Search results “A goal of data mining includes recent”
What your smart devices know (and share) about you | Kashmir Hill and Surya Mattu
Once your smart devices can talk to you, who else are they talking to? Kashmir Hill and Surya Mattu wanted to find out -- so they outfitted Hill's apartment with 18 different internet-connected devices and built a special router to track how often they contacted their servers and see what they were reporting back. The results were surprising -- and more than a little bit creepy. Learn more about what the data from your smart devices reveals about your sleep schedule, TV binges and even your tooth-brushing habits -- and how tech companies could use it to target and profile you. (This talk contains mature language.) Check out more TED Talks: http://www.ted.com The TED Talks channel features 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 more. Follow TED on Twitter: http://www.twitter.com/TEDTalks Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: https://www.youtube.com/TED
Views: 135414 TED
Bioinformatics part 2 Databases (protein and nucleotide)
For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.[9] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 102906 Shomu's Biology
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters What IS DATA ANALYTICS? Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources. Types of data analytics applications : At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis. Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. At the application level, BI and reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves. Keywords: being a data analyst, big data analyst, business analyst data warehouse, data analyst, data analyst accenture, data analyst accenture philippines, data analyst and data scientist, data analyst aptitude questions, data analyst at cognizant, data analyst at google, data analyst at&t, data analyst australia, data analyst basics, data analyst behavioral interview questions, data analyst business, data analyst career, data analyst career path, data analyst career progression, data analyst case study interview, data analyst certification, data analyst course, data analyst in hindi, data analyst in india, data analyst interview, data analyst interview questions, data analyst job, data analyst resume, data analyst roles and responsibilities, data analyst salary, data analyst skills, data analyst training, data analyst tutorial, data analyst vs business analyst, data mapping business analyst, global data analyst bloomberg, market data analyst bloomberg
Talks@12: Data Science & Medicine
Innovations in ways to compile, assess and act on the ever-increasing quantities of health data are changing the practice and police of medicine. Statisticians Laura Hatfield and Sherri Rose will discuss recent methodological advances and the impact of big data on human health. Speakers: Laura Hatfield, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Sherri Rose, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Like Harvard Medical School on Facebook: https://goo.gl/4dwXyZ Follow on Twitter: https://goo.gl/GbrmQM Follow on Instagram: https://goo.gl/s1w4up Follow on LinkedIn: https://goo.gl/04vRgY Website: https://hms.harvard.edu/
Data Collection and Preprocessing | Lecture 6
Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 2016 Leo Isikdogan
Facebook CEO Mark Zuckerberg testifies before Congress on data scandal
Facebook CEO Mark Zuckerberg will testify today before a U.S. congressional hearing about the use of Facebook data to target voters in the 2016 election. Zuckerberg is expected to offer a public apology after revelations that Cambridge Analytica, a data-mining firm affiliated with Donald Trump's presidential campaign, gathered personal information about 87 million users to try to influence elections. »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 134173 CBC News
Anomaly Detection: Algorithms, Explanations, Applications
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 17758 Microsoft Research
Using Data to Analyze Learning
Introduction to Educational Data Mining, Dr. Luc Paquette
Views: 1082 Education at Illinois
Mod-01 Lec-02 Data Mining, Data assimilation and prediction
Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 1939 nptelhrd
Machine Learning with R Tutorial: Identifying Clustering Problems
Make sure to like & comment if you liked this video! Take Hank's course here: https://www.datacamp.com/courses/unsupervised-learning-in-r Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible. Transcript: Hi! I'm Hank Roark, I'm a long-time data scientist and user of the R language, and I'll be your instructor for this course on unsupervised learning in R. In this first chapter I will define ‘unsupervised learning’, provide an overview of the three major types of machine learning, and you will learn how to execute one particular type of unsupervised learning using R. There are three major types of machine learning. The first type is unsupervised learning. The goal of unsupervised learning is to find structure in unlabeled data. Unlabeled data is data without a target, without labeled responses. Contrast this with supervised learning. Supervised learning is used when you want to make predictions on labeled data, on data with a target. Types of predictions include regression, or predicting how much of something there is or could be, and classification which is predicting what type or class some thing is or could be. The final type is reinforcement learning, where a computer learns from feedback by operating in a real or synthetic environment. Here is a quick example of the difference between labeled and unlabeled data. The table on the left is an example with three observations about shapes, each shape with three features, represented by the three columns. This table, the one on the left is an example of unlabeled data. If an additional vector of labels is added, like the column of labels on the right hand side, labeling each observation as belonging to one of two groups, then we would have labeled data. Within unsupervised learning there are two major goals. The first goal is to find homogeneous subgroups within a population. As an example let us pretend we have a population of six people. Each member of this population might have some attributes, or features — some examples of features for a person might be annual income, educational attainment, and gender. With those three features one might find there are two homogeneous subgroups, or groups where the members are similar by some measure of similarity. Once the members of each group are found, we might label one group subgroup A and the other subgroup B. The process of finding homogeneous subgroups is referred to as clustering. There are many possible applications of clustering. One use case is segmenting a market of consumers or potential consumers. This is commonly done by finding groups, or clusters, of consumers based on demographic features and purchasing history. Another example of clustering would be to find groups of movies based on features of each movie and the reviews of the movies. One might do this to find movies most like another movie. The second goal of unsupervised learning is to find patterns in the features of the data. One way to do this is through ‘dimensionality reduction’. Dimensionality reduction is a method to decrease the number of features to describe an observation while maintaining the maximum information content under the constraints of lower dimensionality. Dimensionality reduction is often used to achieve two goals, in addition to finding patterns in the features of the data. Dimensionality reduction allows one to visually represent high dimensional data while maintaining much of the data variability. This is done because visually representing and understanding data with more than 3 or 4 features can be difficult for both the producer and consumer of the visualization. The third major reason for dimensionality reduction is as a preprocessing step for supervised learning. More on this usage will be covered later. Finally a few words about the challenges and benefits typical in performing unsupervised learning. In unsupervised learning there is often no single goal of the analysis. This can be presented as someone asking you, the analyst, “to find some patterns in the data.” With that challenge, unsupervised learning often demands and brings out the deep creativity of the analyst. Finally, there is much more unlabeled data than labeled data. This means there are more opportunities to apply unsupervised learning in your work. Now it's your turn to practice what you've learned.
