Search results “Data mining finance research”
Data Mining in Finance - How is Data Mining Affecting Society?
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: 1446 Gregory Rice
Big Data & Analytics for Finance
Big Data & Analytics is a great opportunity for finance to bring more value to business. How companies can address this challenge? https://www.capgemini.com/consulting-fr/
Views: 15928 Capgemini
Mining Financial Modeling & Valuation Course - Tutorial | Corporate Finance Institute
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
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 2079 Quantopian
New Datasets and Methods in Finance Research
Session: AFA Panel: New Datasets and Methods in Finance Research January 4, 2019 14:30 to 16:30 Hilton Atlanta, Salon West Session Chair: Michael Bailey, Facebook Session type: panel Presented by: Stefano Giglio, Yale University Camelia Kuhnen, University of North Carolina Scott Baker, Northwestern University Rebecca Diamond, Stanford University
Views: 816 afajof
Project Financial data mining
kangaroo insurance model prediction
Views: 51 shangqu liu
Big Data Finance: PhD Thesis in Three Minutes
In this video, I briefly explain my PhD research work which I have been doing at the University of Zurich, Department of Banking and Finance, as a part of the Marie Curie program BigDataFinance: http://bigdatafinance.eu/ You can find more information on the research presented in our publicly available papers: "Agent-Based Model in Directional-Change Intrinsic Time" https://ssrn.com/abstract=3240456 "Instantaneous Volatility Seasonality of Bitcoin in Directional-Change Intrinsic Time" https://ssrn.com/abstract=3243797 This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675044. Content editor and post-production: Alisa Petrova, https://www.youtube.com/channel/UCqJEd9EPcxf-4c13JCK1p9w
15 Hot Trending PHD Research Topics in Data Mining 2018
15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 6004 PhD Assistance
Open-Pit Mining: Financial Model
Get this spreadsheet: http://www.smarthelping.com/2016/11/financial-model-for-open-pit-mining.html Explore all of smarthelping's financial models: http://www.smarthelping.com/p/excel.html **Updating with a DCF analysis, better logic on IRR and ROI basis, better instructions for assumption inputs / value per tonne of ore, and more dynamic cost referencing. There was a fair amount of research that went into gathering all the costs and dimensions needed to give potential miners an idea of the financial implications of running an open-pit operation. One of the more unique features of this financial model is the ability of the user to enter the % of a given ore they expect to have in each tonne of actual ore produced. This ranges from gold and silver to gravel and clay.
Views: 2837 smarthelping
data mining in banking
-- Created using Powtoon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 123 nurul husna
LFS Webcast series - Applying Data-Mining in Finance
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.
Statistical Data Mining Project
Instacart Market Basket analysis : Vineel Patnana, Aditya Sahay
Views: 104 Vineel patnana
Financial Data Mining - Group 2
Fall 2016, UConn
Views: 105 Sudeep Bapat
Stock Price Prediction | AI in Finance
Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow. Code for this video: https://github.com/llSourcell/AI_in_Finance Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://www.datacamp.com/community/tutorials/finance-python-trading http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/ https://www.udacity.com/course/machine-learning-for-trading--ud501 https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 192283 Siraj Raval
Welcome; Geometric Financial Data Mining; Disruption Theory put into Practice
Welcome and Overview Stephen Daffron, Motive Partners Geometric Financial Data Mining Ronald Coifman, Yale University Disruption Theory put into Practice: Finance Services Perspective R.A. Farrokhnia, Columbia Business & Engineering Schools
What future for Big Data mining?
Policymakers are showing growing interest for real-time analysis of public opinion and Big Data. From finance to political campaigners, social media have become a primary source of information, especially when it comes to understanding public opinion trends. However, the potential of social media still needs to be fully exploited. With the explosion of structured and unstructured Big Data, the ability to harness information has become paramount for those who want to successfully use information originating from social media. On the regulatory side, the European Commission wants to promote the data-driven economy as part of its Digital Single Market strategy. The strategy includes better online access and digitalisation as a driver for growth.
