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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: 1401 Gregory Rice
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
8. Time Series Analysis I
 
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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: 183409 MIT OpenCourseWare
How to Build a Financial Model in Excel - Tutorial | Corporate Finance Institute
 
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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
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: 1584152 ExcelIsFun
Best Course In Data Mining
 
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Description
Views: 39 Mira G
Coursera - IoT specialization - arduino interfacing -1
 
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Homework 1 - two buttons and one led. When both buttons are pressed, the led turns on.
Views: 36 Thiago Borba
Alpha Algorithm (Process Discovery Method)
 
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Explanation of the Alpha Algorithm with an Example
Views: 703 Study Conquest
Mining Indeed's Data | Recruitment Research
 
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This week, Johnny takes us through the best methods for mining Indeed.com's data. Want to learn more sourcing and recruitment strategies? Visit: https://www.socialtalent.com Subscribe to make sure you don't miss a video! Facebook: https://www.facebook.com/socialtalent/ Twitter: https://twitter.com/SocialTalent LinkedIn: https://www.linkedin.com/company/social-talent/
Views: 824 SocialTalent
Optimize Insurance Prices with Data Mining Part 1
 
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Using Customer Demand Elasticity to Optimize Insurance Prices | Taylor Fry Consulting Actuaries and Salford Systems, 2009.
Views: 540 Salford Systems
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: 89851 edureka!
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: 99675 edureka!
The Data Science Revolution (Jeremy Howard) - Exponential Finance 2014
 
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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.
Data Science/Machine Learning Project Life cycle
 
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------------------------------------------------------- *About us* Applied AI course (AAIC Technologies Pvt. Ltd.) is an Ed-Tech company based out in Hyderabad offering on-line training in Machine Learning and Artificial intelligence. Applied AI course through its unparalleled curriculum aims to bridge the gap between industry requirements and skill set of aspiring candidates by churning out highly skilled machine learning professionals who are well prepared to tackle real world business problems. *Key highlights of Applied AI course* 1. Job guarantee or money back guarantee 2. Query resolution inside 24 hours 3. Personalized learning path for every course participant 4. 30 Practical Assignments 5. 15 end-to-end case studies based on real world problems across various industries 6. Mentor-ship for portfolio development, resume and interview preparation, and career counseling for every course participant For More information Please visit https://www.appliedaicourse.com/ For any queries you can either drop a mail to [email protected] or call us at +91 8106-920-029 or +91 6301-939-583 Facebook: https://www.facebook.com/appliedaicourse/ Soundcloud: https://soundcloud.com/applied-ai-course Twitter: https://twitter.com/appliedaicourse #AppliedAICourse#Datascience#MachineLearning#ProjectLifecycle
Views: 7963 Applied AI Course
Analyzing And Visualizing Data With Excel 2016
 
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In this workshop, get an introduction to the latest analysis and visualization capabilities in Excel 2016. See how to import data from different sources, create mash/ups between data sources, and prepare the data for analysis. After preparing the data, learn about how business calculations - from simple to more advanced - can be expressed using DAX, how the result can be visualized and shared.
Views: 34160 Microsoft Power BI
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: 2504 STUDENT WINDOW
Stanford Seminar - Topological Data Analysis: How Ayasdi used TDA to Solve Complex Problems
 
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"Topological Data Analysis: How Ayasdi used TDA to Solve Complex Problems" -Anthony Bak, Ayasdi Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week. Learn more: http://bit.ly/WinYX5
Views: 14291 stanfordonline
Hidden Markov Models
 
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This video is part of the Udacity course "Introduction to Computer Vision". Watch the full course at https://www.udacity.com/course/ud810
Views: 100018 Udacity
Leverage Clinical and Financial Data to Improve Decision-Making and Reduce Healthcare Costs
 
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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: 1598 Perficient, Inc.
"Data Science" :Demo For Beginners[2018] | Business Analytics Training - ExcelR
 