Views: 2483 DataCamp
Uncovering Clinical Insights From Unstructured EMR Data to Improve Patient Outcomes (Cloud Next '19)
Rush University Medical Center has identified an opportunity to standardize treatment and improve outcomes for their patients by drawing structured insights (SNOMED codes) from previously-unstructured data. Specifically, the provider, pathology, and lab notes stored in their EMR system contains all of the data necessary to generate a more complete picture of patients' health experience, from complaint and diagnosis to treatment and outcome. Maven Wave is working with Rush to implement a solution to map notes to SNOMED codes, allowing for a more objective approach to clinical care. Maven Wave, Rush, and Google are working to address this challenge. We are creating a solution that will allow for a more objective approach to clinical care by connecting symptoms, treatments, and outcomes. This broad goal will be achieved by: 1. Standardizing and operationalizing transfer of legacy and ongoing note data into a cloud repository for ease of processing. 2. Creating a scalable architecture that can work for pilots through system-wide implementations. 3. Extracting key medical definitions from unstructured notes and enriching them with treatment and outcome data to create insights for better patient outcomes. 4. Enabling continuous improvement through more advanced AI models (e.g., semantic data layer) and wider availability to the broader Rush ecosystem. Cloud Healthcare API → https://bit.ly/2UfpYVp Watch more: Next '19 Data Analytics Sessions here → https://bit.ly/Next19DataAnalytics Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform Speaker(s): Vasudha Gupta, Jawad Khan, David Patterson Session ID: DA111 product:Cloud Healthcare,BigQuery,Cloud Dataflow; fullname:Vasudha Gupta;
DAX for Power BI - Predicting Days Until a Goal (Linear Regression)
In this video, we learn how to calculate a linear regression line. Coupled with What If Analysis to set a goal, we can predict how long it will take to reach that goal. This is a fun and fairly advanced problem that can be used for some simple predictive analytics. Play with my YouTube data! https://bielite.com/#try To enroll in my introductory Power BI course: https://www.udemy.com/learn-power-bi-fast/?couponCode=CHEAPEST Daniil's Blog Post: https://xxlbi.com/blog/simple-linear-regression-in-dax/
Views: 3246 BI Elite
Making AI real with SQL Server Azure databases and Azure big data analytics services - GS005
Are you interested in learning how data and AI can transform your business? If so, this is a ‘must see’ session for you. Be the first to learn about the latest innovations in Microsoft’s Data and AI platform, including SQL Server, Azure SQL Database, Azure Cosmos DB, Azure SQL Data Warehouse, Azure Data Factory and Azure Databricks. Come see our latest demos showcasing the new innovation and learn how these products and services can help you modernize your entire data estate, across on-premises and in the cloud, to help you transform your business with AI-driven insights. Also hear firsthand from customers like Shell, Komatsu, Symantec and Anheuser Busch about their digital transformation journeys using Microsoft’s Data and AI platform. Learn more about Azure AI, databases, and big data analytics: Azure AI platform: https://azure.com/ai Azure SQL Database: https://azure.com/sqldatabase Azure Cosmos DB: https://azure.com/cosmosdb Azure SQL Data Warehouse: https://azure.com/sqldw Azure Data Factory: https://azure.com/adf Azure Databricks: https://azure.com/databricks
Views: 1083 Microsoft Ignite
Advanced Data Mining with Weka (2.6: Application to Bioinformatics – Signal peptide prediction)
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 6: Application to Bioinformatics – Signal peptide prediction http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3030 WekaMOOC
Introduction to Text Analytics with R: Overview
The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5_y0 Watch the latest video tutorials here: https://hubs.ly/H0hz61V0 See what our past attendees are saying here: https://hubs.ly/H0hz6-S0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 74453 Data Science Dojo
fuzzy logic in artificial intelligence in hindi | introduction to fuzzy logic example | #28
fuzzy logic in artificial intelligence in hindi | fuzzy logic example | #28 Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO. The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as − CERTAINLY YES POSSIBLY YES CANNOT SAY POSSIBLY NO CERTAINLY NO well,academy,Fuzzy logic in hindi,fuzzy logic in artificial intelligence in hindi,artificial intelligence fuzzy logic,fuzzy logic example,fuzzy logic in artificial intelligence,fuzzy logic with example,fuzzy logic in artificial intelligence in hindi with exapmle,fuzzy logic,what is fuzzy logic in hindi,what is fuzzy logic with example,introduction to fuzzy logic
Views: 160489 Well Academy
China moon mission: China far side Moon landing launch a new space race? Chang'e 4 touch on lunar!
China moon mission: China far side Moon landing launch a new space race? Chang'e 4 touch on lunar! China far side of the moon mission is just the start of its space ambitions china moon landing. Chinese lunar goddess Chang'e, the Yutu 2 rover is making history as it sends back images and other data from the far side of the moon. The rover touched down , delivered to the moon by the Chang'e 4 probe, a historical first for humankind -- the far side of the moon has not previously been visited -- and a major achievement for China's increasingly impressive space program. Its success "opened a new chapter in humanity's exploration of the moon. Beijing Aerospace Control Center reacting to the touchdown, alongside one of the first images sent back by Chang'e 4 of the moon's far side. Keeping that in mind in this video, Engineering Today will discuss about China far side of the moon mission with achievements in space. Will China’s moon landing launch a new space race far side of the moon? CHINA'S ACHIEVEMENTS IN SPACE China first engaged in space activities. In 1978, Deng Xiaoping articulated China's space policy noting that, as a developing country, China would not take part in a space race. Instead, China's space efforts have focused on both launch vehicles and satellites. China's first space station, Tiangong-1. Missions like Chang'e 4. Its first lunar mission, Chang'e 1, orbited the moon and a rover landed on the moon. China's future plans-include a new space station, a lunar base and possible sample return missions from Mars china moon landing China moon mission, in China, where economic concerns are becoming increasingly pressing amid an ongoing trade war with the US -- was more limited than for the previous lunar mission, the success of Chang'e 4, and the global acclaim it has brought, will be a significant boost to the Chinese space program china moon landing. DREAMS OF SPACE The first stage of China's space dream. In 2020, the next lunar mission, Chang'e 5, is due to land on the moon. Lunar mission in the 2030s. China put a citizen on the moon. The Tiangong 2 space lab has been in orbit for over two years. "Our overall goal is that, by around 2030, China will be among the major space powers of the world," Wu Yanhua, deputy chief of the National Space Administration, said in 2016. But despite these big steps forward, China still has a long way to catch up in the space race. As Chang'e 4 was preparing to descend to the lunar surface, NASA sent back photos of Ultima Thule, the first ever flyby of an object in the Kuiper Belt, a collection of asteroids and dwarf planets a billion miles beyond Pluto. One achievement could see China leapfrog the US, however, and make history in the progress: landing an astronaut on Mars. RED PLANET Not since Gene Cernan climbed on board the Apollo 17 lunar module to return to Earth has humanity stepped foot on anything outside our planet far side of the moon. no one wants to be the first country to leave a corpse on the moon. This isn't to say the manned lunar missions were useless. Those advancements will be key in delivering a person to Mars, a far, far harder task. China will make its first visit to Mars with an unmanned probe set to launch by the end of next year, followed by mars MOON MINING China's space program is about more than that. The moon plays host to a wealth of mineral resources. China already dominates the global supply of REM, and exclusive access to the moon's supply could provide huge economic advantages. In addition to REM, the moon also possesses a large amount of Helium-3. Chinese space scientist, has long advocated for Helium-3 mining as a reason for moon missions. A NEW SPACE RACE? the Chinese space program, is its slow and steady pace. Because of the secrecy that surrounds many aspects of the Chinese space program, its exact capabilities are unknown. However, the program is likely on par with its counterparts of china moon landing. In terms of military applications, China has also demonstrated significant skills. In 2007, it undertook an anti-satellite test, launching a ground-based missile to destroy a failed weather satellite. While successful, the test created a cloud of orbital debris that continues to threaten other satellites. The movie "Gravity" illustrated the dangers space debris poses to both satellites and humans far side of the moon. The U.S., unlike other countries, has not engaged in any substantial cooperation with China because of national security concerns. In fact, a 2011 law bans official contact with Chinese space officials. Does this signal a new space race between the U.S. and China space race. Chinese space program at the International Astronautical Conference in Germany and discussed areas where China and the U.S. can work together for china moon landing. The Trump administration has used the threat posed by China and Russia to support its argument for a new independent military branch, a Space Force.