Views: 983 SSIX Project
Intro and Getting Stock Price Data - Python Programming for Finance p.1
Welcome to a Python for Finance tutorial series. In this series, we're going to run through the basics of importing financial (stock) data into Python using the Pandas framework. From here, we'll manipulate the data and attempt to come up with some sort of system for investing in companies, apply some machine learning, even some deep learning, and then learn how to back-test a strategy. I assume you know the fundamentals of Python. If you're not sure if that's you, click the fundamentals link, look at some of the topics in the series, and make a judgement call. If at any point you are stuck in this series or confused on a topic or concept, feel free to ask for help and I will do my best to help. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 350924 sentdex
Predicting Peer-to-Peer Loan Default Using Data Mining Techniques - Callum Stevens
Access a shiny web app at: https://callumstevens.shinyapps.io/logisticregression/ View full slideshow presentation at: https://goo.gl/mGMkXI Abstract: Loans made via Peer-to-Peer Lending (P2PL) Platforms are becoming ever more popular among investors and borrowers. This is due to the current economic environment where cash deposits earn very little interest, whilst borrowers can face high interest rates on credit cards and short term loans. Investors seeking yielding assets are looking towards P2PL, however most lack prior lending experience. Lenders face the problem of knowing which loans are most likely to be repaid. Thus this project evaluates popular Data Mining classification algorithms to predict if a loan outcome is likely to be 'Fully Repaid‘ or 'Charged Off‘. Several approaches have been used in this project, with the aim of increasing predictive accuracy of models. Several external datasets have been blended to introduce relevant economic data, derivative columns have been created to gain meaning between different attributes. Filter attribute evaluation methods have been used to discover appropriate attribute subsets based on several criteria. Synthetic Minority Over-sampling Technique (SMOTE) has been used to address the imbalanced nature of credit datasets, by creating synthetic 'Charged Off‘ loans to ensure a more even class distribution. Tuning of parameters has been performed, showing how each algorithm‘s performance can vary as a result of changes. Data pre-processing methods have been discussed in detail, which previous research lacked discussion on. The author has documented each Data Mining phase to allow researchers to repeat tests. Selected models have been deployed as Web Applications, providing researchers with accuracy metrics upon which to evaluate them. Possible approaches to improve accuracy further have been discussed, with the hope of stimulating research into this area.
Views: 683 Callum Stevens
How Data Mining Works: the Logistics
A conversation with University of Georgia assistant professor of computer science Jaewoo Lee. Graphics by Lauren Funk. Link to the New York Times article mentioned: https://www.nytimes.com/2018/03/20/technology/facebook-cambridge-behavior-model.html
Views: 13 Madison Gable
How to become a Data Analyst in India - Course and career
This video discuss How to become a data analyst in India. For more videos on Jobs &Careers :https://www.youtube.com/channel/UCEFTTJFLp4GipA7BLZNTXvA?view_as=subscriber For aptitude classes :https://www.youtube.com/watch?v=lxm6ez2cx6Y&list=PLjLhUHPsqNYnM1DmZhIbtd9wNhPO1HGPT Every business collects data such as sales figures, market research, logistics, or transportation costs. A data analyst's job is to take that data and analyse it to help companies make better business decisions. Some examples of a data analyst basic job functions include: 1) estimating market shares; 2) establishing a price of new materials for the market; 3) reducing transportation costs; 4) timing of sales and 5) figuring out when to hire or reduce the workforce.Data analysts are responsible for collecting, manipulating, and analyzing data. How To Get There? By obtaining a university degree, learning important analytical skills, and gaining valuable work experience, you can become a successful data analyst. A bachelor's degree is needed for most entry-level jobs, and a master's degree will be needed for many upper-level jobs. To become an initial level data analyst, you’ll have to earn a degree in a subject such as mathematics, statistics, economics, marketing, finance, or computer science. Higher level data analyst jobs may require a master’s or doctoral degree, and they usually guarantee higher pay. Individuals looking for data analyst jobs must be knowledgeable in computer programs such as Microsoft Excel, Microsoft Access, SharePoint, and SQL databases. Data analysts also must have good communication skills, as they must have an open line of communication with the companies with which they work. Lets see some of the Best courses on Analytics offered in India. 1. Advanced Analytics for Management – IIM This program enables practitioners, managers, and decision-makers to use advanced analytics for better decision-making 2. Analytics Essentials – IIIT, Bangalore “Analytics Essentials”is a 3 months week-end program by International Institute of Information Technology Bangalore (IIITB)providing a foundational certification course in Business Analytics 3. Business Analytics and Intelligence (BAI) – IIM Bangalore This course provides in-depth knowledge of handling data and Business Analytics’ tools that can be used for fact-based decision-making. The participants will be able to analyse and solve problems from different industries such as manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace etc. 4. Certificate Program in Business Analytics – ISB, Hyderabad A combination of classroom and Technology aided learning platform, .Participants will typically be on campus for a 5 day schedule of classroom learning every alternate month for a span of 12 months, which would ideally be planned to include a weekend. 5. Data Analysis Online courses – SRM University SRM University offers part time online courses in data analysis in collaboration with Coursera, edX, Udacity. 