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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: 802 Gregory Rice
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
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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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. ------- 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: 1860 Quantopian
15 Hot Trending PHD Research Topics in Data Mining 2018
 
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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: 4691 PhD Assistance
Stock Price Prediction | AI in Finance
 
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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: 157410 Siraj Raval
How To Get Any Company's Financial Data (FOR FREE!)
 
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Companies House Free Accounts Data Product: http://download.companieshouse.gov.uk/en_monthlyaccountsdata.html Learn Python: https://www.codecademy.com/learn/python If you would like to use the program and/or data shown in this video, please get in touch with us at [email protected] or 01752 588 975.
Views: 2104 Conclient Ltd
Intro and Getting Stock Price Data - Python Programming for Finance p.1
 
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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: 294909 sentdex
Open-Pit Mining: Financial Model
 
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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: 2404 smarthelping
Predicting Stock Prices - Learn Python for Data Science #4
 
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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: 546116 Siraj Raval
How to Read a Research Paper
 
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Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience. 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: http://www.arxiv-sanity.com/ https://www.reddit.com/r/MachineLearning/ https://www.elsevier.com/connect/infographic-how-to-read-a-scientific-paper https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning https://www.reddit.com/r/MachineLearning/comments/6rj9r4/d_how_do_you_read_mathheavy_machine_learning/ https://machinelearningmastery.com/how-to-research-a-machine-learning-algorithm/ http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ 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: 204489 Siraj Raval
Big Data Finance: PhD Thesis in Three Minutes
 
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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
Boston Data Mining | Applying Machine Learning and Science to Trading Decisions
 
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Title & Abstract : "Applying Machine Learning and Science to Trading Decisions" From the view point of a Quantitative Global Macro Investor, the speaker will go over: - Why investors and traders use stop-levels instead of hypothesis tests to exit trades? - Why are the best trading strategies discovered in the search for the surprisingly mundane? - Why searching for profitable strategies leads to bad strategies (and it’s not because of overfitting)? This talk will take a clear look into trade sizing covering Kelly Criterion to the fat tails of the Sharpe Ratio and Correlations that no one talks about in the world of finance and investing. The talk is open to all audiences with an interest in investing. Working examples (IPython/Jupyter Notebooks) will be provided to all that attend and who want to roll up their sleeves and do some python (doing math is always encouraged but optional). The speaker would love to learn more about what parts of the investment process our members are excited about to tailor the presentation material better. The speaker has created a short survey for members who are interested in participating. About Speaker: Sean Kruzel is the Founder and CEO of Astrocyte Research, Inc. Prior to launching his company, Sean worked for large financial institutions and hedge funds where he collaborated in the area of Quantitative Global Macro Strategy and worked as an analyst in fixed income relative-value setting . As CEO of Astrocyte Research, he is currently working towards creating data-driven analysis and frameworks for investors using a combination of industry best practices, novel sources of information and state of the art computation infrastructure. Sean holds a double degree in Mathematics and Economics from Massachusetts Institute of Technology. Fore more info: http://www.meetup.com/Boston-Data-Mining/events/232742130/
Views: 1390 Open Data Science
Link the 3 Financial Statements in Excel - Tutorial | Corporate Finance Institute
 
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Link the 3 Financial Statements in Excel - Tutorial | Corporate Finance Institute Download Excel template: https://corporatefinanceinstitute.com/resources/knowledge/modeling/link-the-3-financial-statements-cfi-webinar/ The 3 financial statements are all linked and dependent on each other. In financial modeling, your first job is to link all three statements together in Excel, so it’s critical to understand how they’re connected. This is also a common question for investment banking interviews, FP&A interviews, and equity research interviews. -- 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
Machine Learning, News Analytics, and Stock Selection
 