Mega-R1. Rule-Based Systems
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter In this mega-recitation, we cover Problem 1 from Quiz 1, Fall 2009. We begin with the rules and assertions, then spend most of our time on backward chaining and drawing the goal tree for Part A. We end with a brief discussion of forward chaining. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 28233 MIT OpenCourseWare
TOP 7 Blockchain Gaming Projects
📲 Download NOW: http://onelink.to/dq89au ✅ Subscribe to our channel: https://goo.gl/qxm8jk These are 7 of the most exciting blockchain gaming projects out there! It's always awesome to see the possibilities of innovation with new technologies and blockchain brings a whole lot to the table! From Enjin to CryptoKitties and other projects all of these explore at least one facet of blockchain technology and integrate it into the gaming world. Which project are you most excited about? -- The goal of the Pillar Project is to return control over personal data back to its rightful owner - you. Starting with an open-source wallet to store, transact, and track cryptocurrencies and tokens, the Pillar Wallet will evolve into a decentralized, personal data-management platform. Our data is getting more concentrated in the hands of a few large institutions while the threat of corruption, theft, sale, and loss grows. This is a security risk. We rely on centralized institutions to store important data: Shopping habits Location Browsing history DNA Medical information Driving history Bank records Trading history Credit scores Pillar is developing a decentralized solution for data management. Starting with a mobile wallet to manage cryptocurrencies and tokens, the Pillar Wallet will evolve to include the services you’re used to on the internet like e-commerce, publications and more. Currently, such platforms act as a custodian of your personal information, storing it on centralized servers. 🌐 Learn more at http://bit.ly/2HT8yrR 🎥 Check out our latest video: https://goo.gl/urQrex 📬 Subscribe to our newsletter: http://eepurl.com/di8X2L 🙌🏼 Pillar Project offers 24/7 in-app live tech support. For any issues, you can reach us via live chat on our website or via [email protected] Follow Pillar's social channels ⏬ 💬 Pillar Forum: https://forum.pillarproject.io 🐦 Twitter: https://goo.gl/BFR39G 💻 Medium: https://goo.gl/UmWra4 📈 BitcoinTalk: https://goo.gl/eZnB2V 🤖 Reddit: https://goo.gl/1C9pJ2 🐱 GitHub: https://goo.gl/cNHi5B 💬 Telegram: https://goo.gl/dZnrdp
Views: 707 Pillar Project
Social Network Analysis
An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 5232 Microsoft Research
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 51584 edureka!
2019 CR1 Visa Timeline for Spouse visa to USA
http://www.visacoach.com/cr1-visa-timeline.html Before you decide on the spouse visa path, it is essential you understand just how long it will take before you make any irrevocable decisions or actions. Many of my clients were shocked and surprised after they returned from their honeymoon to start the visa process to find out not only is the spouse visa slower than a Fiance Visa, but in fact the time it takes is measured in years not months or weeks. To Schedule your Free Case Evaluation with Fred Wahl, the Visa Coach visit http://www.visacoach.com/talk.html or Call - 1-800-806-3210 ext 702 or 1-213-341-0808 ext 702 Bonus eBook “5 Things you Must Know before Applying for your Visa” get it at http://www.visacoach.com/five.html Fiancee or Spouse visa, Which one is right for you? http://imm.guru/k1vscr1 What makes VisaCoach Special? Ans: Personally Crafted Front Loaded Presentations. Front Loaded Fiance Visa Petition http://imm.guru/front Front Loaded Spouse Visa Petition http://imm.guru/frontcr1 K1 Fiancee Visa http://imm.guru/k1 K1 Fiance Visa Timeline http://imm.guru/k1time CR1 Spousal Visa http://imm.guru/cr1 CR1 Spouse Visa Timeline http://imm.guru/cr108 Green Card /Adjustment of Status http://imm.guru/gc How long does it take to get a CR1 Spouse visa? As of 2019, the answer is 14 to 18 months on average. 6 to 8 months USCIS 5 to 7 months NVC 2 to 3 months Consulate I regularly get calls from people saying those numbers must be wrong, because they found a website or person who promised a MUCH shorter processing time so what's their secret? Well the secret is they are either telling you what "you want to hear" so they can get your money, or just referring to one step of the process, not ALL the steps from initial submission of your petition, to visa embossed onto your spouse's passport When I give time estimates I always use what is relevant to the couple, and that is starting from the day USCIS receives the petition, ending on the day your foreign spouse gets the visa. Two different departments of the US government are involved, USCIS (homeland security) and the Department of State. From Mid 2017 through now Homeland Security recently is getting their job done relatively slowly, currently taking 6 to 8 months. (this compares to processing times of a 2 to 3 months years ago) Why is USCIS now taking 2 to 3 times as long? I call this the Trump Effect. President Trump after taking office in January 2017 has mandated that USCIS vigorously enforce and administer immigration laws, take no short cuts. The goal is to restrict Legal immigration while stopping illegal immigration. "We have to get much tougher, much smarter, and less politically correct," Trump said. What this means is that they are very closely examining and scrutinizing all cases looking for reasons to deny. In addition cases that regularly had their interviews waived now specifically there is an Executive order that no interviews regardless of the strength of their evidences, may be waived. The result is USCIS has more work to do, has more bases to touch in the processing of EACH case. And while President Trump has promised to hire more staff to handle the increased load, so far no new staff has been hired, but the workload has increased. This is the Trump Effect. More work, with same staff. The result is that USCIS processing times for spouse visas have stretched to take at least 6 to 8 months. And it is possible this may even get worse, depending on how many new steps USCIS is asked to take, such as "extreme vetting" and "social media data mining" that are new labor intensive steps that have been proposed but not implemented yet. USCIS Processing includes a background check by the FBI In addition to the general slow down due to the "Trump Effect" what also affects how long it takes for USCIS to approve your case is a function of how complete your petition is, how busy the processing center is, how current your FBI file is, and a bit of luck. The most obvious source of added delay is caused by incomplete and sloppy petitions. When USCIS finds a problem, processing grinds to a halt, and it is stopped until the problem is fixed. Sometimes the errors are so big that they don't bother asking for corrections and simply deny a case outright. Once USCIS finishes their part, the case is passed to the US Department of State. The Department of State has a processing center in New Hampshire, called the National Visa Center or NVC. NVC has now completely revised the way spouse visas are processed there. Previously one submitted a hard copy package of civil and financial documents for NVC to review. Now NVC has instituted a fully online system where all documents are submitted electronically over the Internet. So far this system has been fairly buggy. With frequent technical outages and problems.
Views: 5596 Visa Coach
Zoho Docs - Goal Seek Tool
Zoho Docs Goal Seek feature allows you to alter data in formulas to get different results in spreadsheets.