6. Executive Program in Business Analytics – IIM Calcutta This executive 1 year long distance program is designed to expose participants to the tools and techniques of analytics. The program covers topics such as Data Mining, Soft Computing, Design of Experiments, Survey Sampling, Statistical Inference, Investment Management, Financial Modelling, Advanced marketing Research etc. 7. Executive Program in Business Analytics and Business Intelligence – IIM Ranchi Course duration is 3 months. Classes will be conducted by eminent professors and industry experts in the weekends in Mumbai/ Kolkata /Delhi /Bengaluru and in addition to these, there will be one-week learning in IIM Ranchi. 8. Jigsaw Academy courses Jigsaw Academy provides some online analytics courses.Their courses include; Foundation Course in Analytics Data Science Certification Human Resources (HR) Analytics Course Big Data Analytics Using Hadoop and R Advanced Certification in Retail Analytics Advanced Course in Financial Analytics Analytics with R Great Lakes PG Course in Business Analytics 9. M. Tech. Computer Science and Engineering with Specialization in Big Data Analytics – VIT VIT offers full time course in Big Data analysis to promote an academic career for further research in theoretical as well as applied aspects of Big Data Analytics 10. M.Tech (Database Systems) – SRM University SRM University offers a two year full time course in database systems where the students are exposed to theoretical concepts complemented by related practical experiments. 11. M.Tech Computer Engineering and Predictive Analytics – Crescent Engineering College Salary The Salary of Data analysts depends on job responsibilities. An entry-level data analyst with basic technical tools might be looking at anything from Rs. 5 lakhs to 12 lakhs per year. A senior data analyst with the skills of a data scientist can command a high price. #dataanalyst #careeroptions #datascience
Predicting Stock Prices - Learn Python for Data Science #4
In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 607009 Siraj Raval
Sensitivity Analysis for Financial Modeling Course | Corporate Finance Institute
Sensitivity Analysis for Financial Modeling Course | Corporate Finance Institute Enroll in the full course to earn a certificate and advance your career: http://courses.corporatefinanceinstitute.com/courses/sensitivity-analysis-financial-modeling This advanced financial modeling course will take a deep dive into sensitivity analysis with focus on practical applications for professionals working in investment banking, equity research, financial planning & analysis (FP&A), and finance functions. Course agenda includes: Introduction Why perform sensitivity analysis? Model integration - Direct and Indirect methods Analyzing results Gravity sort table Tornado charts Presenting results By the end of this course, you will have a thorough grasp of how to build a robust sensitivity analysis system into your financial model. Form and function are both critical to ensure you can handle quick changes and information requests when you're working on a live transaction.
Algorithmic Bias: From Discrimination Discovery to Fairness-Aware Data Mining (Part 1)
Authors: Carlos Castillo, EURECAT, Technology Centre of Catalonia Francesco Bonchi, ISI Foundation Abstract: Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community. The aim of this tutorial is to survey algorithmic bias, presenting its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions. The tutorial covers two main complementary approaches: algorithms for discrimination discovery and discrimination prevention by means of fairness-aware data mining. We conclude by summarizing promising paths for future research. More on http://www.kdd.org/kdd2016/ KDD2016 conference is published on http://videolectures.net/
Views: 1815 KDD2016 video
SAS Tutorials For Beginners | SAS Training | SAS Tutorial For Data Analysis | Edureka
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: 58294 edureka!
Graph Mining with Deep Learning - Ana Paula Appel (IBM)
Talk Slides: https://drive.google.com/open?id=1nm3jU2sjLxoatWTenffraN3a6xt0QEE8 Deep learning is widely use in several cases with a good match and accuracy, as for example images classifications. But when to come to social networks there is a lot of problems involved, for example how do we represent a network in a neural network without lost node correspondence? Which is the best encode for graphs or is it task dependent? Here I will review the state of art and present the success and fails in the area and which are the perspective. Ana Paula is a Research Staff Member in IBM Research - Brazil, currently work with large amount of data to do Science WITH Data and Science OF Data at IBM Research Brazil. My technical interesting are in data mining and machine learning area specially in graph mining techniques for health and finance data. I am engage in STEAM initiatives to help girls and women to go to math/computer/science are. She is also passion for innovation and thus I become a master inventor at IBM.
Views: 533 PAPIs.io
Future Energy and Mining Finance Will Be an Important Mines and Money Theme in 2017: Andrew Thake
Mines and Money Global Head of Content & Research Andrew Thake, in this interview with SmallCapPower at Mines and Money New York, discusses why the investment conference decided to go to New York and what mining investors are wondering about U.S. President Donald Trump. Find out which themes will be discussed at Mines and Money investment conferences in 2017 by watching our short video.
Views: 114 SmallCapPower
Social Networks for Fraud Analytics
Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud detection it is exactly the opposite. Rather than being a pattern repeatedly popping up in a data set, fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime which appears in many types and forms. As traditional techniques often fail to identify fraudulent behavior, social network analysis offers new insights in the propagation of fraud through a network. Indeed, fraud is not something an individual would commit by himself, but is often organized by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although analyses focus in the first place on fraud detection, the emphasis should shift towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms succeed to keep ahead of new types of fraud and to adapt to changing environment and surrounding effects.
Views: 9487 Bart Baesens
Introduction to FOREX Data Mining
In this public webinar you will get an introduction to FOREX Data Mining with WEKA using several algorithms and sample data.