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Slides available ► https://goo.gl/Sb5RJu Full Event ► https://goo.gl/LvnmwY Yin Luo, Managing Director, Global Head of Quantitative Strategy, Deutsche Bank. Big data and machine learning have generated tremendous interest in empirical finance research. In this paper, we study a unique news analytics database provided by Ravenpack. We apply a suite of innovative machine learning algorithms, including adaBoost, spline regression, and other boosting/bagging techniques on both traditional and unstructured news data in predicting stock returns. We find news sentiment data adds significant incremental predictive power to our machine learning based global stock selection models. Session recorded June 16, 2016 at the RavenPack 4th Annual Research Conference, titled "Reshaping Finance with Alternative Data". Watch all sessions: ► https://goo.gl/3ij1Ev Visit us at ►https://www.ravenpack.com/ Follow RavenPack on Twitter ► https://twitter.com/RavenPack #RavenPack #finance #sentiment #newsanalytics #bigdata
Views: 7315 RavenPack
What is Business Intelligence (BI)?
 
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There are many definitions for Business Intelligence, or BI. To put it simply, BI is about delivering relevant and reliable information to the right people at the right time with the goal of achieving better decisions faster. If you wanna have efficient access to accurate, understandable and actionable information on demand, then BI might be right for your organization. For more information, contact Hitachi Solutions Canada (canada.hitachi-solutions.com).
Views: 371708 Hitachi Solutions Canada
Cosma Shalizi - Why Economics Needs Data Mining
 
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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: 11421 New Economic Thinking
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
 
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 2115 TEDx Talks
How to become a Data Analyst in India - Course and career
 
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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
Shaplets, Motifs and Discords: A set of Primitives for Mining Massive Time Series and Image Archives
 
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The past decade has seen tremendous interest in mining of time series and shape datasets, as such data can be found in domains as diverse as entertainment, finance, medicine and astronomy. However, much of this work has focused on toy problems, with a few thousand objects. In recent years, our research group has made an effort to address the problems of classification, clustering, query-by-content, motif discovery, and outlier detection on truly massive datasets, with 100 million-plus objects. In this talk we will summarize our research findings over the last two years, and show that a small set of primitives, shaplets, motifs and discords, allow us to solve essentially all problems in shape/time series data mining with efficient, effective and interpretable results. We will demonstrate the utility of our ideas, with case studies in anthropology, astronomy, entomology, historical manuscript annotation and medicine.
Views: 638 Microsoft Research
Sensitivity Analysis for Financial Modeling Course | Corporate Finance Institute
 
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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.
Intoduction to Financial Modeling | Financial Modeling Tutorial | What is Financial Modeling
 
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This Financial Modeling tutorial helps you to learn financial modeling with examples. This video is ideal for beginners to learn the basics of financial modeling. To attend a live session, click here: http://goo.gl/0vZIOF This video helps you learn: • Why Financial Modeling ? • Course Benefits • Who should take this course ? • Case: Beta calculation • Estimating the cost of equity The topics related to 'Financial Modeling' have been widely covered in our course. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (Toll Free) or India: +91-8880862004
Views: 94971 edureka!
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.
Sentiment Analysis for Predicting Stock Prices | Trading Data Science
 