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#DataScience #2018 #ExcelRSolutions #Tabelau Get introduced to the learning objectives of Data science the most comprehensive course delivered from ExcelR solution. Topics line deep learning, Text Mining & Natural Language Processing and Times series/Forecasting. Things you will learn in this video 1. Basics Statistics 2. Hypothesis Testing - What & How 3. Regression Analysis 4. Data Mining / Machine Learning 5. Test Mining & Natural Language Processing 6. Forecasting 7. Data Visualization 8. Tableau 9. R & R Studio 10. XLMiner 11. Mini-tab 12. Python SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/excelr-solutions/ Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
pinot coursera
 
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Cernido: Data Mining & Business Intelligence
 
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Our site: http://www.cernido.com/ Cernido provides data mining and big data analysis services. Main focus is collaboration with business consulting companies to provide their customers competitive advantage by analyzing information from different sources.
Views: 379 Cernido Yinius
Learn Python - Full Course for Beginners
 
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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 🔗If you liked this video, Mike accepts donations on his website: https://www.mikedane.com/contribute/ ⭐️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: 6287062 freeCodeCamp.org
Lec 1 | MIT 14.01SC Principles of Microeconomics
 
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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: 1046678 MIT OpenCourseWare
R tutorial: Introduction to cleaning data with R
 
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Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data. You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere. Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned. Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data. With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer. In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw. Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help. In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis. Let's jump right in!
Views: 36391 DataCamp
Introduction to Databases - Databases and SQL for Data Science by IBM #2
 
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This video is part of an online course, Databases and SQL for Data Science. This course introduces relational database concepts and helps you learn and apply foundational knowledge of the SQL language. Enroll today at https://www.coursera.org/learn/sql-data-science?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm to get access to the full course. About this course: Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Visit https://www.coursera.org/learn/sql-data-science?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm to learn more! Professional Certificate: https://www.coursera.org/specializations/ibm-data-science-professional-certificate?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm Keep in touch with Coursera! Twitter: https://twitter.com/coursera Facebook: https://www.facebook.com/Coursera/
Views: 188 Coursera
Using Machine Learning for Predicting NFL Games | Data Dialogs 2016
 
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You are a HUGE football fan. Every week you pick winners in an NFL pick-em' league. Somehow, all that fan experience doesn't translate into consistently winning your league. Perhaps you need a more systematic approach that takes some of the emotion out of it. Where to start? Betting spreads provide a consistent and robust mechanism for encapsulating the variables and predicting outcomes of NFL games. In a weekly confidence pool, spreads also perform very well as opposed to intuition-based guessing and "knowledge" from years of being a fan. Can we do better? In this talk, we will discuss an approach to use machine learning algorithms to make improvements on the spread method of ranking winners on a weekly basis as an exercise in winning your friendly neighborhood confidence pool. https://datadialogs.ischool.berkeley.edu/2016/schedule/using-machine-learning-predicting-nfl-games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Bhattacharyya Senior Data Scientist Teachers Pay Teachers Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources. At TpT, Amit works on developing both technical and modeling infrastructure to analyze customer behavior and ways to more effectively connect buyers and sellers. Amit also teaches in the MIDS program at the UC Berkeley School of Information. He received a Ph.D. in physics from Indiana Universtiy. Previously, he did a two-year stint in advertising, and worked as a quantitative analyst at various banks and hedge funds for twelve years. In his spare time, he likes to plan skiing and backpacking trips, and dabble with machine learning algorithms for fantasy football.
Applied Data Science with Python
 
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The video is an introduction for a specialization from University of Michigan on https://studyscienceblog.wordpress.com/2017/11/21/applied-data-science-with-python-specialization
Views: 3345 Educational courses
Information and Data Models - Databases and SQL for Data Science by IBM #8
 