Views: 549 Zoho Docs
Twitter API with Python: Part 2 -- Cursor and Pagination
In this video, we will continue with our use of the Tweepy Python module and the code that we wrote from Part 1 of this series: https://www.youtube.com/watch?v=wlnx-7cm4Gg The goal of this video will be to understand how Tweepy handles pagination, that is, how can we use Tweepy to comb over the various pages of tweets? We will see how to accomplish this by making use of Tweepy's Cursor module. In doing so, we will be able to directly access tweets, followers, and other information directly from our own timeline. We will also continue to improve the code that we wrote from Part 1 Relevant Links: Part 1: https://www.youtube.com/watch?v=wlnx-7cm4Gg Part 2: https://www.youtube.com/watch?v=rhBZqEWsZU4 Part 3: https://www.youtube.com/watch?v=WX0MDddgpA4 Part 4: https://www.youtube.com/watch?v=w9tAoscq3C4 Part 5: https://www.youtube.com/watch?v=pdnTPUFF4gA Tweepy Website: http://www.tweepy.org/ Cursor Docs: http://docs.tweepy.org/en/v3.5.0/cursor_tutorial.html API Reference: http://docs.tweepy.org/en/v3.5.0/api.html GitHub Code for this Video: https://github.com/vprusso/youtube_tutorials/tree/master/twitter_python/part_2_cursor_and_pagination My Website: vprusso.github.io This video is brought to you by DevMountain, a coding boot camp that offers in-person and online courses in a variety of subjects including web development, iOS development, user experience design, software quality assurance, and salesforce development. DevMountain also includes housing for full-time students. For more information: https://devmountain.com/?utm_source=Lucid%20Programming Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here: http://bit.ly/lp_vim If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe: http://bit.ly/lp_subscribe
Views: 13769 LucidProgramming
Webinar: Data Science for Beginners - How to Get Started
Data science training 75% OFF coupon: http://bit.ly/2TOqJ7A DOWNLOAD THE RESOURCES: http://bit.ly/2TKO228 What it takes to become a data scientist -- starting in the right place. In this webinar two of our instructors, Iliya and Simona, talk about the 3 things they needed to learn before all the books and trainings started to finally click. They discuss the most confusing data science terms, how they fit together, and where in the data processing timeline the data science processes happen. MORE INFORMATION ABOUT THE TRAINING: http://bit.ly/2TOqJ7A Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Twitter: https://twitter.com/365datascience LinkedIn: https://www.linkedin.com/company/365d... Get in touch about the training at: [email protected] Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you!
Views: 7916 365 Data Science
Environment Impact Assessment Part 1
Support us : https://www.instamojo.com/@exambin/ Download our app : http://examb.in/app Environmental Impact Assessment Developmental projects in the past were undertaken without any consideration to their environmental consequences. As a result the whole environment got polluted and degraded. In view of the colossal damage done to the environment, governments and public are now concerned about the environmental impacts of developmental activities. So, to assess the environmental impacts, the mechanism of Environmental Impact Assessment also known as EIA was introduced. EIA is a tool to anticipate the likely environmental impacts that may arise out of the proposed developmental activities and suggest measures and strategies to reduce them. EIA was introduced in India in 1978, with respect to river valley projects. Later the EIA legislation was enhanced to include other developmental sections since 1941. EIA comes under Notification on Environmental Impact Assessment (EIA) of developmental projects 1994 under the provisions of Environment (Protection) Act, 1986. Besides EIA, the Government of India under Environment (Protection) Act 1986 issued a number of other notifications, which are related to environmental impact assessment. EIA is now mandatory for 30 categories of projects, and these projects get Environmental Clearance (EC) only after the EIA requirements are fulfilled. Environmental clearance or the ‘go ahead’ signal is granted by the Impact Assessment Agency in the Ministry of Environment and Forests, Government of India. Projects that require clearance from central government can be broadly categorized into the following sectors • Industries • Mining • Thermal power plants • River valley projects • Infrastructure • Coastal Regulation Zone and • Nuclear power projects The important aspects of EIA are risk assessment, environmental management and Post product monitoring. Functions of EIA is to 1. Serve as a primary environmental tool with clear provisions. 2. Apply consistently to all proposals with potential environmental impacts. 3. Use scientific practice and suggest strategies for mitigation. 4. Address all possible factors such as short term, long term, small scale and large scale effects. 5. Consider sustainable aspects such as capacity for assimilation, carrying capacity, biodiversity protection etc... 6. Lay down a flexible approach for public involvement 7. Have a built-in mechanism of follow up and feedback. 8. Include mechanisms for monitoring, auditing and evaluation. In order to carry out an environmental impact assessment, the following are essential: 1. Assessment of existing environmental status. 2. Assessment of various factors of ecosystem (air, water, land, biological). 3. Analysis of adverse environmental impacts of the proposed project to be started. 4. Impact on people in the neighborhood. Benefits of EIA • EIA provides a cost effective method to eliminate or minimize the adverse impact of developmental projects. • EIA enables the decision makers to analyses the effect of developmental activities on the environment well before the developmental project is implemented. • EIA encourages the adaptation of mitigation strategies in the developmental plan. • EIA makes sure that the developmental plan is environmentally sound and within limits of the capacity of assimilation and regeneration of the ecosystem. • EIA links environment with development. The goal is to ensure environmentally safe and sustainable development. Environmental Components of EIA: The EIA process looks into the following components of the environment: • Air environment • Noise component : • Water environment • Biological environment • Land environment EIA Process and Procedures Steps in Preparation of EIA report • Collection of baseline data from primary and secondary sources; • Prediction of impacts based on past experience and mathematical modelling; • Evolution of impacts versus evaluation of net cost benefit; • Preparation of environmental management plans to reduce the impacts to the minimum; • Quantitative estimation of financial cost of monitoring plan and the mitigation measures. Environment Management Plan • Delineation of mitigation measures including prevention and control for each environmental component, rehabilitation and resettlement plan. EIA process: EIA process is cyclical with interaction between the various steps. 1. Screening 2. Scoping 3. Collection of baseline data 4. Impact prediction 5. Mitigation measures and EIA report 6. Public hearing 7. Decision making 8. Assessment of Alternatives, Delineation of Mitigation Measures and Environmental Impact Assessment Report 9. Risk assessment
Views: 24812 Exambin
Data Analysis & Using Solver   Excel 2013 Beginners Tutorial
Microsoft Excel, this list covers all the basics you need to start entering your data and building organized workbooks Main Play list : http://goo.gl/O5tsH2 (70+ Video) Subscribe Now : http://goo.gl/2kzV8M Topics include: 1. What is Excel and what is it used for? 2. Using the menus 3. Working with dates and times 4. Creating simple formulas 5. Formatting fonts, row and column sizes, borders, and more 6. Inserting shapes, arrows, and other graphics 7. Adding and deleting rows and columns 8. Hiding data 9. Moving, copying, and pasting 10. Sorting and filtering data 11. Securing your workbooks 12. Tracking changes
Views: 62879 tutorbeta
Advanced Data Mining with Weka (4.2: Installing with Apache Spark)
Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Installing with Apache Spark http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2701 WekaMOOC
K-means clustering: how it works
Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 547829 Victor Lavrenko
Excel Lesson 03: Cell Referencing and What-If Analyses in Microsoft Excel
This video is intended for people who want to learn about cell referencing and how to conduct a what-if analysis in Microsoft Excel. Topics covered in this lesson include relative cell references, absolute cell references, mixed cell references, the difference between functions and formulas, and conducting a what-if analysis in Excel. You may jump to any of these topics by using the links below: 1. Cell referencing: (1:07) 2. Relative cell references: (1:25) 3. Absolute cell references: (4:02) 4. Mixed cell references: (7:28) 5. Functions vs. formulas: (10:08) 6. Conducting a what-if analysis: (11:49)
Views: 32139 Dr. Daniel Soper
Machine Learning and Causal Inference for Policy Evaluation
Author: Susan Athey Abstract: A large literature on causal inference in statistics, econometrics, biostatistics, and epidemiology (see, e.g., Imbens and Rubin [2015] for a recent survey) has focused on methods for statistical estimation and inference in a setting where the researcher wishes to answer a question about the (counterfactual) impact of a change in a policy, or ""treatment"" in the terminology of the literature. The policy change has not necessarily been observed before, or may have been observed only for a subset of the population; examples include a change in minimum wage law or a change in a firm's price. The goal is then to estimate the impact of small set of ""treatments"" using data from randomized experiments or, more commonly, ""observational"" studies (that is, non-experimental data). The literature identifies a variety of assumptions that, when satisfied, allow the researcher to draw the same types of conclusions that would be available from a randomized experiment. To estimate causal effects given non-random assignment of individuals to alternative policies in observational studies, popular techniques include propensity score weighting, matching, and regression analysis; all of these methods adjust for differences in observed attributes of individuals. Another strand of literature in econometrics, referred to as ""structural modeling,"" fully specifies the preferences of actors as well as a behavioral model, and estimates those parameters from data (for applications to auction-based electronic commerce, see Athey and Haile [2007] and Athey and Nekipelov [2012]). In both cases, parameter estimates are interpreted as ""causal,"" and they are used to make predictions about the effect of policy changes. In contrast, the supervised machine learning literature has traditionally focused on prediction, providing data-driven approaches to building rich models and relying on cross-validation as a powerful tool for model selection. These methods have been highly successful in practice. This talk will review several recent papers that attempt to bring the tools of supervised machine learning to bear on the problem of policy evaluation, where the papers are connected by three themes. The first theme is that it important for both estimation and inference to distinguish between parts of the model that relate to the causal question of interest, and ""attributes,"" that is, features or variables that describe attributes of individual units that are held fixed when policies change. Specifically, we propose to divide the features of a model into causal features, whose values may be manipulated in a counterfactual policy environment, and attributes. A second theme is that relative to conventional tools from the policy evaluation literature, tools from supervised machine learning can be particularly effective at modeling the association of outcomes with attributes, as well as in modeling how causal effects vary with attributes. A final theme is that modifications of existing methods may be required to deal with the ""fundamental problem of causal inference,"" namely, that no unit is observed in multiple counterfactual worlds at the same time: we do not see a patient at the same time with and without medication, and we do not see a consumer at the same moment exposed to two different prices. This creates a substantial challenge for cross-validation, as the ground truth for the causal effect is not observed for any individual. ACM DL: http://dl.acm.org/citation.cfm?id=2785466 DOI: http://dx.doi.org/10.1145/2783258.2785466
Data Issues: Multiple Testing, Bias, Confounding, Missing...