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Does using social media to gauge sentiment accurately reflect stock price movement? See more options trading videos: http://ow.ly/ODhHs On today's Skinny on Options Data Science, Tom and Tony are joined by Mike Rechenthin, PhD (Dr. Data) to discuss how some traders are using sentiment analysis to learn the opinions about specific stocks of thousands of people on social media websites (i.e. Twitter, Facebook, and Yahoo Finance message boards). Dr. Data first explains the algorithm behind sentiment analysis; then describes some of its shortcomings and how the number can be manipulated. While he believes it has useful applications in fields like marketing, he doubts its usefulness for predicting the opinions of investors. Instead of using sentiment analysis to determine a positive or negative opinion of a stock, he suggests relying more on another measure of investor sentiment, an option’s implied volatility. Math is the most feared four-lettered word around, even to Tom and Tony. Luckily the well dressed Dr. Data is here to show how to tame the beast and even use it to make money. Check out his segments on analysis and data manipulation to understand the reasoning behind our trades. You can watch a new Skinny on Options Data Science episode live and check out all previous episodes everyday at http://ow.ly/EoyGW! ======== tastytrade.com ======== Finally a financial network for traders, built by traders. Hosted by Tom Sosnoff and Tony Battista, tastytrade is a real financial network with 8 hours of live programming five days a week during market hours. From pop culture to advanced investment strategies, tastytrade has a broad spectrum of content for viewers of all kinds! Tune in and learn how to trade options successfully and make the most of your investments! Plus, access our visual trading platform, dough, to learn the basics of options trading and manage your portfolio! With hours of tutorial videos and unique tools on a simple, easy-to-use trading interface, dough.com is here to make learning how to trade options fun! Subscribe to our YouTube channel: http://goo.gl/s2bAxF Watch tastytrade LIVE daily Monday-Friday 7am-3:15pmCT: https://goo.gl/OTv3Ez Follow tastytrade: Twitter: https://twitter.com/tastytrade Facebook: https://www.facebook.com/tastytrade LinkedIn: http://www.linkedin.com/company/tastytrade Instagram: http://instagram.com/tastytrade Pinterest: http://www.pinterest.com/tastytrade/
Views: 12551 tastytrade
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: 49843 edureka!
Realising Community Value through Data Mining
 
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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: 88 ADMA
Excel Crash Course for Finance Professionals - FREE | Corporate Finance Institute
 
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Excel Crash Course for Finance Professionals - FREE | Corporate Finance Institute Enroll in the FREE full course to earn your certification and advance your career: http://courses.corporatefinanceinstitute.com/courses/excel-crash-course-for-finance The ultimate Excel crash course for finance professionals. Learn all the Excel tips, tricks, shortcuts, formulas and functions you need for financial modeling in this free online course. Key concepts include: formatting, ribbon shortcuts, if statements, eomonth, year, paste special, fill right, fill down, auto sum, sumproduct, iferror, today(), concatenate, special numbers, vlookup, index, match, xirr, xnpv, yearfrac, and much more. -- 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
What is Ad Hoc Reporting? | Ad Hoc Reporting
 
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What is Ad Hoc Reporting and what are its benefits? Watch this demo to learn about ad hoc reporting and why it's an important feature in embedded reporting tools. To learn more about JReport's ad hoc reporting, visit http://www.jinfonet.com/resources/bi-defined/ad-hoc-reporting/
Social Networks for Fraud Analytics
 
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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: 8940 Bart Baesens
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 231031 CS Dojo
Big data: The Shell investigation - VPRO documentary - 2013
 