Data mining applications and techniques
1.Business Sector 2.Marketing and Retailing sector 3.Bio informatics 4.Climatology 5.Banking and Finance 6.Security and Data Integrity 7.E-commerce 8.Forensic and Criminal Investigation 9.Goverment Records 10.Cloud computing
Views: 825 Karthiga Ganesan
Algorithmic Bias: From Discrimination Discovery to Fairness-Aware Data Mining (Part 3)
Authors: Carlos Castillo, EURECAT, Technology Centre of Catalonia Francesco Bonchi, ISI Foundation Abstract: Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community. The aim of this tutorial is to survey algorithmic bias, presenting its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions. The tutorial covers two main complementary approaches: algorithms for discrimination discovery and discrimination prevention by means of fairness-aware data mining. We conclude by summarizing promising paths for future research. More on http://www.kdd.org/kdd2016/ KDD2016 conference is published on http://videolectures.net/
Views: 724 KDD2016 video
Realising Community Value through Data Mining
Prerecorded webinar with Daniel Emerson IAPA INFORMED Webinar April 2015 with Daniel Emerson, PhD. Student at QUT. Daniel shared with IAPA members the results of a research study conducted into road development safety. The focus is on development of a data mining method using the regression tree to analyze enterprise-wide, multivariate heterogeneous data. The method performs sensitivity analysis to determine the critical risk threshold of a variable of interest for each instance in the data, and subsequently evaluates instances to identify those that fall on the high risk section of the curve. The team involved included: Researchers: – Daniel Emerson : MasterIT(Research)/ PhD Student QUT; – A. Prof Richi Nayak: DM researcher/lecturer QUT; – Justin Z. Weligamage: Principal Engineer-Asset Management, Toowoomba Regional Council Programmer: – Dr Reza Hassanzadeh: QUT If you would like to understand the process and thinking more, here’s your chance. You can also download a copy of the presentation at www.iapa.org.au
Views: 90 ADMA
What is FINANCIAL ANALYST? What does FINANCIAL ANALYST mean? FINANCIAL ANALYST meaning & explanation
✪✪✪✪✪ Mobile phone based cryptocurrency. No mining, just visit the app once a day, tap the button and watch your coins grow - https://minepi.com/almir1977 ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is FINANCIAL ANALYST? What does FINANCIAL ANALYST mean? FINANCIAL ANALYST meaning - FINANCIAL ANALYST definition - FINANCIAL ANALYST explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license A financial analyst, securities analyst, research analyst, equity analyst, investment analyst, or rating analyst is a person who performs financial analysis for external or internal financial clients as a core part of the job. Writing by reports or notes expressing opinions is always a part of "sell-side" (brokerage) analyst job and is often not required for "buy-side" (investment firms) analysts. Traditionally, analysts use fundamental analysis principles but technical chart analysis and tactical evaluation of the market environment are also routine. Often at the end of the assessment of analyzed securities, an analyst would provide a rating recommending an investment action, e.g. to buy, sell, or hold the security. The analysts obtain information by studying public records and filings by the company, as well as by participating in public conference calls where they can ask direct questions to the management. Additional information can be also received in small group or one-on-one meetings with senior members of management teams. However, in many markets such information gathering became difficult and potentially illegal due to legislative changes brought upon by corporate scandals in the early 2000s. One example is Regulation FD (Fair Disclosure) in the United States. Many other developed countries also adopted similar rules. Financial analysts are often employed by mutual and pension funds, hedge funds, securities firms, banks, investment banks, insurance companies, and other businesses, helping these companies or their clients make investment decisions. Financial analysts employed in commercial lending perform "balance sheet analysis," examining the audited financial statements and corollary data in order to assess lending risks. In a stock brokerage house or in an investment bank, they read company financial statements and analyze commodity prices, sales, costs, expenses, and tax rates in order to determine a company's value and project future earnings. In any of these various institutions, the analyst often meets with company officials to gain a better insight into a company's prospects and to determine the company's managerial effectiveness. Usually, financial analysts study an entire industry, assessing current trends in business practices, products, and industry competition. They must keep abreast of new regulations or policies that may affect the industry, as well as monitor the economy to determine its effect on earnings. Financial analysts use spreadsheet and statistical software packages to analyze financial data, spot trends, and develop forecasts; see Financial modeling. On the basis of their results, they write reports and make presentations, usually making recommendations to buy or sell a particular investment or security. Senior analysts may actually make the decision to buy or sell for the company or client if they are the ones responsible for managing the assets. Other analysts use the data to measure the financial risks associated with making a particular investment decision. Financial analysts in investment banking departments of securities or banking firms often work in teams, analyzing the future prospects of companies that want to sell shares to the public for the first time. They also ensure that the forms and written materials necessary for compliance with Securities and Exchange Commission regulations are accurate and complete. They may make presentations to prospective investors about the merits of investing in the new company. Financial analysts also work in mergers and acquisitions departments, preparing analyses on the costs and benefits of a proposed merger or takeover. There are buy-side analysts and sell-side analysts.
