Search results “Financial data mining coursera”
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: 641 Gregory Rice
Introduction to Data Science with R - Data Analysis Part 1
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 858824 David Langer
Intoduction to Financial Modeling | Financial Modeling Tutorial | What is Financial Modeling
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: 84088 edureka!
Вебинар «Практические задачи Data Mining: проблемы и решения»
Вебинар посвящен современным подходам и стратегиям применения методов Data Mining для решения актуальных задач в различных областях: бизнесе/маркетинге, финансах, банковской области, телекоммуникациях и др. http://www.statsoft.ru/products/STATISTICA_Data_Miner/
Views: 2012 StatSoftRussia
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
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: 1483134 ExcelIsFun
Start your future today: Build in-demand career skills on Coursera
Coursera offers over 1000 affordable online courses from the world’s best universities. Transform your career with skills in business, computer science, data science, and more; join for free to start learning today.
Views: 1208459 Coursera
Lec 1 | MIT 14.01SC Principles of Microeconomics
Lecture 1: Introduction to Microeconomics Instructor: Jon Gruber, 14.01 students View the complete course: http://ocw.mit.edu/14-01SCF10 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 896456 MIT OpenCourseWare
MOOC Climat : Un défit pour la finance
In line with the global commitments made at COP 21, keeping global warming below 2 degrees will involve a massive reorientation of investment in the coming years. Climate change already brings both risks and opportunities for finance professionals. Whether they be bankers, insurers, investors or service providers, all financial players must now take the climate into account in their businesses. This transformation of finance is already under way, but it will need to accelerate. The Institut Louis Bachelier*, a leading research network in economics and finance, has hosted several research programmes around climate finance since its creation in 2008. The Institute is now launching a MOOC (Climate: a challenge for finance) that provides fundamental insights on finance and climate. This MOOC is aimed at financial professionals and Master students who want to have an accurate and up-to-date transversal view of the climate change issue in the financial sphere. Divided into six sequences, it will address the challenges and risks related to climate change, solutions to make the various financial sectors greener, the role of public and private financing, the international financial dimension and the need for financial systems to evolve. https://www.fun-mooc.fr/courses/course-v1:psl+47003+session01/about
Views: 342 noemiedie
Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Training | Edureka
( Python Training : https://www.edureka.co/python ) This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014. This Python Pandas tutorial video helps you to learn following topics: 1. What is Data Analysis? 2. What is Pandas? 3. Pandas Operations 4. Use-case Check out our Python Training Playlist: https://goo.gl/Na1p9G Subscribe to our channel to get video updates. Hit the subscribe button above. #Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonPandas How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 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. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 126791 edureka!
Big Data Training Coursera
goo.gl/wsOrJH Big Data Training Coursera goo.gl/4vHNcm The well-known three Vs of Big Data - Volume, Variety, and Velocity – are increasingly placing pressure on organizations that need to manage this data as well as extract value from this data deluge for Predictive Analytics and Decision-Making. Big Data technologies, services, and tools such as Hadoop, MapReduce, Hive and NoSQL/NewSQL databases and Data Integration techniques, In-Memory approaches, and Cloud technologies have emerged to help meet the challenges posed by the flood of Web, Social Media, Internet of Things (IoT) and machine-to-machine (M2M) data flowing into organizations. goo.gl/UYSz3Z http://www.bigdatatraining.in/ WebSite: http://www.bigdatatraining.in Mail: [email protected] Call: +91 9789968765 044 - 42645495 Call –: +91 97899 68765 / +91 9962774619 / 044 – 42645495 Weekdays / Fast Track / Weekends / Corporate Training modes available Our Trainings Also available across India in Bangalore, Pune, Hyderabad, Mumbai, Kolkata, Ahmedabad, Delhi, Gurgon, Noida, Kochin, Tirvandram, Goa, Vizag, Mysore,Coimbatore, Madurai, Trichy, Guwahati & Chennai On-Demand Fast track Trainings globally available also at Singapore, Dubai, Malaysia, London, San Jose, Beijing, Shenzhen, Shanghai, Ho Chi Minh City, Boston, Wuhan, San Francisco, Chongqing
Views: 254 Keerthna Chennai
Univariate data analysis - 01 - Introduction
These videos are part of the FREE online book, "Process Improvement using Data", http://yint.org/pid Related is the Coursera course, "Experimentation for Improvement". Join the course for FREE at http://yint.org/experiment
Views: 2032 Kevin Dunn
Using Machine Learning and Data Science to Solve Real Business Problems (DataEDGE 2018)