Dr. Lance Waller from Emory University presents a lecture titled "Data Issues: Multiple Testing, Bias, Confounding, & Missing Data." View Slides https://drive.google.com/open?id=0B4IAKVDZz_JUczRSd0NucjlhT00 Lecture Abstract Once data are scraped, wrangled, linked, merged, and analyzed, what information do they reveal and can we trust the resulting conclusions? In this presentation, we define and review data issues relating to the analysis and interpretation of observational data from the field of epidemiology and consider implications for data science, especially regarding the goal of moving from big data to knowledge. Specifically, we explore concepts of bias, confounding, effect modification, and missing/mismeasured data as applied to data science. We provide an analytic context based on sampling concepts and explore relevant literature and tools from epidemiology, biostatistics, computer science, and data science. As with many issues in data science, the full applicability of the concepts is very much a work in progress and present multiple opportunities for future development. About the Speaker Lance A. Waller, Ph.D. is Rollins Professor and Chair of the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Committee on Applied and Theoretical Statistics. His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley). Join our weekly meetings from your computer, tablet or smartphone. Visit our website to view our schedule and join our next live webinar! http://www.bigdatau.org/data-science-seminars
Real-Time Crime Forecasting Challenge, National Institute of Justice
​ This webinar will offer a brief overview of the National Institute of Justice and the data science needs of the criminal justice field. In addition, it will provide details about the Crime Forecasting Challenge, including who can submit, how to retrieve datasets, and the submission categories. Attendees will have the opportunity to ask questions during this webinar. The overall goal of the Crime Forecasting Challenge is to harness recent advances in data science to drive innovation in algorithms that advance place-based crime forecasting. Contestants will be provided a real-time dataset from one police jurisdiction with which to work, and from which to develop place-based crime forecasts for that jurisdiction. Enter the Challenge: http://nij.gov/funding/Pages/fy16-crime-forecasting-challenge.aspx (Opinions or points of view expressed represent the speaker and do not necessarily represent the official position or policies of the U.S. Department of Justice. Any product or manufacturer discussed is presented for informational purposes only and do not constitute product approval or endorsement by the U.S. Department of Justice.)
financial modeling 101, financial modeling basics, and best practices
financial modeling 101, financial modeling basics, and best practices. what is financial modeling? planning for the future of your small business is an important part of success. financial modeling takes different shapes, but basically, it’s about plugging different numbers and scenarios into a formula very often on an excel sheet and seeing the effect they have. a well-built financial model will help a business owner understand the costs and profits from their management decisions. what will it cost to open a new location, hire a new employee, and how does that impact the bottom line? these can even tell businesses they have enough customer service people to take on the number of customers they want to next year. that’s why using financial statements and market research will give you more accurate results. it’s even a good idea to consider a professional consultant to get an objective base to start from. you can get a bunch of different scenarios by changing the variables which can be factors like the size of your target market, price per unit (which can even include extra selling costs like transportation) and estimated profit. one of the best things about financial modeling is it’s always a work in progress. as time goes by and your small business conditions change, you’ll always have the ability to plug in new numbers to see what comes out. as you might imagine, there are a variety of financial models to choose from. however, there are a few that are considered standards: three-statement model. this is one of the more basic ones that covers incomes statements, cash flow, and balance sheets. discounted cash flow model. don’t let the name scare you off. this model builds on the previous one to value a company. budget models. as the name implies, this is the model that’s used to put a budget together. other models that small business should find helpful include a forecasting model and option pricing model that basically makes use of the calculator built into excel.
Josh Reich: Simple banking to help customers meet their financial goals
The best way to improve personal banking and meet your financial goals is to ditch your traditional bank, says Josh Reich, CEO of Simple. Reich's online platform and Simple card gives users access to ATMs and real-time banking that instantly shows transactions on mobile devices -- all for free. Simple banking helps people plan their future with features like the daily Safe-to-Spend Balance which highlights savings goals and gives easy to understand account information. Co-founding Simple in 2009, Reich's background includes running a data mining consulting firm, a quantitative strategy group at a $10-billion fund and he did a stint at Root Exchange.
