's factory of fake news has produced one more fake product today. It appears that fake press conferences, fake agendas and fake spins and fake statements have become the everyday character of the BJP and its 'lawless' Law Minister Ravi Shankar Prasad," he told reporters. Claiming that Cambridge Analytica and another Indian firm OBI, run by the son of a NDA leader, compliment each other's businesses, Surjewala said their achievements include managing four election campaigns successfully for the ruling BJP. He claimed that Cambridge's local partner OBI talked of having achieved 'target 272+' (in 2014), providing constituency-wise database to BJP candidates and of extending support to it in national elections and state polls in Haryana, Maharashtra, Jharkhand and Delhi. Subscribe our YouTube channel here: https://www.youtube.com/user/abpnewstv  Like us on Facebook: https://www.facebook.com/abplive/  Follow us on Twitter: https://twitter.com/abpnewstv              And do not miss any updates on our website: http://www.abplive.in/videos
Views: 6798 ABP NEWS
LFS Webcast series - Applying Data-Mining in Finance
 
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Click on the link below to watch the full webcast: https://www.londonfs.com/video/webCast/url/data-mining-in-finance In this webcast Dr Jan De Spiegeleer explores the exciting topic of "big data" and the application of data-mining techniques in finance. Topics covered: - General concept of applying machine learning to financial data: - Cross validation - Supervised vs. unsupervised learning - Classification vs. regression - Data visualization - Toolkit of the Data Scientist: main programming languages used in data science - Big data and big error: how a well-known classification model (Naive Bayes) can fail to achieve a correct classification on a simple dataset - Decision Trees - Case studies: K-Means Clustering & Ridge Regression This video was produced by London Financial Studies Limited.
The Power of Regulatory Data Mining – Real life case studies & concepts - Webinar recording
 
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Achieve 70-90% process time reduction by utilizing machine learning functionalities for Regulatory Data Mining. Dive into artificial intelligence’s success stories with us in this month’s complimentary webinar. We will have an in-depth analysis of the real-life case studies, explaining the business problem as well as the solutions approach. Discover new potentials and opportunities for your organization: 1. DMS Migration: Automated Document Type Detection / Meta Data Extraction 2. eTMF: Automated trial document analysis 3. Automated eCTD Baseline Creation 4. Adverse Event Safety Data Extraction 5. IDMP: SmPC data mining 6. Labeling – Core Data Sheet (CDS) Analysis and Global Labeling Harmonization Recorded webinar (date: 2019-01-30) To learn more about our data mining software, please visit: https://cunesoft.com/en/products/distiller/ https://cunesoft.com/ or write us at [email protected]