Sourav Dey, Managing Director of Machine Learning, Manifold — AI and machine learning have the power to transform entire industries. Companies in consumer facing industries like banking, healthcare, and e-commerce that aren’t using these new tools and advances today run the real risk of failure as we gear towards an automated future. Sourav Dey, Managing Director and Head of Data Science at Manifold, will talk about the importance for today’s companies to adopt ML and data science and how they can do it. Using specific real world use cases, Sourav will detail his process for casting business problems as machine learning problems—from identifying which business problem to solve, to hypothesis generation, to AB testing. He’ll explain how to transform, segment, and measure variables to obtain meaningful data unique to each company, and share examples of how businesses he’s worked with have implemented these technologies to analyze specific areas of their business and resolve their toughest problems. As Managing Director of Machine Learning, Sourav is responsible for the overall delivery of data science and data product services to make clients successful. Before Manifold, Sourav led teams to build data products across the technology stack, from smart thermostats and security cams (Google / Nest) to power grid forecasting (AutoGrid) to wireless communication chips (Qualcomm). He holds patents for his work, has been published in several IEEE journals, and has won numerous awards. He earned his Ph.D., MS, and BS degrees from MIT in Electrical Engineering and Computer Science.
How to Build a Financial Model in Excel - Tutorial | Corporate Finance Institute
How to Build a Financial Model in Excel - Tutorial | Corporate Finance Institute Learn how to build a financial model in Excel with our video course (part 1). Enroll in the FULL course to earn your certificate and advance your degree: http://www.corporatefinanceinstitute.com In this course you will learn to build a financial model from scratch by working in Excel and following along with the video. Upon successfully completing the course and all quizzes you will obtain your Financial Modeling Certificate from the Corporate Finance Institute. -- 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
Business Data Analysis with Excel
Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required! Dave will cover the following during the presentation: • The types of business data and why business data is a unique analytical challenge. • Requirements for robust business data analysis. • Using histograms, running records, and process behavior charts to analyze business data. • The rules of trend analysis. • How to properly compare business data across time, organizations, geographies, etc.Where you can learn more about the tools and techniques. *Excel spreadsheets can be found here: https://github.com/datasciencedojo/meetup/tree/master/business_data_analysis_with_excel **Find out more about David here: https://www.meetup.com/data-science-dojo/events/236198327/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8xWx0 See what our past attendees are saying here: https://hubs.ly/H0f8xGd0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 43436 Data Science Dojo
The Data Science Revolution (Jeremy Howard) - Exponential Finance 2014
Data Science (Big Data, Data Analysis, Machine Learning) is said to represent a larger potential disruption than the industrial revolution. This session will address the impact on the financial world by data driven decision-making, predictive modeling, machine learning, intelligent computing and more.
The Power of Data & Algorithms
Join the discussion on all things big data--from predictive analysis and social media data mining, to how data can predict fluctuations with the stock market.  Panelists:    •Industry Expert: Dr. Jingsong Cui, VP of Data Science at Nielson    •Industry Expert: Joseph F. Miceli Jr., CEO of Omniscient Analytics    •Florida Poly Math and Analytics Professor: Dr. Athanasios Gentimis    •Moderator: Crystal Lauderdale, Director of Marketing & Communications
Process Mining with ProM - free online course at FutureLearn.com
Sign up now at http://bit.ly/2rUYp0u 'Process Mining with ProM' is a free online course by Eindhoven University of Technology on FutureLearn.com Process mining is a relatively new and exciting field which combines business process management with data science. Using process mining techniques you can analyse and visualise business processes based on event data recorded in event logs. Process mining provides a critical, process-centric perspective on data, which is not available with classical data mining or machine learning techniques. #FLProM At FutureLearn, we want to inspire learning for life. We offer a diverse selection of free, high quality online courses from some of the world's leading universities and other outstanding cultural institutions. Browse all courses and sign up here: http://www.futurelearn.com
Views: 3715 FutureLearn
Coursera Statistical Inference Week 1 Quiz - Random Variables
www.Stats-Lab.com | Coursera Data Science Specialization | Statistical Inference Advised to use Full Screen Mode
Views: 1565 Dragonfly Statistics
Course Spotlight: Data Science Specialization
Professor Roger Peng describes the sequence of topics in the Data Specialization from Johns Hopkins University
Views: 5319 Coursera
Coursera Review
How to teach yourself programming.