Computational Biology in the 21st Century: Making Sense out of Massive Data
Computational Biology in the 21st Century: Making Sense out of Massive Data Air date: Wednesday, February 01, 2012, 3:00:00 PM Category: Wednesday Afternoon Lectures Description: The last two decades have seen an exponential increase in genomic and biomedical data, which will soon outstrip advances in computing power to perform current methods of analysis. Extracting new science from these massive datasets will require not only faster computers; it will require smarter algorithms. We show how ideas from cutting-edge algorithms, including spectral graph theory and modern data structures, can be used to attack challenges in sequencing, medical genomics and biological networks. The NIH Wednesday Afternoon Lecture Series includes weekly scientific talks by some of the top researchers in the biomedical sciences worldwide. Author: Dr. Bonnie Berger Runtime: 00:58:06 Permanent link: http://videocast.nih.gov/launch.asp?17563
Views: 5091 nihvcast
Coding With Python :: Learn API Basics to Grab Data with Python
Coding With Python :: Learn API Basics to Grab Data with Python This is a basic introduction to using APIs. APIs are the "glue" that keep a lot of web applications running and thriving. Without APIs much of the internet services you love might not even exist! APIs are easy way to connect with other websites & web services to use their data to make your site or application even better. This simple tutorial gives you the basics of how you can access this data and use it. If you want to know if a website has an api, just search "Facebook API" or "Twitter API" or "Foursquare API" on google. Some APIs are easy to use (like Locu's API which we use in this video) some are more complicated (Facebook's API is more complicated than Locu's). More about APIs: http://en.wikipedia.org/wiki/Api Code from the video: http://pastebin.com/tFeFvbXp If you want to learn more about using APIs with Django, learn at http://CodingForEntrepreneurs.com for just $25/month. We apply what we learn here into a Django web application in the GeoLocator project. The Try Django Tutorial Series is designed to help you get used to using Django in building a basic landing page (also known as splash page or MVP landing page) so you can collect data from potential users. Collecting this data will prove as verification (or validation) that your project is worth building. Furthermore, we also show you how to implement a Paypal Button so you can also accept payments. Django is awesome and very simple to get started. Step-by-step tutorials are to help you understand the workflow, get you started doing something real, then it is our goal to have you asking questions... "Why did I do X?" or "How would I do Y?" These are questions you wouldn't know to ask otherwise. Questions, after all, lead to answers. View all my videos: http://bit.ly/1a4Ienh Get Free Stuff with our Newsletter: http://eepurl.com/NmMcr The Coding For Entrepreneurs newsletter and get free deals on premium Django tutorial classes, coding for entrepreneurs courses, web hosting, marketing, and more. Oh yeah, it's free: A few ways to learn: Coding For Entrepreneurs: https://codingforentrepreneurs.com (includes free projects and free setup guides. All premium content is just $25/mo). Includes implementing Twitter Bootstrap 3, Stripe.com, django south, pip, django registration, virtual environments, deployment, basic jquery, ajax, and much more. On Udemy: Bestselling Udemy Coding for Entrepreneurs Course: https://www.udemy.com/coding-for-entrepreneurs/?couponCode=youtubecfe49 (reg $99, this link $49) MatchMaker and Geolocator Course: https://www.udemy.com/coding-for-entrepreneurs-matchmaker-geolocator/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Marketplace & Dail Deals Course: https://www.udemy.com/coding-for-entrepreneurs-marketplace-daily-deals/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Free Udemy Course (40k+ students): https://www.udemy.com/coding-for-entrepreneurs-basic/ Fun Fact! This Course was Funded on Kickstarter: http://www.kickstarter.com/projects/jmitchel3/coding-for-entrepreneurs
Views: 449557 CodingEntrepreneurs
Getting Started with Weka - Machine Learning Recipes #10
Hey everyone! In this video, I’ll walk you through using Weka - The very first machine learning library I’ve ever tried. What’s great is that Weka comes with a GUI that makes it easy to visualize your datasets, and train and evaluate different classifiers. I’ll give you a quick walkthrough of the tool, from installation all the way to running experiments, and show you some of what it can do. This is a helpful library to have while you’re learning ML, and I still find it useful today to experiment with new datasets. Note: In the video, I quickly went through testing. This is an important topic in ML, and how you design and evaluate your experiments is even more important than the classifier you use. Although I publish these videos at turtle speed, I’ve started working on an experimental design one, and that’ll be next! Also, we will soon publish some testing tips and best practices on tensorflow.org (https://goo.gl/nZcS5R). Links from the video: Weka → https://goo.gl/2TYjGZ Ready to use datasets → https://goo.gl/PM8DtH More on evaluating classifiers, particularly in the medical domain → https://goo.gl/TwTYyk Check out the Machine Learning Recipes playlist → https://goo.gl/KewA03 Follow Josh on Twitter → https://twitter.com/random_forests Subscribe to the Google Developers channel → http://goo.gl/mQyv5L
Views: 79014 Google Developers
Text Data Mining Research Using Copyrighted & Use-Limited Text Data
Text Data Mining (TDM) Research Using Copyrighted and Use-Limited Text Data Sets: Developing an Agenda to Support Scholarly Use Beth Sandore Namachchivaya University Librarian University of Waterloo See https://www.cni.org/topics/information-access-retrieval/text-data-mining-tdm-research-using-copyrighted-and-use-limited-text-data-sets-developing-an-agenda-to-support-scholarly-use for more information about this talk. Coalition for Networked Information (CNI) Spring 2018 Membership Meeting April 12-13, 2018 Washington, DC cni.org/mm/spring-2018/
"Automated Digital Forensics" (CRCS Lunch Seminar)
CRCS Lunch Seminar (Monday, October 18, 2010) Speaker: Simson Garfinkel, Naval Postgraduate School Title: Automated Digital Forensics Abstract: Despite what you may have seen in the movies, today the primary use of digital forensics is to demonstrate the presence of child pornography on the computer systems of suspected criminal perpetrators. Although digital forensics has a great potential for providing criminal leads and assisting in criminal investigations, today's tools are incredibly difficult to use and there is a nationwide shortage of trained forensic investigators. As a result, computer forensics is most often a tool used for security convictions, not for performing investigations. This talk presents research aimed at realizing the goal of Automated Digital Forensics—research that brings the tools of data mining and artificial intelligence to the problems of digital forensics. The ultimate goal of this research is to create automated tools that will be able to ingest a hard drive or flash storage device and produce a high-level reports that be productively used by relatively untrained individuals. This talk will present: * A brief introduction to digital forensics and related privacy issues. * Histogram Analysis -- Using Frequency and Context to understand disks without understanding files. • Instant Drive Analysis, our work which allows the contents of a 1TB hard drive to be inventoried in less than 45 seconds using statistical sampling. • Our efforts to build Standardized Forensic Corpora of files and disk images, so that work different practitioners can be scientifically compared. Many of the tools and much of the data that we will discuss can be downloaded from the author's websites at http://afflib.org/ and http://digitalcorpora.org/ Bio: Simson L. Garfinkel is an Associate Professor at the Naval Postgraduate School in Monterey, California. His research interests include computer forensics, the emerging field of usability and security, personal information management, privacy, information policy and terrorism. He holds six US patents for his computer-related research and has published dozens of journal and conference papers in security and computer forensics. Garfinkel is the author or co-author of fourteen books on computing. He is perhaps best known for his book Database Nation: The Death of Privacy in the 21st Century. Garfinkel's most successful book, Practical UNIX and Internet Security (co-authored with Gene Spafford), has sold more than 250,000 copies and been translated into more than a dozen languages since the first edition was published in 1991. Garfinkel is also a journalist and has written more than a thousand articles about science, technology, and technology policy in the popular press since 1983. He started writing about identity theft in 1988. He has won numerous national journalism awards, including the Jesse H. Neal National Business Journalism Award two years in a row for his "Machine shop" series in CSO magazine. Today he mostly writes for Technology Review Magazine and the technologyreview.com website. As an entrepreneur, Garfinkel founded five companies between 1989 and 2000. Two of the most successful were Vineyard.NET, which provided Internet service on Martha's Vineyard to more than a thousand customers from 1995 through 2005, and Sandstorm Enterprises, an early developer of commercial computer forensic tools. Garfinkel received three Bachelor of Science degrees from MIT in 1987, a Master's of Science in Journalism from Columbia University in 1988, and a Ph.D. in Computer Science from MIT in 2005.