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VPRO Backlight examines how you can penetrate into closed strongholds with the help of big data. What do these huge information streams reveal over a multinational like Shell? Ever since the disclosures about the snooping practices of the US and Dutch intelligence services, we are becoming more and more aware of the huge amount of digital data stored over us on the net, in the matrix. But not only data from citizens, but also information about governments and multinationals is being collected. This results in enormous files of many terabytes: big data. The good news is that much of this information is accessible to all of us. You only need to know how to search. In the episode 'Big data: The Shell investigation' VPRO Backlight investigates how these huge data sources make new ways of journalism possible. The case is energy giant Shell. Using a message about a billion debt that Shell would have left for the Iranian regime, VPRO backlight searches and falls into a sea of ​​digital information. This way, we will fish some extraordinary remarkable information about the doings of this Dutch multinational in regard to Iran. The research focuses on Shell's activities in the years 2002 - 2010, the period when the international community decided on a commercial boycott against Iran because of its controversial nuclear program. VPRO Backlight shows how Royal Dutch Shell ended its rogue operations in Iran's "rogue state" and ended up in 2012 with a two billion dollars debt to the Iranian regime. VPRO Backlight also addresses its research on the intimate relationship between Shell and the Dutch government. What role does The Hague play when it comes to Shell's interests abroad and how far is this deliberate diplomacy going? Finally, VPRO backlight asks whether there is a "revolving door" between Shell and the Dutch government. With the use of an interactive research tool - the powerhouse - that was developed specifically for this purpose, Shell's and the government's relationships are being visualized. All this is being investigated with, as a source, the free available big data files about Shell and its trading partners. What is the power of digital resources and how far can big data enrich research journalism? Conversations in this regard bring VPRO Backlight with a number of colleagues including journalist and shell expert Marcel Metze, energy reporter at Dow Jones, Benoit Faucon, ship tracking expert, John van Schaik and Kenneth Cukier, data journalist at The Economist and author of the book ‘Big Data: a revolution that will transform how we live, work, and think’. Originally broadcasted by VPRO in 2013. © VPRO Backlight October 2013 On VPRO broadcast you will find nonfiction videos with English subtitles, French subtitles and Spanish subtitles, such as documentaries, short interviews and documentary series. VPRO Documentary publishes one new subtitled documentary about current affairs, finance, sustainability, climate change or politics every week. We research subjects like politics, world economy, society and science with experts and try to grasp the essence of prominent trends and developments. Subscribe to our channel for great, subtitled, recent documentaries. Visit additional youtube channels bij VPRO broadcast: VPRO Broadcast, all international VPRO programs: https://www.youtube.com/VPRObroadcast VPRO DOK, German only documentaries: https://www.youtube.com/channel/UCBi0VEPANmiT5zOoGvCi8Sg VPRO Metropolis, remarkable stories from all over the world: https://www.youtube.com/user/VPROmetropolis VPRO World Stories, the travel series of VPRO: https://www.youtube.com/VPROworldstories VPRO Extra, additional footage and one off's: https://www.youtube.com/channel/UCTLrhK07g6LP-JtT0VVE56A www.VPRObroadcast.com Credits: Director: Shuchen Tan Research: William de Bruijn Production: Jenny Borger Editors: Frank Wiering, Henneke Hagen In collaboration with MediaFonds / Sandbergen instituut. English, French and Spanish subtitles: Ericsson. French and Spanish subtitles are co-funded by European Union.
Views: 35551 vpro documentary
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1550893 ExcelIsFun
Graph Mining with Deep Learning - Ana Paula Appel (IBM)
 
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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: 276 PAPIs.io
Web Scraping, Data mining and Data Analysis
 
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BINARYBITS WEB SCRAPING COMPANY ACROSS WORLD OFFER WEB SCRAPING, WEB RESEARCH, DATA MINING, EMAIL SEARCHING, SEARCH MISSING INFORMATION, EMAIL DATABASE DEVELOPMENT, PRODUCT SCRAPING AND UPLOADING, DATA ENTRY AND DATA PROCESSING
Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
 
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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: 550281 MBAbullshitDotCom
Analyze Stock Data with Microsoft Excel
 
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Visualization of data is a powerful method to see trends and make decisions. Microsoft Excel trending capabilities are tools to visualize large data sets, such as financial information on company performance.
Views: 17213 APMonitor.com
Andreas Weigend interviewed at Strata Jumpstart 2011
 
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Andreas Weigend (PhD Physics 1991, Stanford University; Diplom Physik, Philosophie 1986, Universitaet Bonn) is an Associate Professor of Information Systems at the Stern School of Business, New York University. He came to Stern from the University of Colorado at Boulder where he had founded the Time Series Analysis Group, after working at Xerox PARC (Palo Alto Research Center) on knowledge discovery. At the Santa Fe Institute, he co-directed the Time Series Prediction Competition that led to the volume Time Series Prediction: Forecasting the Future and Understanding the Past (1994, Addison Wesley). His research focuses on basic methodologies for modeling and extracting knowledge from data and their application across different disciplines. He develops and integrates ideas and analytical tools from statistics and information theory with neural networks and other machine learning paradigms. His approach, basic science on real problems, emphasizes the importance of rigorous evaluations of new methods in data mining. His recent work uses computational intelligence to extract and understand hidden states in financial markets, and to exploit this information to improve density predictions. He has published about one hundred articles in scientific journals, books and conference proceedings. He co-edited four books including Decision Technologies for Financial Engineering (1998, World Scientific). Prof. Weigend received a Research Initiation Award by the National Science Foundation (NSF), a major grant by the Air Force Office of Scientific Research (AFOSR), a Junior Faculty Development Award by the University of Colorado and a NYU Curricular Development Challenge Grant for his innovative course Data Mining in Finance. This course covers the foundations of knowledge discovery, data mining, prediction and nonlinear modeling, as well as specific techniques including neural networks, graphical models, evolutionary programming and clustering techniques. It develops solutions to current problems in finance and includes integrated in-depth projects with major Wall Street firms. Prof. Weigend organized the sixth international conference Computational Finance that took place at Stern on January 6-8, 1999, drawing more than 300 attendees. He has given tutorials and short executive courses on time series analysis, volatility prediction, nonlinear modeling, risk management and decision making under uncertainty and consulted for a broad spectrum of firms ranging from financial boutiques to Goldman Sachs, J. P. Morgan, Morgan Stanley and Nikko Securities.
Views: 494 O'Reilly
[Sponsored Talk] Advancing Data Science for Financial Inclusion: Trusting Social's Journey
 