Views: 666 Money Jones
Using Data Mining and Visualization to Investigate Retention in STEM
This STEM Central webinar was presented on March 25th 2015 by a transdisciplinary team at Northern Kentucky University: Maureen Doyle, Associate Dean of Informatics; Kristi Haik, Chair of the Department of Biological Sciences; Madhura Kulkarni, Assistant Director at Center for Integrative Natural Science and Mathematics; Bethany Bowling, Associate Professor of Biological Sciences; Mark Lancaster, Assistant Professor of Statistics and Data Science; and their students Kacie Kotnik and Nathaniel Hudson. Abstract: At Northern Kentucky University (NKU) we are implementing two parallel NSF-funded programs to improve student persistence and provide impactful career exploration. Project FORCE (Focus on Occupations, Recruiting, Community, and Engagement) builds STEM-wide efforts that include peer-led study sessions, research opportunities for students at risk of leaving, and activities that promote community. Project SOAR (Scholarships, Opportunities, Achievements, and Results) recruits 22 academically-talented students with financial need and provides them with scholarships, targeted academic interventions, intentional mentorship, and career networking. The leadership teams of these projects use data-mining and visualization techniques to assess STEM student data provided by NKU Institutional Research. In this webinar we will discuss launching this type of investigation with the necessary entities at your institution (i.e. IR, IRB), approaches to analyzing and mining the data, and selecting visualization tools. The focus will be on how to implement this approach at your institution with examples from our analyses.
Digital Symplexity - What Is Process Mining & P2P Celonis Process Mining Webinar
Digital Symplexity walks through WHAT IS PROCESS MINING & HOW DOES IT WORK using Celonis Process Mining- webinar. Shows 100% process transparency to the Procure to Pay Process
Views: 3260 Digital Symplexity
11. Introduction to Machine Learning
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 380043 MIT OpenCourseWare
Learn Python - Full Course for Beginners
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗The Mike's website: https://www.mikedane.com/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 2142200 freeCodeCamp.org
8. Time Series Analysis I
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the first of three lectures introducing the topic of time series analysis, describing stochastic processes by applying regression and stationarity models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 161742 MIT OpenCourseWare
Module 1: Data Analysis in Excel
This video is part of the Analyzing and Visualizing Data with Excel course available on EdX. To sign up for the course, visit: http://aka.ms/edxexcelbi
Views: 387074 DAT206x
Data Science Training in INDIA USA CANADA UK UAE - Demo Video 1
Data Science Training in INDIA USA CANADA UK UAE Demo Video Data Science Training in Bangalore Data Science Training in Mumbai Data Science Training in Pune Data Science Training in Chennai Data Science Training in Delhi Data Science Training in Hyderabad Data Science Training Chicago Data Science Training DALLAS Data Science Training SAN FRANCISCO Data Science Training in London Data Science Training in Dubai Data Science Training in toronto
DATA MINING   3 Text Mining and Analytics   3 9 Latent Dirichlet Allocation LDA Part 1
Views: 128 Ryo Eng
International Journal of Data Mining & Knowledge Management Process
International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 147 aircc journal
pinot coursera
coursera, coursera review, coursera python, coursera courses, coursera financial aid, coursera algorithms, coursera matlab, coursera google, coursera nlp, coursera certificate, coursera r programming week 3 assignment, coursera app, coursera ai, coursera ad, coursera android, coursera arduino, coursera answers, coursera accounting, coursera angular, coursera advanced machine learning, coursera big data, coursera bioinformatics, coursera business, coursera business analytics, coursera blockchain, coursera bitcoin, coursera business model, coursera basic statistics, coursera big data analytics, coursera brian bushee, coursera cryptography, coursera ceo, coursera certificate value, coursera cnn, coursera courses videos, coursera chinese, coursera capstone project, coursera calculus, coursera degree, coursera deep learning, coursera deep learning review, coursera demo, coursera dl, coursera data science, coursera data mining, coursera downloader, coursera digital marketing, coursera data structures, coursera english, coursera edx, coursera excel, coursera enroll, coursera economics, coursera exam, coursera español, coursera entrepreneurship, coursera experience, coursera example, coursera free, coursera for business, coursera finance, coursera free courses with certificates, coursera