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This video is part of an online course, Databases and SQL for Data Science. This course introduces relational database concepts and helps you learn and apply foundational knowledge of the SQL language. Enroll today at https://www.coursera.org/learn/sql-data-science?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm to get access to the full course. About this course: Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Visit https://www.coursera.org/learn/sql-data-science?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm to learn more! Professional Certificate: https://www.coursera.org/specializations/ibm-data-science-professional-certificate?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm Keep in touch with Coursera! Twitter: https://twitter.com/coursera Facebook: https://www.facebook.com/Coursera/
Views: 58 Coursera
Time Series Forecasting Theory | AR, MA, ARMA, ARIMA | Data Science
 
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In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models. For training, consulting or help Contact : [email protected] For Study Packs : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 405038 Analytics University
INSERT Statement - Databases and SQL for Data Science by IBM #6
 
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This video is part of an online course, Databases and SQL for Data Science. This course introduces relational database concepts and helps you learn and apply foundational knowledge of the SQL language. Enroll today at https://www.coursera.org/learn/sql-data-science?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm to get access to the full course. About this course: Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Visit https://www.coursera.org/learn/sql-data-science?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm to learn more! Professional Certificate: https://www.coursera.org/specializations/ibm-data-science-professional-certificate?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=ibm Keep in touch with Coursera! Twitter: https://twitter.com/coursera Facebook: https://www.facebook.com/Coursera/
Views: 145 Coursera
11. Introduction to Machine Learning
 
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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: 538556 MIT OpenCourseWare
DATA MINING   3 Text Mining and Analytics   3 9 Latent Dirichlet Allocation LDA Part 1
 
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https://www.coursera.org/learn/text-mining
Views: 151 Ryo Eng
How to start a Business Analytics Career in India ? - Skills required, Pay scale, Job opportunities
 
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Get the latest interview tips,Job notifications,top MNC openings,placement papers and many more only at Freshersworld.com(www.freshersworld.com?src=Youtube). The major role of a BA is – data mining, statistical analysis, predictive modelling and multivariate test. Business Analytics career in India has emerged as the preferred role of choice in IT and ITES industries. The Business Analyst is also required to support decision making roles with real-time analysis. Business Analysts also work closely with the senior management and provide support in data-driven decision making that impacts matters related to product development to marketing. There is a continued strong demand for business analysts especially in India where candidates can land opportunities in IT and ITES industries. According to one article, India has at least 1.2 million business analysts and by 2020 India is pegged to have the highest number of business analysts. Some of the major sectors where Business Analysts have a promising start are retail, banking, healthcare, ecommerce, hospitality, manufacturing etc. Kick-start a Business Analytics career in India One of the most promising career paths in IT today, the job description for business analysts sometimes converge analytics and project management roles. If you are interested in a Business Analyst role, a background in mathematics and engineering is a must, backed by good analytical and communication skills. However, there are those who believe candidates with a business background can also make a career in BA. By upskilling themselves with short online courses, these candidates can also get a good grip on business analytics and start a career. Skills required by Business Analytics candidate: A Business Analyst should be proficient in applied statistics, have knowledge of statistical suite such as SAS, R, SPSS, should know SQL, Hive, knowledge of testibg framework and a working knowledge of BI tools such as Qlik, Tableau, Spot fire among others. Skills may vary depending on the organization’s requirement. However, this is a basic knowledge framework required for making the cut. How can candidates with a business background start a Business Analytics career? While most engineers gravitate towards the data engineering and information management field, candidates with a business background can easily transition into the Business Analyst role. MBA holders can sharpen their skills by a) enrolling in analytics courses, b) participating in mentoring sessions and boot camps to lands their dream job. And though companies don’t expect deep knowledge of tools, a basic understanding can help in landing the right job. Job opportunities for Business Analysts: From leading financial institutions, to consultancies such as Deloitte, E&Y, and global retailers Target, Walmart and online leader Amazon require Business Analytics professionals. Top Employers: Some of the top Business Analytics companies to work for are: • Tata Consultancy Services • Cognizant • Accenture • GENPACT • Wipro • Infosys • IBM • Deloitte • HPE Some of the startups that provide an excellent opportunity to pursue Business Analytics career are Fractal Analytics, Mu Sigma Analytics and Absolut Data. Pay scale: One of the most sought after jobs, business analysts have a rare blend of business and analytical skills and are rewarded with good pay packages. The average salary of senior business analyst is INR 8,59,025 per year. The average salary of BA is INR 6,44,857 per year. Download our app today to manage recruitment whenever and where ever you want : Link :https://play.google.com/store/apps/details?id=com.freshersworld.jobs&hl=en ***Disclaimer: This is just a training video for candidates and recruiters. The name, logo and properties mentioned in the video are proprietary property of the respective organizations. The Preparation tips and tricks are an indicative generalized information. In no way Freshersworld.com, indulges into direct or indirect promotion of the respective Groups or organizations.
Data Science Lifecycle
 