Views: 53780 The Audiopedia
Bank Marketing Data Mining Project using KNIME
This is the presentation for the Data Mining Project done using Bank Marketing data set for subject 31005 Advance Data Analytic.
Views: 2728 Sunish Manandhar
Data Mining: Exploring the Ethical Dilemmas
This event focused on researchers who employ data mining techniques in their work. In this thematic context we aim to better understand the cross-disciplinary practice of data mining and its associated implications, such as privacy issues, ethics and the interplay with open data. PhD students as well as early career and experienced researchers from around the UK came together to explore how they manage data that they have created when undertaking mining projects, and a panel session helped to identify key questions that researchers face when encountering these implications. For more details on the event, visit www.ses.ac.uk/2018/07/17/data-mining
Data Analyst Interview Questions and Answers - For Freshers and Experienced Candidates
Learn most important Data Analyst Interview Questions and Answers, asked at every interview. These Interview questions will be useful to all entry level candidates, beginners, interns and experienced candidates interviewing for the role of Data Analyst across various domains like banking, financial, marketing, statistical etc. The examples and sample answers with each question will make it easier for candidates to understand these conceptual, situational and behavioral interview questions.
Views: 46798 CareerRide
Cosma Shalizi - Why Economics Needs Data Mining
Cosma Shalizi urges economists to stop doing what they are doing: Fitting large complex models to a small set of highly correlated time series data. Once you add enough variables, parameters, bells and whistles, your model can fit past data very well, and yet fail miserably in the future. Shalizi tells us how to separate the wheat from the chaff, how to compensate for overfitting and prevent models from memorizing noise. He introduces techniques from data mining and machine learning to economics -- this is new economic thinking.
Views: 11666 New Economic Thinking
SPS2017: Educational Data Mining Software
The video is giving details about research software developed using WEKA (Open source Data Mining tool) and JAVA (Programming Language). The first version is developed in 2017. Anyone having the link can download this software and directly use this software without any installation. All the instructions are given in 'README.txt' file in a downloaded zip folder. The link to download the setup will be provided on request. Any suggestions and questions are invited in the comment section below. Feel free to add below. Developer: Er. Prabhjot Kaur Music Credits: Youtube Audio Library
Views: 95 Prabhjot Kaur
Context Summits Miami 2019: The Role of Alternative Data within Investing Panel
Michael Marrale, CEO of M Science, together with Fidelity's Head of Advanced Data Analytics John Avery and Kirk McKeown, Head of Proprietary Research at Point72, joins a panel moderated by Dan Furstenberg, Jefferies’ Global Head of Hedge Fund Distribution and Head of Data Strategy, at Context Summits Miami 2019. Held at The Fontainebleau Miami Beach on January 30, 2019, Michael shares his insights on “The Role of Alternative Data & Machine Learning within Fundamental, Quantitative & Quantamental Investing.” About M Science M Science is a data-driven research and analytics firm, uncovering new insights for leading financial institutions and corporations. M Science is revolutionizing research, discovering new data sets and pioneering methodologies to provide actionable intelligence. Our research teams have decades of experience working with unstructured data in near real-time to discern critical insights that help clients make smarter, more informed decisions. We combine the best of finance, data and technology to create a truly unique value proposition for both financial services firms and major corporations in a variety of industries. M Science is a portfolio company of Leucadia Investments, a division of Jefferies Financial Group Inc. (NYSE: JEF). Website: https://www.mscience.com/ LinkedIn: https://www.linkedin.com/company/m-science-llc/ Twitter: https://twitter.com/msciencellc?lang=en Facebook: https://www.facebook.com/MScienceLLC/ Michael Marrale LinkedIn: https://www.linkedin.com/in/marrale/ Twitter: https://twitter.com/MichaelVMarrale Instagram: https://www.instagram.com/michaelmarrale/ Facebook: https://www.facebook.com/michael.marrale.75 John Avery LinkedIn: https://www.linkedin.com/in/john-avery-a377077/ Company Website: https://www.fidelity.com/ Kirk McKeown Company Website: https://www.point72.com/ Dan Furstenberg LinkedIn: https://www.linkedin.com/in/dan-furstenberg-990a0562/ Instagram: https://www.instagram.com/defursty/ Company Website: http://www.jefferies.com/ Context Summits Website: https://contextsummits.com/ LinkedIn: https://www.linkedin.com/company/context-summits/ Twitter: https://twitter.com/contextsummits Facebook: https://www.facebook.com/ContextSummits/ YouTube: https://www.youtube.com/channel/UCSc6v5OAur4v_ouZnWssfLQ Context Summits Miami 2019 Website: https://contextsummits.com/miami19/
Views: 352 M Science LLC
Gene Ekster – The Alternative Data revolution on Wall St
This talk will focus on the role that non-traditional data research, known as alternative data, is beginning to play across the investment community. We will address how datasets such as point of sale transactions, web site usage, municipality records, social media data and similar information are being utilized by traditional long-short funds, quantitative hedge funds and also mutual funds. Topics covered will include aspects of the developing alternative data ecosystem including: * Alternative data R&D process flow * Computing infrastructure and the technology stack * Research & analytics providers * Technical solutions to common issues found in alt. data * Best practices We’re going to walk through a few examples of how noisy, unstructured data become an investable signal using tools such as text mining and machine learning. The aim is to introduce the audience to the process of how hedge fund portfolio managers and sell-side research analysts are systematically generating returns by leveraging unique primary (bots / scrapers, channel checks) and third party datasets (including data brokers). This includes sourcing, compliance, scrubbing out PII, alpha generation related to revenue estimates and approaches to balance the secret sauce with product transparency. Finally, we’ll ponder the future of alternative data in finance and touch on how companies in the data space can best take advantage of this growing trend. Gene Ekster was previously head of R&D at Point72 Asset Management (formerly SAC Capital), a Director of Data Product at 1010Data and a Senior Analyst at Majestic Research (now ITG Investment Research). Currently, Gene works with asset management firms and data providers in a consulting capacity to help integrate alternative data into the investment process. He can be reached via LinkedIn (https://www.linkedin.com/in/geneekster). This talk was recorded at The Fifth Elephant 2016, India's premier data analytics conference.