financial aid 2018, coursera financial accounting, coursera free courses online, coursera financial markets, coursera founder, coursera graphic design, coursera gis, coursera game theory, coursera google cloud, coursera guitar, coursera getting and cleaning data project, coursera google course, coursera game development, coursera google it certification, coursera hadoop, coursera html, coursera hinton, coursera hack, coursera hindi, coursera how to learn, coursera html css and javascript for web developers, coursera history of rock, coursera how to use, coursera how to submit assignments, coursera imba, coursera it, coursera introduction, coursera interaction design, coursera iot, coursera intro, coursera india, coursera id verification, coursera iot course, coursera it support, coursera java, coursera javascript, coursera java programming and software engineering fundamentals specialization, coursera jupyter notebook, coursera jira, coursera john hopkins, coursera jazz improvisation with lstm, coursera jobs, coursera alfredo jimenez, jesi coursera, coursera korean, coursera ketone, coursera kafka, coursera kibana, coursera kaggle, daphne koller coursera, know thyself coursera, philip klein coursera, coursera nasıl kullanılır, coursera lectures, coursera login, coursera lectures java, coursera learning how to learn, coursera linear regression, coursera learning english, coursera logic, coursera lectures data science, coursera linkedin certificate, coursera live chat, coursera machine learning, coursera music, coursera ml, coursera machine learning assignment, coursera mba, coursera mechanical engineering, coursera marketing, coursera masters, coursera music production, coursera networking, coursera natural language processing, coursera neural networks, coursera neural networks and deep learning, coursera neural networks for machine learning, coursera nlp course, coursera negotiation michigan, coursera notebook, coursera nlp jurafsky, coursera office, coursera or edx, coursera online degree, coursera online courses, coursera operating system, coursera octave, coursera online chat, coursera offline, coursera office phone number, coursera or edx courses, como funciona o coursera, coursera psychology, coursera premium account, coursera php, coursera python course, coursera physics, coursera payment, coursera percolation, coursera philosophy, coursera python for everybody, coursera quiz answers, coursera quantum computing, coursera qlikview, coursera quantum physics, que es coursera, coursera r programming assignment 1 part 3, coursera r programming week 3 assignment solution, coursera robotics, coursera registration, coursera r programming week 3, coursera regression, coursera r programming week 2 assignment part 3, coursera review 2017, r coursera assignment 1, statistics with r coursera, r tutorial coursera, coursera scala, coursera svm, coursera seo, coursera sql, coursera spark, coursera software, coursera spanish, coursera sign up, coursera software security project 1, coursera statistics, coursera tesol, coursera tensorflow, coursera tutorial, coursera thermodynamics, coursera tableau, coursera ted talk, coursera token, coursera tesol review, coursera type websites, coursera take course for free, coursera unity,
Views: 32 Civil Video Share
Python for Machine Learning and Data Mining Udemy Course
https://www.udemy.com/python-for-machine-learning-and-data-mining Udemy Course coupon discount: PYTHONML0817
DATA MINING   3 Text Mining and Analytics   3 10 Latent Dirichlet Allocation LDA Part 2
Views: 231 Ryo Eng
Lecture 1 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 2059826 Stanford
19. Investment Banks
Financial Markets (2011) (ECON 252) Professor Shiller characterizes investment banking by contrasting it to consulting, commercial banking, and securities trading. Then, in order to see the essence of investment banking, he reviews some of the principles that John Whitehead, the former chairman of Goldman Sachs, has formulated. These principles are the basis for a discussion of the substantial power that investment bankers have, and their role in society. Government regulation of these powerful investment banks has been a thorny issue for many years, and especially so now since they played a significant role in world financial crisis of the 2000s. 00:00 - Chapter 1. Key Elements of Investment Banking 09:50 - Chapter 2. Principles and Culture of Investment Banking 16:54 - Chapter 3. Regulation of Investment Banking 27:21 - Chapter 4. Shadow Banking and the Repo Market 33:04 - Chapter 5. Founger: From ECON 252 to Wall Street 46:24 - Chapter 6. Fougner: Steps to Take Today to Work on Wall Street 53:49 - Chapter 7. Fougner: From Wall Street to Silicon Valley, Experiences at Facebook 57:56 - Chapter 8. Fougner: Question and Answer Session Complete course materials are available at the Yale Online website: online.yale.edu This course was recorded in Spring 2011.