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In this video I talk about the lifecycle of the data science process. It all starts with a hypothesis...
Views: 6432 Mike Bernico
New Python Tutorial: Diagnose data for cleaning
 
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First video of our latest course by Daniel Chen: Cleaning Data in Python. Like and comment if you enjoyed the video! A vital component of data science involves acquiring raw data and getting it into a form ready for analysis. In fact, it is commonly said that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose your data for problems to dealing with missing values and outliers. At the end of the course, you'll apply all of the techniques you've learned to a case study in which you'll clean a real-world Gapminder dataset! So you've just got a brand new dataset and are itching to start exploring it. But where do you begin, and how can you be sure your dataset is clean? This chapter will introduce you to the world of data cleaning in Python! You'll learn how to explore your data with an eye for diagnosing issues such as outliers, missing values, and duplicate rows. Try the first chapter for free: https://www.datacamp.com/courses/cleaning-data-in-python
Views: 16056 DataCamp
Lecture 1 | Machine Learning (Stanford)
 
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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: 2181164 Stanford
Data Science With R | Introduction to Data Science with R | Data Science For Beginners | Simplilearn
 
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This Data Science with R tutorial will help you understand what is R, why R, what is comprehensive R archive network, how to install R, what is linear regression, what is correlation analysis in R and at the end you will also see a use case implementation using R where we predict the class of a flower. Today, it is imperative for every modern business to understand the huge amounts of data it maintains on its customers and itself. R programming language makes it easy for a business to go through the business’s entire data. Now, lets deep dive into this video to understand Data Science using R programming. Below topics are explained in this Data Science with R tutorial: 1. Introduction to R ( 00:38 ) - Why R? - Comprehensive R archive network - Installing R 2. Simple linear regression using R ( 12:20 ) - The line of best fit - Correlation analysis in R 3. Classification using R ( 38:24 ) - Use case: Predict the class of a flower To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/WGtBKQ Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analysis techniques that can be performed using R. The data science course is packed with real-life projects and case studies and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Science-With-R-0vCK17cQt14&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 8355 Simplilearn
Uncharted Lecture Series: "A Framework for Data Mining in Wind Power Time Series"
 
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On Thursday, March 19, 2015, Oliver Kramer, a juniorprofessor for computational intelligence at the University of Oldenburg in Germany and an ICSI alumnus, gave a talk about his work on data mining and green energy. Dr. Kramer's full abstract and bio are available at https://www.icsi.berkeley.edu/icsi/events/2015/03/kramer-data-mining-framework Abstract: Wind energy is playing an increasingly important part for ecologically friendly power supply. The fast growing infrastructure of wind turbines can be seen as a large sensor system that screens the wind energy at a high temporal and spatial resolution. The resulting databases consist of huge amounts of wind energy time series data that can be used for prediction, controlling, and planning purposes. In this talk, I describe WindML, a Python-based framework for wind energy related machine learning approaches. Read the full abstract at https://www.icsi.berkeley.edu/icsi/events/2015/03/kramer-data-mining-framework
Views: 639 ICSIatBerkeley
Process Mining with ProM - free online course at FutureLearn.com
 
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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: 4890 FutureLearn
How to apply for #financialaid on coursera with guaranteed 100% #approvalwithin15 days.
 
