Search results “Data mining is simply filtering through large amounts of raw”
Understanding Wavelets, Part 1: What Are Wavelets
This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts can be applied to 2D data such as images. Video Transcript: Hello, everyone. In this introductory session, I will cover some basic wavelet concepts. I will be primarily using a 1-D example, but the same concepts can be applied to images, as well. First, let's review what a wavelet is. Real world data or signals frequently exhibit slowly changing trends or oscillations punctuated with transients. On the other hand, images have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data, both perceptually and in terms of the information they provide. The Fourier transform is a powerful tool for data analysis. However, it does not represent abrupt changes efficiently. The reason for this is that the Fourier transform represents data as sum of sine waves, which are not localized in time or space. These sine waves oscillate forever. Therefore, to accurately analyze signals and images that have abrupt changes, we need to use a new class of functions that are well localized in time and frequency: This brings us to the topic of Wavelets. A wavelet is a rapidly decaying, wave-like oscillation that has zero mean. Unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. Wavelets come in different sizes and shapes. Here are some of the well-known ones. The availability of a wide range of wavelets is a key strength of wavelet analysis. To choose the right wavelet, you'll need to consider the application you'll use it for. We will discuss this in more detail in a subsequent session. For now, let's focus on two important wavelet transform concepts: scaling and shifting. Let' start with scaling. Say you have a signal PSI(t). Scaling refers to the process of stretching or shrinking the signal in time, which can be expressed using this equation [on screen]. S is the scaling factor, which is a positive value and corresponds to how much a signal is scaled in time. The scale factor is inversely proportional to frequency. For example, scaling a sine wave by 2 results in reducing its original frequency by half or by an octave. For a wavelet, there is a reciprocal relationship between scale and frequency with a constant of proportionality. This constant of proportionality is called the "center frequency" of the wavelet. This is because, unlike the sinewave, the wavelet has a band pass characteristic in the frequency domain. Mathematically, the equivalent frequency is defined using this equation [on screen], where Cf is center frequency of the wavelet, s is the wavelet scale, and delta t is the sampling interval. Therefore when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. For instance, here is how a sym4 wavelet with center frequency 0.71 Hz corresponds to a sine wave of same frequency. A larger scale factor results in a stretched wavelet, which corresponds to a lower frequency. A smaller scale factor results in a shrunken wavelet, which corresponds to a high frequency. A stretched wavelet helps in capturing the slowly varying changes in a signal while a compressed wavelet helps in capturing abrupt changes. You can construct different scales that inversely correspond the equivalent frequencies, as mentioned earlier. Next, we'll discuss shifting. Shifting a wavelet simply means delaying or advancing the onset of the wavelet along the length of the signal. A shifted wavelet represented using this notation [on screen] means that the wavelet is shifted and centered at k. We need to shift the wavelet to align with the feature we are looking for in a signal.The two major transforms in wavelet analysis are Continuous and Discrete Wavelet Transforms. These transforms differ based on how the wavelets are scaled and shifted. More on this in the next session. But for now, you've got the basic concepts behind wavelets.
Views: 129216 MATLAB
Analyzing Big Data in less time with Google BigQuery
Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery
Views: 49407 Google Cloud Platform
Interview with a Data Analyst
This video is part of the Udacity course "Intro to Programming". Watch the full course at https://www.udacity.com/course/ud000
Views: 269319 Udacity
The Logic of Data Mining in Social Research
This video is a brief introduction for undergraduates to the logic (not the nitty-gritty details) of data mining in social science research. Four orienting tips for getting started and placing data mining in the broader context of social research are included.
Views: 273 James Cook
Working with Datasets and Discovering Data
See what's new in our latest version - http://www.talend.com/products Within this video, we’ll dive deeper into how to work with our Datasets and how to search, browse and visualize your data. A dataset is simply a local or remote file that I can import into the Talend Data Preparation Tool, and potentially any other data sources such as database tables or cloud applications although not in the context of the Free Desktop version. After which I can then create a new “recipe” of functions to adjust my data to be presented to me in a new way. Think of it as implementing a filtering lens to view the data. I can then export my new recipe as a new “preparation”, leaving my original data unchanged.