Views: 300796 YaleCourses
Lecture 1 — Intro to Crypto and Cryptocurrencies
First lecture of the Bitcoin and cryptocurrency technologies online course. For the accompanying textbook, including the free draft version, see: http://bitcoinbook.cs.princeton.edu/ In this lecture (click the time to jump to the section): * Cryptographic hash functions 1:51 * Hash pointers and data structures 20:28 * Digital signatures 29:25 * Public keys as identities 39:04 * A simple cryptocurrency 44:39
Introduction MOOC Blended Learning: Data Analytics for Lean Six Sigma
This is an introduction to the Massive Open Online Course (MOOC) of Data Analytics for Lean Six Sigma. For more information on Lean Six Sigma at the Amsterdam Business School, please visit http://www.ibisuva.nl the blended learning video can be found at https://www.coursera.org/
Data Science: Kaggle GRANDMASTER за полгода? | Павел Плесков, Data Nerds
Подписывайтесь! https://on.fless.pro/subscribe Начать заниматься data science в сентябре 2017, а к февралю 2018 стать экспертом науки о данных и получать офферы на Chief Data Scientist. Думал, такое не возможно, пока не познакомился с Пашей Плесковым из Data Nerds. Внимание: Видео может вызвать FOMO и отвесить пинка под зад. Скоро снова сентябрь =) Хотите повторить? Смотрите, как. Тайм-теги: 00:27 #1 Background 02:35 #2 Data science: с чего все началось? 04:45 #3 KAGGLE GRANDMASTER за 6 месяцев... как? 07:39 #4 Data science: много ли математики нужно на практике? 10:30 #5 Data science: немного о рабочем железе 13:47 #6 Соревнования. Командный опыт 17:42 #7 KAGGLE GRANDMASTER и работа 21:29 #8 Соревнования vs работа 23:19 #9 Чем занимаешься сейчас? 26:43 #10 В чем отличие DATA NERDS и DS в BIG3 28:49 #11 Почему бы не найти работу в США? 30:50 #12 Про планы? 32:31 #13 Пара советов? Другие недавние интервью: - КАК ПОПАСТЬ В БОЛЬШУЮ ТРОЙКУ В 35 ЛЕТ | БОРИС КУЛАХМЕТОВ - https://youtu.be/yT0KtsCjvRw - КАРЬЕРА НА СТЫКЕ DIGITAL И STRATEGY CONSULTING | АНАСТАСИЯ КИМ, IBM iX - https://youtu.be/7kwd_0qYXY4 БУДЕМ НА СВЯЗИ! FLESS https://fless.pro Instagram https://www.instagram.com/flesspro Facebook https://www.facebook.com/flesspro VK https://vk.com/flesspro Telegram https://t.me/flesspro
Views: 12221 Fless
Leverage Clinical and Financial Data to Improve Decision-Making and Reduce Healthcare Costs
The Perficient High-Performance Costing Expressway provides a complete solution that enables hospitals to rapidly deploy a micro-level costing solution. It provides integrated software and hardware with a prescribed set of data integrations and services to quickly deploy a costing application. Learn more: http://www.perficient.com/Products/Industry-Tools/Healthcare/High-Performance-Costing-Expressway Healthcare costs are rising at a faster pace than the economy is growing. Hospitals are often the focus of this concern, because they constitute the largest single component of healthcare spending. When looking at hospital costs, it is important to keep in mind that there are both direct and indirect expenses that contribute to the total cost of care. How do we understand total cost of care? Our team of experts leverages a proprietary technology for our clients that enables transparency of fully burdened margin by service, patient and procedure. For decades, spreadsheets and costing software have been the best alternatives in determining cost of care. It is now more important than ever to transform these methods and leverage administrative, clinical and financial data in order to gain control of healthcare costs. Creating transparent costing models to indicate profitability across multiple dimensions of data is the key to driving healthcare costs down. Embracing data-driven decision making in a provider setting requires agile thinking to pinpoint and respond to the short- and long-term needs of the organization. This shift requires finance departments to transcend from the typical focus on aggregating data to a value-added analytical view of hospital data. This new approach will provide greater visibility into changes in variables and assumptions and will require organizations to fully understand and ensure transparency exists for key performance indicators.