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Introduction to credit scoring Limitation of credit scoring Alternative credit scoring Data Science challenges in alternative credit scoring How Trusting Social works? Thuong is a research scientist working on advance machine learning and big data to solve financial problem such as credit scoring. His machine learning and data science experience spreads across multiple fields including mobile and social networks, pervasive computing, Internet of Things, e-health, and finance, in both academia and industrial environments. His research works have been published in several leading conferences and journals.
Views: 59 HasGeek TV
28c3: Datamining for Hackers
 
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Download high quality version: http://bit.ly/rBS7SW Description: http://events.ccc.de/congress/2011/Fahrplan/events/4732.en.html Stefan Burschka: Datamining for Hackers Encrypted Traffic Mining This talk presents Traffic Mining (TM) particularly in regard to VoiP applications such as Skype. TM is a method to digest and understand large quantities of data. Voice over IP (VoIP) has experienced a tremendous growth over the last few years and is now widely used among the population and for business purposes. The security of such VoIP systems is often assumed, creating a false sense of privacy. Stefan will present research into leakage of information from Skype, a widely used and protected VoIP application. Experiments have shown that isolated phonemes can be classified and given sentences identified. By using the dynamic time warping (DTW) algorithm, frequently used in speech processing, an accuracy of 60% can be reached. The results can be further improved by choosing specific training data and reach an accuracy of 83% under specific conditions
Views: 7987 28c3
Predicting Peer-to-Peer Loan Default Using Data Mining Techniques - Callum Stevens
 
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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: 648 Callum Stevens
DATA MINING CUP 2017: Interview with the winning team
 
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The winning team (5 students of the Swiss Federal Institute of Technology Lausanne) of the 2017 DATA MINING CUP talking about the competition, the difficulty of the task, their approach to the problem and the importance for students to solve real tasks of the industry. The video was filmed at the prudsys personalization summit - the conference for omni-channel personalization in retail. The DATA MINING CUP as the world renowned student competition for intelligent data analysis combines theory and practice. It has been organized by prudsys AG every year starting in 2000. prudsys AG --- https://prudsys.de https://prudsys.de/blog/ https://summit.prudsys.de http://www.data-mining-cup.de http://www.facebook.com/prudsys http://www.twitter.com/prudsys
Views: 672 prudsys
Range, variance and standard deviation as measures of dispersion | Khan Academy
 
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Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/e/variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/variance-of-a-population?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/box-and-whisker-plots/v/range-and-mid-range?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1280947 Khan Academy
Data analyst scope & Jobs
 
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Transitioning to the field of Data Analytics may not sound easy to do - given the nature of the career and how highly technical the domain is. But we bring to you a plan that will make the transition as smooth as possible. 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.
Views: 562 STUDENT WINDOW
Python for Economists: An overview of Python tools for Economists
 
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Gary Hlusko http://www.pyvideo.org/video/3702/python-for-economists-an-overview-of-python-tool http://pyohio.org/schedule/presentation/167/ Python has developed applications in GIS, text analysis, networks, statistics, csv manipulation, data analysis, data mining and simulations. Despite this, there are few references for using python as an economist. This talk provides an introduction to economic tools using python. I conclude with python in data analysis and future projects for economists using python.
Views: 4119 Next Day Video
What future for Big Data mining?
 