Views: 953 Harvard's CRCS
Closing the Loop (Full Film) - English with Multi-Language Subtitles
"Unless we go to Circular it's game over for the planet. It's game over for society." Closing The Loop is the world's first feature length documentary on the zero-waste / circular economy, supporting UN Sustainable Development Goal 12 on Responsible Production and Consumption. The film is presented by global sustainability expert Prof. Dr. Wayne Visser, in collaboration with Emmy and two time Telly Award winning director Graham Ehlers Sheldon. The film ranges across three continents and includes commentary from global experts and centres of excellence like the World Economic Forum and the University of Cambridge. A number of innovative circular economy cases are also featured in detail. The Circular Economy Club (CEC) is a communication and promotion partner of Closing the Loop. A film by Kaleidoscope Futures Lab. and Stand Up 8 Productions.
Views: 2520 Closing the Loop Film
In How Many Ways Can an Algorithm be Fair? - Suchana Seth
Recent research in machine learning has thrown up some interesting measures of algorithmic fairness – the different ways that a predictive algorithm can be fair in its outcome. In this talk, Suchana Seth will explore what these measures of fairness imply for technology policy and regulation, and where challenges in implementing them lie. The goal is to use these definitions of fairness to hold predictive algorithms accountable. Suchana Seth Suchana is a physicist-turned-data scientist from India, and the Mozilla Open Web Fellow at Data & Society Research Institute. She has built scalable data science solutions for startups and industry research labs, and holds patents in text mining and natural language processing. Suchana believes in the power of data to drive positive change; she volunteers with DataKind, mentors data-for-good projects, and advises research on IoT ethics. She is also passionate about closing the gender gap in data science, and leads data science workshops with organizations like Women Who Code. At Data & Society, Suchana is studying ways to operationalize ethical machine learning and AI in the industry. Her interests include fairness, accountability and transparency in machine learning, monetizing AI ethically, security vulnerabilities specific to machine learning and AI systems, and the regulatory landscape for predictive algorithms. #TuringSeminars #aiattheturing
Database Lesson #8 of 8 - Big Data, Data Warehouses, and Business Intelligence Systems
Dr. Soper gives a lecture on big data, data warehouses, and business intelligence systems. Topics covered include big data, the NoSQL movement, structured storage, the MapReduce process, the Apache Cassandra data model, data warehouse concepts, multidimensional databases, business intelligence (BI) concepts, and data mining,
Views: 82238 Dr. Daniel Soper
Get the Net Present Value of a Project Calculation - Finance in Excel - NPV()
Premium Course: https://www.teachexcel.com/premium-courses/68/idiot-proof-forms-in-excel?src=youtube Excel Forum: https://www.teachexcel.com/talk/microsoft-office?src=yt Excel Tutorials: https://www.teachexcel.com/src=yt This tutorial shows you how to get the Net Present Value of a project or business venture in the future using excel. You can do this very easily in excel spreadsheets and this will teach you how to do that using the estimated cash flows of a project. The NPV() function is used for the calculations. This is also a basic discounted cash flows example. This includes discount rate and number of periods in order to use the npv function. To follow along with the spreadsheet used in the video and also to get free excel macros, tips, and more video tutorials, go to the site: http://www.TeachMsOffice.com
Views: 275942 TeachExcel
1/2: Karianne Bergen: Big data for small earthquakes
Part 1 of 2: Dr. Karianne Bergen, Harvard Data Science Initiative Fellow at Harvard U., presents "Big data for small earthquakes: a data mining approach to large-scale earthquake detection" at the MIT Earth Resources Laboratory on September 28, 2018. "Earthquake detection, the problem of extracting weak earthquake signals from continuous waveform data recorded by sensors in a seismic network, is a critical and challenging task in seismology. New algorithmic advances in “big data” and artificial intelligence have created opportunities to advance the state-of-the-art in earthquake detection algorithms. In this talk, I will present Fingerprint and Similarity Thresholding (FAST; Yoon et al, 2015), a data mining approach to large-scale earthquake detection, inspired by technology for rapid audio identification. FAST leverages locality sensitive hashing (LSH), a technique for efficiently identifying similar items in large data sets, to detect new candidate earthquakes without template waveforms ("training data"). I will present recent algorithmic extensions to FAST that enable detection over a seismic network and limit false detections due to local correlated noise (Bergen & Beroza, 2018). Using the foreshock sequence prior to the 2014 Mw 8.2 Iquique earthquake as a test case, we demonstrate that our approach is sensitive and maintains a low false detections rate, identifying five times as many events as the local seismicity catalog with a false discovery rate of less than 1%. We show that our new optimized FAST software is capable of discovering new events with unknown sources in 10 years of continuous data (Rong et al, 2018). I will end the talk with recommendations, based on our experience developing the FAST detector, for how the solid Earth geoscience community can leverage machine learning and data mining to enable data-driven discovery. "
NDSS 2019 How Bad Can It Git? Characterizing Secret Leakage in Public GitHub Repositories
SESSION 4B-3 How Bad Can It Git? Characterizing Secret Leakage in Public GitHub Repositories GitHub and similar platforms have made public collaborative development of software commonplace. However, a problem arises when this public code must manage authentication secrets, such as API keys or cryptographic secrets. These secrets must be kept private for security, yet common development practices like adding these secrets to code make accidental leakage frequent. In this paper, we present the first large-scale and longitudinal analysis of secret leakage on GitHub. We examine billions of files collected using two complementary approaches: a nearly six-month scan of real-time public GitHub commits and a public snapshot covering 13% of open-source repositories. We focus on private key files and 11 high-impact platforms with distinctive API key formats. This focus allows us to develop conservative detection techniques that we manually and automatically evaluate to ensure accurate results. We find that not only is secret leakage pervasive — affecting over 100,000 repositories— but that thousands of new, unique secrets are leaked every day. We also use our data to explore possible root causes of leakage and to evaluate potential mitigation strategies. This work shows that secret leakage on public repository platforms is rampant and far from a solved problem, placing developers and services at persistent risk of compromise and abuse. PAPER https://www.ndss-symposium.org/wp-content/uploads/2019/02/ndss2019_04B-3_Meli_paper.pdf SLIDES AUTHORS Michael Meli (North Carolina State University) Matthew R. McNiece (Cisco Systems and North Carolina State University) Bradley Reaves (North Carolina State University) Network and Distributed System Security (NDSS) Symposium 2019, 24-27 February 2019, Catamaran Resort Hotel & Spa in San Diego, California. https://www.ndss-symposium.org/ndss-program/ndss-symposium-2019-program/ ABOUT NDSS The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies. https://www.ndss-symposium.org/ #NDSS #NDSS19 #NDSS2019 #InternetSecurity
Views: 95 NDSS Symposium
Utopian Global Business Presentation With CEO William Rowell And STORH CEO Ryan Messer