Views: 1567 Perficient, Inc.
Coursera CEO, Rick Levin: Leaders Must Communicate Their Vision
During his October 16th Stanford GSB View From The Top talk, Coursera CEO Rick Levin shared key insights for leaders: “Have a vision. Communicate it clearly. Pick the right people and put them in the right jobs.” Read about his take on online education: http://www.gsb.stanford.edu/insights/coursera-ceo-richard-levin-democratizing-learning-takes-time Levin also discussed his experience leading the economic growth of Yale's resources and his transition from Yale into Coursera.
Machine Learning and Baseball: A MIDS Capstone Project | Data Dialogs 2015
Zach Beaver, Jason Goodman, Josh Lu, & Alan Si http://datadialogs.ischool.berkeley.edu/2015/schedule/machine-learning-and-baseball-mids-capstone-project A good baseball hitter watches game film to pick up on the opposing pitcher's tendencies. The batter uses that information to predict what the pitcher will throw next. But few batters can do this as well as a computer that knows everything about every pitch the pitcher has ever thrown, and uses machine learning to figure out what he's likely to do next. In our MIDS capstone project, we attempted to build just such a system. In this talk, we'll present our project and the lessons we learned about data science in the process. ---------------------------------------- Zach Beaver Data Scientist Elder Research, Inc. Zach Beaver is a data scientist at Elder Research, Inc. in Washington DC, where he assists government agencies in leveraging their data to support better decision-making. His recent work includes fraud detection, developing methods to assess the credibility of home appraisals, and entity resolution. He is a recent graduate of UC Berkeley's Master of Information and Data Science program and holds degrees in Biology and Computer Science from Wofford College. ---------------------------------------- Jason Goodman Jason Goodman is a recent graduate of the UC Berkeley Master of Information and Data Science (MIDS) program. Prior to MIDS, he worked as a management consultant at Oliver Wyman, solving strategy problems for clients in the retail, pharmaceutical and financial services sectors. He holds a B.A. degree in Economics from Dartmouth College. ---------------------------------------- Josh Lu Tax Manager Ernst and Young, Quantitative Services group Josh Lu is a recent graduate of the UC Berkeley Master of Information and Data Science program. He is a CPA and works as Tax Manager in Ernst and Young's Quantitative Services group, specializing in research and development tax credits. He holds a Bachelor's degree in Accounting from Santa Clara University. ---------------------------------------- Alan Si Alan is a recent graduate of the UC Berkeley Master of Information and Data Science program. He previously worked for Upworthy as a Machine Learning Fellow and is currently a Data Scientist at Castlight Health where he focuses on improving User engagement and patient outcomes. He is originally from Canada and holds Bachelor degrees in Mechanical Engineering as well as Business Administration.
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 205552 ACADGILD
CS@ILLINOIS' Online Master's of Computer Science - Data Science [Admissions Webinar]
Learn how to get your MCS-DS online with the University of Illinois and Coursera: https://www.coursera.org/degrees/mast... Get more information on the admissions requirements and process, curriculum and more from Computer Science Professor and MCS-DS Program Director John Hart; Graduate Program Specialist and Academic Advisor Christine Martinez; and Graduate Programs Coordinator Viveka Kudaligama at the University of Illinois Urbana Champaign. Webinar originally aired January 18, 2018.
Views: 1741 Coursera
Corporate Finance Professional Certificate Program | ColumbiaX on edX
Take this program on edx.org: https://www.edx.org/professional-certificate/columbiax-corporate-finance Learn both the sound theoretical principles of finance and the practical environment in which financial decisions are made. You will learn how to value a firm over the course of this Corporate Finance professional certificate program, giving you the skills necessary to make sound financial and investment decisions. We will cover: Framework for valuation (including, as special cases, valuation of stocks and bonds, and evaluation of investment opportunities) Free cash flow method for firm valuation Concepts of risk and return and identification of opportunity cost of capital Alternative sources of external funding for company operations Together, these concepts make up the essential building blocks for a career in finance. In this hands-on program, you will be given the opportunity to learn through a variety of real-world transactions and case studies as well as work through exercises in constructing Excel models to help deepen your understanding of concepts. This program is for those looking to advance their career in a range of professions, including investment banking, private equity, consulting, general management, and CFO track jobs within a corporation. This program is based on the first-year course taught in Columbia Business School’s MBA core program.