Views: 2602 HasGeek TV
Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Views: 572877 MBAbullshitDotCom
Top 4 Best Laptops for Data Analysts
I highlight the top 4 best laptops for data analysts looking to enter the data science field. Data analytics is a hot topic, what do you need to become a successful data analysts? ►►► Full List of Laptops Below - Don't Miss It! ► You May Also Like - Data Analyst Job Description ( http://bit.ly/2DySEjP ) ► Or Check out the Full Playlist for Data Analysts ( http://bit.ly/2mB4G0N ) Top 4 Best Laptops for Data Analysts ► Dell XPS 15 ( http://amzn.to/2CWz8cr ) ► Razer Blade Stealth ( http://amzn.to/2EHGZ2M ) ► Macbook Pro 15 ( http://amzn.to/2sFIEQu ) ► MSI GS63VR Stealth Pro ( http://amzn.to/2FxJLnI ) In a world of technology and options what is the right choice when choosing a computer for your new career in the data science industry. I hope this video helps you decide how to pick the best pc (personal computer) for your work as a data analyst. I receive a lot of questions on what are the best tools and resources. Here are some of the top questions: - Mac vs PC for Data analysts? - Mac vs windows for Data analysts? - What is the best laptop for data analysts? - What are the top 4 laptops for data analysts? - What is the best laptop for Data Science? - How to choose the right laptop for data analytics? - Best Laptops for Data Analytics 2018 Here at Jobs in the Future we have spent a great deal of time researching and informing you about the Data Science industry, and we will continue to do so with practical, informative content that doesn't weigh you down with all the jargon. Today I want to touch on what gear you need to become a well outfitted Data Analyst. 1 ) Dell XPS 15 (Top Recommendation) Talk about Fast! Check out the specs on this beast. full 32GB of RAM, 1TB SSD, and a NVIDIA GeForce GTX 1050 - 15.6-inch 4K Ultra HD (3840 x 2160) InfinityEdge touch display - 32 GB DDR4-2400MHz, No Optical Drive - 1TB PCIe Solid State Drive - 7th Generation Intel Core i7-7700HQ Quad Core Processor (6M cache, up to 3.8 GHz), NVIDIA GeForce GTX 1050 4GB DDR5 Graphics 2 ) Razer Blade Stealth (not for Data Science) Still very fast, but do be warned. You cannot swap out the 16GB RAM. It is soldered to the mother board. Major kill-joy. - 4K Display (3840x2160) - 12.5” IGZO 16:9 Touchscreen - 512GB ultra-fast PCIe SSD - 7th Gen Intel Core i7-7500U Dual-Core Processor with Hyper - Threading 2.7GHz / 3.5GHz (Base/Turbo) - 16GB dual-channel onboard memory (LPDDR3-1866MHz) - Thunderbolt 3 (USB-C) - Intel HD Graphics 620 (Makes this not the best Data Science computer, but still a great choice for data analyst) 3 ) Macbook Pro 15 (For the Apple Gurus) I know, you were getting worried. You thought I was going to leave out the trusty go to, the Macbook Pro. Well here it is! But be warned. Just like the Razer, the Macbook Pro has its 16GB RAM Soldered to the motherboard. This is why I placed the Dell XPS 15 and the MSI GS63VR on this list of machines. - 2.9GHz quad-core 7th-generation Intel Core i7 processor - Turbo Boost up to 3.9GHz - 16GB 2133MHz LPDDR3 memory - 512GB SSD storage - Radeon Pro 560 with 4GB memory (GPU) - Four Thunderbolt 3 ports 4 ) MSI GS63VR Stealth Pro (The Top Performer) This computer is simply beyond reason. Pack with power. Ready to handle whatever data job you can throw at it. - Display: 15.6" Full HD - Anti-Glare Wide View Angle 1920x1080 - Processor: Intel Core i7-7700HQ (2.8-3.8GHz) - Graphics Card: NVIDIA's GTX 1070 8G GDDR5 Max Q - RAM: 32GB (16GB x2) DDR4 2400MHz - Hard Drive: 512GB SSD + 1TB (SATA) 5400rpm ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 Udemy ► http://fxo.co/52oG Envato ► http://bit.ly/2CSoROx edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser Host Gator ► http://bit.ly/2CBonPO ( Get 60% off Website Hosting with the link ) ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support! Disclaimer - Links in this description are affiliate links. Which means I receive a small commission when you purchase a product at NO extra cost to you.