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Many of you face difficulty in applying in process of applying financial aid on coursera , i had recorded the whole process for applying a financial aid so that you don't have to search all the web and read the lengthy blogs etc , there are still many who are not aware of the process like you , help them by sharing this video and showing your support to our channel https://www.youtube.com/channel/UCCkM_hNH5-PR_24wrWjlyPw?view_as=subscriber support us at linked in: https://www.linkedin.com/pulse/how-apply-financial-aid-courseraorg-satyam-sharma The format for all questions: Why are you applying for Financial Aid? I am satyamsharma, my family is facing difficulty for surviving , paying our 2 siblings college fee and having our daily meals , my father gets a very low annual pay which can’t even pay our annual fees of our college , we are 2 siblings , its really hard for us to get an education such as machine learning who are really passionate in this subject but a good friend of mine told me about the financial aid , and different organisation on coursera providing to help financially weak people to take specialization course and this thing was like a blessing for me and I requested my friend to help me in applying the financial aid for machine learning , he agreed and that’s why I am applying for financial aid today , I thank all the organisation on coursera for providing such a great opportunity to achieve our passion and helping us in serving the mankind. 2) How will taking this course help you achieve your career goals? Everyday I see my family facing problems for basic needs and my father struggling to pay our college fee , I always want to help my family in strengthening our financial condition since I am really passionate in programming in machine learning and with the help of financial aid provided by the great organisations on coursera , I feel myself blessed for such a great opportunity and I promise to give my 100% in learning everything in the field of machine learning and helping my family to stabilize our financial condition and the only way to achieve everything above is this course , this course will not only help me in achieve my career goals but only give me the skills which would help us survive and strength our financial condition. I would thank everyone on coursera for giving us such an good opportunity which would help a lot of people in achieving our career goals 3) If you answered no, please help us understand why. I would not consider paying a low-interest loan for course fee , as my family is facing difficulty for basic needs , paying our 2 siblings college fee and having our day to day meals that’s why I request the organization on coursera to please approve my financial aid for the machine learning programming and the result is with that financial aid I would achieve my career goal and that would help us having a stable financial state , I really feel blessed for this opportunity and I would be thank full for my friend who helped me referring the financial aid for the programming for everybody (Getting started with python) and the organisation (University of washington) who are giving us this valuable opportunity , thank you ,coursera. dont forget to share the video with your friends:)
Views: 104 empty minds
Coursera Statistical Inference Week 1 Quiz - Random Variables
 
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www.Stats-Lab.com | Coursera Data Science Specialization | Statistical Inference Advised to use Full Screen Mode
Views: 1651 Dragonfly Statistics
Data Science Festival 2018 - Marios Michailidis
 
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H2O’s Driverless AI – An AI that creates AI! Through my kaggle journey to the top spot, I have noticed that many of the things I do to perform competitively in data challenges can be automated. In fact automation is critical to achieve very predictive scores because while the machine “runs”, I can focus on other aspects of the modelling process to extract the most value. In this talk I will share some of the learnings derived from my kaggle experience and show how you can achieve competitive performance in predictive modelling tasks automatically, using H2O.ai’s Driverless AI. Marios Michailidis (@StackNet_) is a competitive data scientist at H2O.ai currently working on Driverless AI – a software that automates machine learning. He holds a Bsc in accounting Finance from the University of Macedonia in Greece, an Msc in Risk Management from the University of Southampton and a PhD in machine learning from the University College London (UCL) with a focus on ensemble modelling. Marios is the creator of kazAnova a freeware GUI for credit scoring and data mining. He is also the creator of StackNet Meta-Modelling Framework. In his spare time, he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform.
Data Capstone Project - Advanced Data Science with IBM
 