Views: 976 Talend
How to use GETPIVOTDATA in Excel 2016: Pivot Tables Excel 2016
Right, let’s summarize what we have seen so far in this exercise. We started working with a raw SAP extraction in Excel. We carried out a mapping exercise and obtained two new columns – one containing months and another one showing years. Then, we were able to build a structure based on the data we had in our extraction and on what we wanted to show in the report we are building. After that, we formatted the output sheet nicely and added the necessary formulas that will calculate subtotal, total, absolute and percentage variances, etc. Finally, we inserted a Pivot Table that will allow us to extract the necessary numbers. The next two lessons will make working with Pivot Tables so much more valuable for you! Our goal here is to extract the data from the Pivot Table and populate the cells of the output sheet. Let’s start by filling the first cell in the report – Volume for 2015. I’ll link to one of the cells of the Pivot Table that we already have. We already know how GetPivotData works. The first argument shows the field for which we are extracting data. In this case, we are extracting data for Volumes. The second argument, which in this case is the cell A3, is the first cell of the Pivot Table to which we are linking. And then, all of the other arguments of the function give us directions to which cell exactly in the Pivot Table we are linking. Let’s take a look at the formula while we are in the sheet containing our Pivot Table. We have “Month” equal to 1, meaning we are extracting volume data for January. The year is 2015 and the brand is Buratino. You can see how easy it is to understand this formula. Each of the names has an easy to understand meaning. Sometimes, we can even work with this function without looking at the source sheet. And this is simply great, as it reduces the likelihood of mistakes significantly. But, that’s not everything. We can substitute each of these hard inputs with a reference. And this is when GetPivotData becomes awesome! For example, I will type 2015 in this row here and will then replace the hard input with a reference to this cell and everything will remain as it was, before we made the replacement. This is how we can make the function flexible and re-use it for the rows and columns in the entire table. I will simply type 2016 on the right. Copy and paste the function to the right. And this will provide us the volume in 2016. Very well. But wait, these are not the volumes of all the firms we want to show here, but just the ones for the Buratino brand. If we erase this criterion, we will stop specifying we are looking for a particular brand and Excel will provide us with the sales volume of the entire company. And in the same way, we need to eliminate the “month” field, as right now, the formula extracts volumes only for January. I’ll erase these two parameters of the function. Ok. So this is the volume of sales of the entire firm in 2015. As we have already specified the numbers are in thousands, let’s divide this figure by 1000. It will look much neater.  All right. The question is: are we going to be able to paste this function in all blank cells that have to be filled in this report? The answer is that we are not ready to do that … yet. I’ll need to fix the row reference of the “Year” item, which will prevent it from changing its location when pasting the function downwards. The other thing that needs to be taken care of is the fact we still have one hard input at the beginning of the function – its first argument, which reads “Sum of Volume”. We need to find a way to allow us to change this argument for each of the rows we have below. So, for example, when we paste the function in the row below, the first argument will have to be “Sum of Gross Sales”. …because this is how it is named in the Pivot Table from which we are extracting data. So, why don’t we substitute this bit with an “&” function? I can separate the first part with parentheses, which indicate this is text and then add the & function and link to the cell B6. ………..…And here’s the proof that this approach functions. The first argument of the function will change dynamically when we paste it downwards and to the right. However, we forgot to add a dollar sign in front of the column reference of this argument. Without the dollar sign in front of it, it will change its column position when pasting to the right. I’ll add the dollar sign now. All right, perfect. Now we can paste the function for all blank cells in our report. GetPivotData is truly great, isn’t it?
Views: 15644 365 Careers
SAS® Text Analytics Software Demo
http://www.sas.com/en_us/software/analytics/text-miner.html SAS Text Analytics help companies address big data issues that arise from unstructured content by applying linguistic rules and statistical methods. SAS TEXT MINER Get faster, deeper insight from unstructured data. Why limit yourself to analyzing legacy data? Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. Discover new information, topics and term relationships that deepen your understanding. And add what you learn to your models to improve lift and performance. Benefits: * Improve model performance. * Add subject-matter expertise. * Automatically know more. * Determine what's hot and what's not. LEARN MORE ABOUT SAS TEXT MINER http://www.sas.com/en_us/software/analytics/text-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss To learn more about SAS Text Analytics, visit http://www.sas.com/textanalytics
Views: 23324 SAS Software
OSI Model (OSI Reference Model) : The 7 Layers Explained