Views: 4130 edX
Kenneth Cukier: Big data is better data
Self-driving cars were just the start. What's the future of big data-driven technology and design? In a thrilling science talk, Kenneth Cukier looks at what's next for machine learning — and human knowledge. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
Views: 302412 TED
MOOC on Public Finance and Policy in India - Introduction
MOOC on Public Finance and Policy in India - Introduction
Views: 100 MOOCs UGC
Microsoft Excel 2016 - Learn Excel 2016 Beginners Tutorial Video
Learn Microsoft 2016 from the pro's. In this FREE beginners tutorial for Microsoft 2016 you will learn the basics of Excel 2016, and Tips and Tricks so that you can get the most out of your Excel experience. Our beginner videos start with simple tasks like selecting cells and entering data, then quickly move to calculating data with basic formulas and functions. The intermediate portion shows you how simple it is to sort and filter and then moves to one of Excel’s most popular features, Pivot Tables. Our advanced videos start with functions like IF and VLOOKUP and finish up with Macros. Some of the other topics in the Microsoft Excel 2016 tutorial include: Start Screen & Templates, Ribbon & QA Toolbar - Part 1, Ribbon & QA Toolbar - Part 2, Backstage View, Interface, Share Pane, Tell Me, Smart Lookup, Navigating, Keyboard Shortcuts, Entering Text Formatting Numbers, Deleting & Formatting, AutoSum, Simple Formulas, Simple Functions, Absolute Referencing, Inserting Rows Inserting Columns, Moving & Copying Data, Autofill, Cell Styles, Worksheet Themes, Excel Templates, Grouping Worksheets, Freeze Panes, Custom Views, Spell Check, Printing, Flash Fill, List Design & Single Level Sorting, Multi-Level Sorting, Custom Sorting, Filtering, Multi-Level Filtering, Search Filtering, Format as Table, Table Style Options, Remove Duplicates, Convert to Range, Subtotal, Multi-Level, Subtotaling, Remove Subtotals, Quick Analysis Charts, Inserting Data Charts, Formatting Data Charts, Chart, Templates, Spark lines, Printing Charts, Importing From Web, MS Query, Exporting Overview, Pivot Tables, Multiple-Field Pivot Tables, Drill Down Reports, Pivot Charts & Grouping Fields, Slicer Tool, Data Validation - Part 1, Data Validation - Part 2, Cell & Sheet Protection, File Encryption, Conditional Formatting, Linking Data, Inserting Comments.. and more Please subscribe to Learn iT! and check out our website (http://learnit.com) to browse other Microsoft Courses, Project Management, and IT Training courses.
Views: 652232 Learn iT! Training
MOOC Financial Service
Please watch this video
Views: 6 Santhiyavalli G
Statistical Programming DC - March 2016 - Machine Learning with R
About the Talk Machine learning has become increasingly associated with advanced analytics. We will explore the basic idea behind machine learning, the steps to apply machine learning, and some of the supervised and unsupervised models. We will focus on classification models and will walk through the R code for those models. We will go over a real life business application at a major institution and touch on key practical lessons learned for machine learning. The talk will address know how from a practitioner’s point of view.   There will be code! About Rafi Rafi is an advance analytics manager with the Advisory Services data and analytics practice at Ernst & Young. Rafi is a practitioner with a background in economic research, finance and data analytics from algorithms to BI to data mining. He has consulted on new product developments, business initiatives, process reengineering, technology solutions and efficacy implementations. He has developed R machine learning models for large financial institutions and oversees the analytics training for Data and Analytics. Rafi holds a Master's of Science of Finance from Johns Hopkins University and B.S. in Economics from George Washington University. He serves on the financial advisory committee to the board of Washington Yu-Ying Public charter school and on the corporate advisory board for the master in analytics at GWU. Follow him at @refaellav
Views: 237 Casey Driscoll
Lesson 3 10 The Option to Wait   Gold Mine Example
4 Financial Evaluation and Strategy - Corporate Finance
Views: 352 Ryo Eng

Broadcast meteorology cover letter
Good sample of cover letter
Firemen newsletter formats
Chcbp application letters
Iti mallepally hyderabad admissions essay