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This Project is a part of the Data Science Project with respect to the course from Advanced Data Science with IBM. This project describes the Multiclass Text Classification of the San Francisco Crime Data Set using Random Forest and Naive Bayes
Views: 42 Vijay Krishnan
R Finance Tutorial: Financial Return
 
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Like and comment on this video if you liked it! About the course: Learning R can be intimidating, especially without concrete examples you might see in the real world. In this finance oriented introduction to R, you will learn essential data structures such as lists and data frames and have the chance to apply that knowledge directly to financial examples. By the end of the course, you will feel comfortable with the basics of manipulating your data to perform financial analysis in R. Take this course: https://www.datacamp.com/courses/introduction-to-r-for-finance Great job so far! As this is a finance-oriented course, there are some financial terms and mathematics that you will have to be comfortable with to do the exercises. One of these is the concept of returns. It will be easiest to demonstrate this through an example. Suppose you have 50 dollars worth of Apple stock. If that stock earns you a 10% return in January, how much money do you have at the end of the month? The math for this isn't complicated! 10% of 50 is 5 dollars, so by adding this to your existing 50 dollars, you now have 55 dollars. Great! Now, let's think about this a different way. At the end of the month, you have 100% of your original cash, the 50 dollars, and now you have an extra 10%. In total, you now have 110% of your original value. Dividing this by 100 to turn percentages into decimals gives us the following equation. The 1.10 you see there is important. You will use this value as a return multiplier. Multiplying your original 50 dollars by this return multiplier gives us the correct answer of 55 dollars. Time to generalize this. The general formula in terms of variables for the return multiplier is the following: One, plus the interest rate divided by 100 to change it to a decimal. Using this, we can then say that the total amount at the next period is the original amount times the multiplier. This example can easily extend to multiple periods. For example, if you also earned an extra 5% in February, how much money would you have at the end of February? Simple! Take the amount at the end of January, 55 dollars, and multiply this by the return multiplier corresponding to 5%, 1.05. But wait, if you write this math another way, you can see that this is nothing more than the original 50 dollars, times the return multiplier for January, times the return multiplier for February. To move two months forward, we multiply our original 50 dollar by the return multipliers for those two months. Now it's your turn! The next few exercises will test your knowledge of financial returns in combination with variables!
Views: 1441 DataCamp
2018 – 2019 Data Analytics Practice Update
 