Views: 35955 Ben G Kaiser
REIS Episode 257: Paul Del Pozo: Data Mining for Leads
Paul Del Pozo Investment Group, LLC. is a South Florida based Real Estate Investment Company. He specializes in Buying, Selling, Rehabbing, and Flipping Wholesale properties. In his free time he lives in a fitness world building a health and bodybuilding background. What you’ll learn about in this episode: • How Paul went from being a bodybuilder and personal trainer to becoming a successful real estate investor • Paul’s process of obtaining and working leads, networking, and learning the fundamentals of wholesaling • Why Paul believes it’s important to develop your skills in finding leads before you do anything else • How Paul integrates technology into the operation of his real estate business • How Paul uses a MLS data program called Propstream to find lists and comps, and how it has changed his business • How Propstream allows you to filter lists based on different categories like equity, property characteristics and more • How Paul has found financial independence in the 3 1/2 years that he’s been working in real estate • Why getting into real estate has been a catalyst of self-improvement even in other areas of Paul’s life • Which markets Paul is working in now, and why he has been moving his focus more into cash flow • How the slogan “Flex and Flip” has become a cornerstone of Paul’s business philosophy and his professional calling card Resources: • http://REInvestorSummit.com/Data • http://REInvestorSummit.com/Machine • http://REInvestorSummit.com/Everywhere • http://REInvestorSummit.com/aof • http://REInvestorSummit.com/coaching Love the show? Subscribe, rate, review, and share! Here’s How » https://reinvestorsummit.com/how-to-subscribe-rate-our-podcast-5-stars-on-itunes/ Join the Real Estate Investor Summit Community: http://reinvestorsummit.com/ https://www.facebook.com/1000Houses/ https://twitter.com/mitch_stephen https://www.youtube.com/channel/UC3fkChxDhvYJyEdof5RhjAw https://www.linkedin.com/in/mitch-stephen-0b32491b/
Views: 161 Mitch Stephen
FNP 2018 🔴 Part 1
The 1st Financial Narrative Processing (FNP) Workshop at LREC 2018. We are live streaming the even including the talks and questions. For more info: http://wp.lancs.ac.uk/cfie/fnp2018 The workshop will focus on the use of Natural Language Processing (NLP), Machine Learning (ML), and Corpus Linguistics (CL) methods related to all aspects of financial text mining and financial narrative processing (FNP). There is a growing interest in the application of automatic and computer-aided approaches for extracting, summarising, and analysing both qualitative and quantitative financial data. In recent years, previous manual small-scale research in the Accounting and Finance literature has been scaled up with the aid of NLP and ML methods, for example to examine approaches to retrieving structured content from financial reports, and to study the causes and consequences of corporate disclosure and financial reporting outcomes. One focal point of the proposed workshop is to develop a better understanding of the determinants of financial disclosure quality and the factors that influence the quality of information disclosed to investors beyond the quantitative da ta reported in the financial statements. The workshop will also encourage efforts to build resources and tools to help advance the work on financial narrative processing (including content retrieval and classification) due to the dearth of publicly available datasets and the high cost and limited access of content providers. The workshop aims to advance research on the lexical properties and narrative aspects of corporate disclosures, including glossy (PDF) annual reports, US 10-K and 10-Q financial documents, corporate press releases (including earning announcements), conference calls, media articles, social media, etc.
Views: 73 Mahmoud El-Haj
Knowledge discovery and data mining in pharmaceutical cancer research (KDD 2011)
Knowledge discovery and data mining in pharmaceutical cancer research KDD 2011 Paul Rejto Biased and unbiased approaches to develop predictive biomarkers of response to drug treatment will be introduced and their utility demonstrated for cell cycle inhibitors. Opportunities to leverage the growing knowledge of tumors characterized by modern methods to measure DNA and RNA will be shown, including the use of appropriate preclinical models and selection of patients. Furthermore, techniques to identify mechanisms of resistance prior to clinical treatment will be discussed. Prospects for systematic data mining and current barriers to the application of precision medicine in cancer will be reviewed along with potential solutions.