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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: 889 SSIX Project
Ben Goertzel - The Future of A.I.
 
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Source ► https://goo.gl/zu1mjB Ben Goertzel ► https://en.wikipedia.org/wiki/Ben_Goertzel Ben Goertzel is Chief Scientist of financial prediction firm Aidyia Holdings; Chairman of AI software company Novamente LLC, which is a privately held software company, and bioinformatics company Biomind LLC, which is a company that provides advanced AI for bioinformatic data analysis (especially microarray and SNP data); Chairman of the Artificial General Intelligence Society and the OpenCog Foundation; Vice Chairman of futurist nonprofit Humanity+; Scientific Advisor of biopharma firm Genescient Corp.; Advisor to the Singularity University; Research Professor in the Fujian Key Lab for Brain-Like Intelligent Systems at Xiamen University, China; and general Chair of the Artificial General Intelligence conference series, an American author and researcher in the field of artificial intelligence. He is an advisor to the Machine Intelligence Research Institute (formerly the Singularity Institute) and formerly its Director of Research. His research work encompasses artificial general intelligence, natural language processing, cognitive science, data mining, machine learning, computational finance, bioinformatics, virtual worlds and gaming and other areas. He has published a dozen scientific books, 100+ technical papers, and numerous journalistic articles. He actively promotes the OpenCog project that he co-founded, which aims to build an open source artificial general intelligence engine. He is focused on creating benevolent superhuman artificial general intelligence; and applying AI to areas like financial prediction, bioinformatics, robotics and gaming. --------- Facebook: https://www.facebook.com/agingreversed Twitter: https://twitter.com/Aging_Reversed Support the Channel: https://goo.gl/ciSpg1 Channel t-shirt: https://teespring.com/aging-reversed
Views: 20743 Aging Reversed
Working with Time Series Data in MATLAB
 
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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 50888 MATLAB
Algorithmic Bias: From Discrimination Discovery to Fairness-Aware Data Mining (Part 2)
 
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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: 921 KDD2016 video
DATA & ANALYTICS: Analyzing 25 billion stock market events in an hour with NoOps on GCP
 
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Recorded on Mar 23 2016 at GCP NEXT 2016 in San Francisco. Watch how FIS & Google are working to build a next-generation stock market reconstruction system that aims to bring transparency to the US financial markets and drive innovation across financial services. In this video we dive into the proposed system architecture and show how products like Cloud Bigtable, Cloud Dataflow and BigQuery enable this process. As part of the exercise, we ran a load test to process, validate, and link 25 billion US equities and options market events in 50 minutes, generating some impressive statistics in the process. Speakers: Neil Palmer and Todd Ricker from FIS and Carter Page from Google.
Views: 20675 Google Cloud Platform
R vs Python | Best Programming Language for Data Science and Analysis | Edureka
 
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***** Python Online Training: https://www.edureka.co/python ***** ***** R Online Training: https://www.edureka.co/r-for-analytics ***** This Edureka video on R vs Python provides you with a short and crisp description of the top two languages used in Data Science and Data Analytics i.e. Python and R (Blog:http://bit.ly/2ClaowR). You will also see the head to head comparison between the two on various parameters and learn why one is preferred over the other in certain aspects. Following topics are covered in the video: 1:30 Various Aspects of Comparison 1:40 Speed 1:56 Legacy 2:13 Code 2:28 Databases 2:45 Practical Agility 3:10 Trends 3:31 Salary 4:25 Syntax Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------- Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka ------------------------------------------------------------------------------------------------ #PythonVsR #Python #R #Pythononlinetraining #Javaonlinetraining ----------------------------------------------------------------- For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 71977 edureka!