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We are excited to present the 2018 - 2019 Data Analytics Practice Update video with Bartosz Piwcewicz (FIAA) and Michael Storozhev (FIAA, CERA) on behalf of the Data Analytics Working Group (DAWG) and the Young Data Analytics Working Group (YDAWG). This video covers key Data Analytics industry issues in 2018, and emerging topics that will impact the Data Analytics sector in 2019. The 2018 – 2019 Practice Update video series aim to provide: - Key headline news from the industry/practice area; - Important changes to legislation, regulation, standards etc.; - Significant appointments to new roles; - Emerging topics and issues within the industry/practice area; - Details of the Practice Committee's activities; - Useful sources of CPD; and - Volunteering opportunities. Jump to each chapter of the Data Analytics Practice Update video below 00:35 – 2018 Key Industry News 04:12 – 2019 Key Industry Forecasts 07:01 – Useful Sources of CPD 09:06 – Volunteering 09:46 – Thank You View our useful sources of CPD below: - Actuaries in Data Analytics Microsite: https://actuaries.asn.au/microsites/actuaries-in-data-analytics - Actuaries Digital: https://www.actuaries.digital - Becoming an Analytics-Based Insurer: A Road Map: https://www.actuaries.digital/2018/01/02/becoming-an-analytics-based-insurer-a-road-map-part-1/ - Statistical Machine Learning | A Crash Course: http://actuaries.logicaldoc.cloud/download-ticket?ticketId=46fbcdcaac395e47964fc6a2dfd11487fb6ae8b - Actuaries Institute Events: https://actuaries.asn.au/events/calendar - Actuaries Institute Past Events: https://actuaries.asn.au/knowledge-bank/past-events - Actuaries Institute Podcasts: https://www.spreaker.com/show/actuaries-institutes-show - Podcast | Managing a Data Analytics Team: https://www.spreaker.com/user/actinst/managing-a-data-analytics-team - Online Courses: https://www.coursera.org/courses?query=johns%20hopkins - Data Analytics Newsletters: https://actuaries.asn.au/microsites/actuaries-in-data-analytics/knowledge/general - MeetUp: https://www.meetup.com/en-AU/topics/data-analytics/ Want to get involved with the DAWG or YDAWG? Write an article for Actuaries Digital, become an education volunteer, appear in our podcast, join one of the many committees/working groups or share your knowledge with us through the email below: [email protected] Thank you to our volunteers, working group members and HQ staff for all your continued hard work in supporting the Data Analytics Working Group and Young Data Analytics Working Group. View more Practice Update videos across different areas here: https://www.actuaries.asn.au/practice-area/practice-update-videos Follow the Actuaries Institute on our social channels: ↳ LinkedIn: https://www.linkedin.com/company/792645/ ↳ Facebook: https://www.facebook.com/pages/Actuaries-Institute/183337668450632 ↳ Instagram: https://www.instagram.com/ActuariesInst ↳ Twitter: https://www.twitter.com/ActuariesInst ↳ Spreaker: https://www.spreaker.com/user/actinst Subscribe to our YouTube channel: https://www.youtube.com/subscription_center?add_user=ActuariesInstitute About the Actuaries Institute As the sole professional body for Members in Australia and overseas, the Actuaries Institute represents the interests of the profession to government, business and the community. Actuaries assess risks through long-term analyses, modelling and scenario planning across a wide range of business problems. This unrivalled expertise enables the profession to comment on a range of business-related issues including enterprise risk management and prudential regulation, retirement income policy, finance and investment, general insurance, life insurance and health financing. Find out more about actuaries: https://www.actuaries.asn.au Copyright © 2018 by Institute of Actuaries of Australia All rights reserved. No part of this video (including the sound recording embodied therein) may be copied, reproduced, screened, distributed, or transmitted in any format by any means without the prior written permission of the publisher, or unless otherwise permitted by copyright law. For permission requests, write to the publisher, addressed “Attention: Permissions Coordinator,” at the address below. Disclaimer Learning material associated with this subject, including this video, is intended for education and training purposes only and is not intended to be used as the basis of work for which payment is received or to which legal liability is or may be attached Publisher The Institute of Actuaries of Australia ABN 69 000 423 656 Level 2, 50 Carrington Street, Sydney NSW 2000 Tel: +61 (0)2 9239 6100 Fax: +61 (0)2 9239 6170 https://www.actuaries.asn.au Actuaries Magazine https://www.actuaries.digital #actuary #actuaries #actuariesinstitute #practiceupdate #dataanalytics #dawg #ydawg #analytics #data #bigdata #datascience #video #workinggroup #science #meetup #podcast #media #coursera #event #statistics #machinelearning #ai
Views: 511 Actuaries Institute
Machine Learning - Data Preparation (Online Webinar)
 
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This webinar will discuss ways to prepare data for Machine Learning, including data cleaning, data sufficiency, feature engineering, feature scaling, datasets etc. This session will also demonstrate some Python programs around these concepts.
Views: 902 CellStrat
Statistics One with Andrew Conway
 
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The course "Statistics One" by Professor Andrew Conway from Princeton University, will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/stats1
Views: 25774 CourseraVideos
data mining in Telecommunication part 3
 
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کەمپینی بە کوردی کردنی زانست لە زانکۆی گەشەپیدانی مرۆیی
Views: 199 shahen uhd