Utopian Global business presentation with CEO William Rowell and STORH CEO Ryan Messer Buy STORH Tokens here: https://utopianglobal.com?ref=start STORH Whitepaper: https://utopianglobal.com/static/assets/docs/STORH_WP1.0_15Jan19.pdf This is your personal invitation and an opportunity to take a stake in a brand new energy and resource holding company. We are in a unique position. With an ever growing population requiring endless amounts of energy, the existing world supply is decreasing. Therefore demand for energy will only increase. And STORH™ will be a player in providing part of the solution. And now you can profit from this. This is a company that will provide you with an ownership in the following cross sector projects; ✓ An oil field in Texas USA, producing out of 90 wells and has 20+ years of known reserves ✓ Midstream blending and transport opportunities with huge potential ✓ A water reclamation project ✓ A remediation project in Peru encompassing over 10 000 square miles with great returns ✓ Patented sustainable technologies which will tap into a huge growing market What might one expect from early adapting in STORH™? ✓ A token value of anywhere between 16x to 40x for early adaptors ✓ An average yearly return of 20% and upwards, year on year ✓ Paying out for the next 20 - 25 years! Get your STORH tokens with a 75 % discount; LINK This offer will expire around the 25th February 2019 after which the offer will be reduced back to a discount of 25% for the second round. What Is STORH Token? STORH ™ is a token that is backed 100% by real world energy assets. Tokenization is a revolutionary new process, where real-world assets become digital tokens on the blockchain. By switching to a digital system, early adaptors can liquefy real-world assets while retaining the characteristics of the asset. How To Buy STORH Token? STORH tokens can be purchased directly through the website of a member from as little as €50. you may choose to become an affiliate of Utopian Global and purchase STORH™ tokens at a deeper discount and in addition you can also earn free STORH™ tokens through the recommendation of the product to others. Register here: https://utopianglobal.com?ref=start What can we expect the STORH token price to be in the future? Once STORH™ is offered through a public ETO (open market, which is similar to an IPO) the expected rewards are: Starting at €1 in Q4 2019, and moving through to €4 (Q1 2020) and €10 (ultimate target). It is expected that quarterly rewards payments to commence at the end of April 2019 and will average around 20% per annum. Once €32 million is raised the listing price will be €1. For early adaptors, that will represent a 400% ROI. The introduction of the Peru project will dwarf all the other assets under STORH™ and because of this, we expect to see a quadrupling of the token price. The goal is of the executive team is to have the net assets of the company valued at €1 billion within a rather short period of time. The targeted coin price at this stage is €10 per token. These are estimates based solely on the value of the energy assets that will be owned by STORH™, which can all be found in the official white paper. Each project STORH™ undertakes will also have a remediation project that seeks to leave the environment on par or better than it was found. Private placements are generally only ever available to certain groups - big institutional money. Institutions that include banks, pension funds, insurance companies, VC firms and wealthy individuals, but never the retail market (the common people). You are in a very unique position right now to benefit from this private placement. STORH Team (Whitepaper) “Our team, including the Board and Advisors, carries over 250 years of international resource experience including sustainable technology, fossil fuel development, mineral extraction and many other associated projects.” “The operational experience of the CEO and founder Ryan Messer includes: • Developed and divested of 52,000 acres/80 square miles of a 3 dimensional seismic data survey • Operation of 3000-acre coalbed methane project with 3rd party natural gas gathering system including the development of over twenty wells • Built, operated and divested a drilling rig service company with multiple land rigs • Operated and drilled in Catahoula Lake, Louisiana utilizing floating barge rig, within the Wildlife & Fisheries Refuge • Executed sale of several thousand-acre resource play in the Cotton Valley for over $10k/acre • Pioneered energy development in North Bayou Jack Field, 21,000-foot horizontal completions in the Austin Chalk, now being developed by Blackbrush & EOG Resources • Exploitation of pre-XTO, Hunt minerals with over 140 wells”
Views: 133 Crypto Mining
Clinical Data Disclosure - The Five P's (Full)
90 second summary video: https://youtu.be/Dl08xpfOVf0 Links to related materials: http://ands.org.au/presentations/index.html#15-09-16 CC-BY-NC This presentation would be of particular interest to: -- researchers: publishing articles based on clinical research data -- support staff: managing health and clinical data This presentation will look at the practical considerations, for researchers, of publishing articles about clinical data, and preparing clinical data for sharing and publication. Iain Hrynaszkiewicz is Head of Data and HSS Publishing at Nature Publishing Group and Palgrave Macmillan, where his responsibilities include developing new areas of open research publishing and data policy. He is publisher of Scientific Data and helps develop open access monograph publishing. Iain previously worked at Faculty of 1000 and BioMed Central as an Editor and Publisher, of multidisciplinary life science journals and evidence-based medicine journals, and the Current Controlled Trials clinical trial registry. He has led various initiatives, and published several articles, related to data sharing, open access, open data and the role of publishers in reproducible research. Research funders, regulators, legislators, academics and the pharmaceutical industry are working to increase transparency of clinical research data while protecting research participant privacy. Journals and publishers are also involved and some have been strengthening their policies on researchers providing access to the data supporting published results, and providing new ways to publish and link to data online. Scientific Data (Nature Publishing Group), which publishes descriptions of scientifically valuable datasets, in July 2015 launched a public consultation and published draft guidelines on linking journal articles and peer review more robustly and consistently with clinical data that are only available on request. -- Editorial: http://www.nature.com/articles/sdata201534 -- Guidance for publishing descriptions of non-public clinical datasets: http://biorxiv.org/content/early/2015/06/30/021667 More information: -- ANDS Sensitive Data Guide: http://ands.org.au/datamanagement/sensitivedata.html -- ANDS youtube playlist for Sensitive Data and Ethics: https://www.youtube.com/playlist?list=PLG25fMbdLRa5pvodHMYDi3c0LTu8N3Ks- (includes a 1min video on the benefits of publishing sensitive data)
Jayant Bhandari: Emerging Markets Are in Huge Trouble?
Jason Burack of Wall St for Main St interviewed first time guest, resource stock investor, Jayant Bhandari http://jayantbhandari.com/. Jayant is constantly traveling the world to look for investment opportunities, particularly in the natural resource sector and he writes articles about culture, economics, investing and junior mining. He is a follower of the Austrian School of Economics and he runs a yearly seminar in Vancouver titled: Capitalism & Morality. Full Bio here: http://jayantbhandari.com/about/ Jayant has written many articles about India's demonetization scheme over the last year and his newest articles include pieces on Emerging Markets http://www.acting-man.com/?p=51179 and Japan http://www.acting-man.com/?p=51880 During this 40+ minute interview, Jason starts off the interview by asking Jayant his opinion on emerging markets since he travels often to emerging market countries including India. Jayant thinks that besides China, emerging markets are extremely corrupt, extremely inefficient 3rd world countries that are in huge trouble. Jason also asks Jayant about more Indian bank bailouts, India's demonetization scheme, corruption in India, socialism in India starting with Gandhi and about India's new attempt to transition to a faster digital economy with Aandar. Jason then asks Jayant whether he thinks China or India is in better shape long term? Jason also asks Jayant about his travels to Japan. Jayant has a contrarian view on Japan and he thinks Japan is in much better shape long term than most people in the financial industry. To wrap up the interview, Jason asks Jayant about whether Bitcoin and other crypto currency have taken away a lot of capital from junior miners and explorers in Vancouver and how Jayant has made some profitable stock trades of 60% in only 6 months? Please visit the Wall St for Main St website here: http://www.wallstformainst.com/ Follow Jason Burack on Twitter @JasonEBurack Follow Wall St for Main St on Twitter @WallStforMainSt Commit to tipping us monthly for our hard work creating high level, thought proving content about investing and the economy https://www.patreon.com/wallstformainst Also, please take 5 minutes to leave us a good iTunes review here! We have 33 5 star iTunes reviews and we need to get to our goal of 100 5 star iTunes reviews asap! https://itunes.apple.com/us/podcast/wall-street-for-main-street/id506204437 If you feel like donating fiat via Paypal, Bitcoin, Gold Money, or mailing us some physical gold or silver, Wall St for Main St accepts one time donations on our main website. Wall St for Main St is also available for personalized investor education and consulting! Please email us to learn more about it! If you want to reach us, please email us at: [email protected]
Views: 6816 WallStForMainSt