Behavior Research in AIS - Current Topics in Auditing - Spring 2018 - Professor Vasarhelyi
Current Topics In Auditing Lecture 9 Professor Vasarhelyi March 20, 2018 Please visit our website at http://raw.rutgers.edu Time Stamps: Topic 1: The Impact of Initial Information Ambiguity on the Accuracy of Analytical Review Judgments. 0:04:15 Background & Research 0:04:53 Concepts 0:07:18 Hypotheses 0:07:59 Experiments 0:09:51 Results 0:11:43 Additional Test 0:18:45 Conclusion & Contributions Topic 2: Attention-Directing Analytical Review Using Accounting Ratios: A Case Study 0:26:19 Attention-Directing Analytical Review 0:27:05 Background 0:27:53 Case Study Method 0:29:01 The Case Firm 0:29:48 Often Used Ratios 0:30:07 Types of Errors 0:30:45 Errors’ Effects 0:31:20 Investigating Rules 0:34:31 Conclusions Topic 3: Clustering Based Peer Selection with Financial Ratios 0:48:17 Research Objectives and Contributions 0:49:28 Motivation 0:52:53 Existing Classification 0:55:09 Why base clustering on financial ratios? 0:57:47 Research Methodology 1:14:53 How are Comparisons Made Across Classification Schemes? 1:17:57 SIC vs Clustering - Within Group Dispersion 1:20:38 Comparison of Adjusted R-Squared 1:21:08 Conclusion Topic 4: Adding an Accounting Layer to Deep Neural Network: 1:29:34 Financial Statements Fraud and Earnings Management 1:30:10 Fraud Detection, Data Mining, and Audit Analytics 1:30:31 Framework Topic 5: A Field Study on the Use of Process Mining of Event Logs as an Analytical Procedure in Auditing 1:59:23 Introduction 2:00:58 Protocol for Applying Process Mining 2:04:28 Field Study Site 2:05:38 Identify the Designed Process 2:07:28 Preliminary Analysis of the Event Log 2:09:29 Process Discovery 2:16:26 Role Analysis 2:20:27 Verification by Attribute Analysis 2:29:18 Social Network Analysis 2:30:02 Research Implications of Process Mining 2:30:31 Conclusion Please subscribe to our channel to get the latest updates on the RU Digital Library. To receive additional updates regarding our library please subscribe to our mailing list using the following link: http://rbx.business.rutgers.edu/subscribe.html
Bart de Moor - Serious Data, Serious Mining
Big data, data mining, machine learning, predictive analytics, etc.: All of these have become buzzwords in today’s information driven world, that is overwhelmed by a tsunami of data. For sure, in many applications, the mere size of the database is a real challenge, with of course Google as a most prominent example. But not only size matters. In many applications, the data are ‘technical’ and ‘complicated’, and the objectives of the mining exercise are also technical and/or economical, with a clear return-on-investment. In this presentation, we will talk about ‘serious data’, by which we basically mean those data for which an in depth know-how and understanding of the field and application is mandatory. Examples are biomedical and health data (think of genomics, decision support tools), industrial data (process industry monitoring and control), environmental (micro-climate simulations), financial (fraud detection, bank customer modeling), smart city applications (energy grid monitoring), etc. We will also talk about‘serious mining’, by which we mean that we use a full toolbox of advanced machine learning algorithms, including system identification methodologies for dynamical systems and time series, clustering, classification, ranking algorithms, etc. In our lecture, we will first give a broad overview of the general trends that explain the tsunami of data in technical applications. Then we will briefly elaborate on the necessary ingredients for data mining (compute infrastructure, storage, analytics, visualization, security). Of utmost importance before the mining exercise can even start, is a clear enunciation of the objectives. We will show examples in ICT, Finance, Education, Smart Cities, Health and then enumerate the mining tasks that one can formulate. We will briefly dwell into the typical work package partitioning of a data-mining project, elaborate on advanced algorithms we use, and finish with use cases from load forecasting on the national electricity grid in Belgium, industrial process monitoring, social network clustering, financial fraud detection and finally several health and genomics related projects. Bart De Moor obtained his Master Degree in Electrical Engineering in 1983 and a PhD in Engineering in 1988 at the KU Leuven. For 2 years, he was a Visiting Research Associate at Stanford University (1988-1990) at the departments of EE (ISL, Prof. Kailath) and CS (Prof. Golub). Currently, he is a full professor at the Department of Electrical Engineering in the research group STADIUS and the Scientific Director of the iMinds Future Health Department. His research interests are in numerical linear algebra, algebraic geometry and optimization, system theory and system identification, quantum information theory, control theory, datamining, information retrieval and bio-informatics (see publications on http://www.bartdemoor.be).
Views: 385 SVV Lab
Time Series data Mining Using the Matrix Profile part 1
Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 1 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 3230 KDD2017 video