Enroll to Full Course: https://goo.gl/liK0Oq The "OSI Model" also known as "OSI Reference Model" is discussed here in 2 parts: a) Understanding OSI Reference Model b) OSI Model layers a) Understanding OSI Model (00:22): http://youtu.be/p7UR7Nipqcs?t=22s The OSI reference model is one such communication model. OSI stands for "Open Systems Interconnection" which means that every system participating in this model is open for communication with other systems. This model was first defined by an organization called as ISO. The OSI model divides the communication into 7 layers. b) OSI Model layers (2:15) : http://youtu.be/p7UR7Nipqcs?t=2m15s Quick Look of the 7 layers of the OSI reference Model: 7) Application Layer is where the users interact with applications to provide data 6) Presentation Layer is concerned with the format of data exchanged between the end systems 5) Session Layer allows users on different machines to create sessions between them 4) Transport Layer is concerned with end to end communication of messages 3) Network Layer is concerned with routing of packets to correct destination 2) Data Link Layer is concerned with transmission of error free data in the form of Frames 1) Physical Layer is concerned about transmission of raw bits over the communication link Search Terms: OSI Model, OSI layers, OSI Model Layers, OSI 7 Layers, Network Layer, 7 Layers of OSI model, OSI network Model, what is osi model, OSI system, OSI Reference Model, ISO OSI Model, OSI Model layers, video URL : https://www.youtube.com/watch?v=p7UR7Nipqcs Watch ALL CN VIDEOS: https://www.youtube.com/playlist?list=PL9OIoIp8YySF4mkIihOb_j2HZIRIlYuEx For more, visit http://www.udemy.com/u/EngineeringMentor Facebook: https://www.facebook.com/SkillGurukul Twitter : https://twitter.com/Engi_Mentor
Views: 565443 Skill Gurukul
Life is easy. Why do we make it so hard? | Jon Jandai | TEDxDoiSuthep
Never miss a talk! SUBSCRIBE to the TEDx channel: http://bit.ly/1FAg8hB Jon is a farmer from northeastern Thailand. He founded the Pun Pun Center for Self-reliance, an organic farm outside Chiang Mai, with his wife Peggy Reents in 2003. Pun Pun doubles as a center for sustainable living and seed production, aiming to bring indigenous and rare seeds back into use. It regularly hosts training on simple techniques to live more sustainably. Outside of Pun Pun, Jon is a leader in bringing the natural building movement to Thailand, appearing as a spokesperson on dozens of publications and TV programs for the past 10 years. He continually strives to find easier ways for people to fulfill their basic needs. For more information visit http://www.punpunthailand.org About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 7589945 TEDx Talks
What is AIR FLOW METER? What does AIR FLOW METER mean? AIR FLOW METER meaning & explanation
What is AIR FLOW METER? What does AIR FLOW METER mean? AIR FLOW METER meaning - AIR FLOW METER definition - AIR FLOW METER explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ An air flow meter, is a device that measures air flow, i.e. how much air is flowing through a tube. It does not measure the volume of the air passing through the tube, it measures the mass of air flowing through the device per unit time. Thus air flow meters are simply an application of mass flow meters for a special medium. Typically, mass air flow measurements are expressed in the units of kilograms per second (kg/s). An air flow meter is used in some automobiles to measure the quantity of air going into the internal combustion engine. All modern electronically controlled Diesel Engines use an air flow meter, as it is the only possible means of determining the air intake for them. In the case of a petrol engine, the electronic control unit (ECU) then calculates how much fuel is needed to inject into the cylinder ports. In the diesel engine, the ECU meters the fuel through the injectors into the engines cylinders during the compression stroke. Air flow meter is a special sensor which as been modified now by MEMS technology. The vane (flap) type air flow meters (Bosch L-Jetronic and early Motronic EFI systems or Hitachi) actually measure air volume, whereas the later "hot wire" and "hot film" air mass meters measure the mass of air flow. The flap type meter includes a spring which returns the internal flap to the initial position. Sometimes if the spring is tensioned too tightly, it can restrict the incoming air and it would cause the intake air speed to increase when not opened fully. Differential pressure is also used for air flow measurement purposes. Air flow meters may fail or wear out. When this happens, engine performance will often decrease significantly, engine emissions will be greatly increased, and usually the Malfunction Indicator Lamp (MIL) will illuminate. In most countries in Europe, and in places in the United States where emissions inspections are obligatory, a lit MIL is cause for a vehicle to fail the inspection. Some engines do not idle with an air flow meter failure. In the development process of internal combustion engines with engine test stands, an air flow meter/air flow measuring unit is used for measuring the continuous gravimetric air consumption of combustion engines. Air flow meters monitor air (compressed, forced, or ambient) in many manufacturing processes. In many industries, preheated air (called "combustion air") is added to boiler fuel just before fuel ignition to ensure the proper ratio of fuel to air for an efficient flame. Pharmaceutical factories and coal pulverizers use forced air as a means to force particle movement or ensure a dry atmosphere. Air flow is also monitored in mining and nuclear environments to ensure the safety of people.
Views: 129 The Audiopedia
Excel Removing Outliers from Pivot Table Data (Finding Quartiles, IQR, and Outliers)
Removing Outliers from pivot table data can be a bit tricky, but I've made a step by step video of how to identify and filter outliers from a pivot tables source data. I'll go through and example looking at production data where I would like to summarize by average values, but because of outliers, the data is skewed. To eliminate this problem, I show you how to find each quartile (Q1, Q2 - Median, and Q3), calculate the IQR, and then determine outliers. Welcome to The Engineer Toolbox Channel, where I give you the tools you need to solve real world engineering problems. Channel Link: https://www.youtube.com/channel/UCX-H7IOnCfHihvtnypTORMA Check out my Channel for more videos and tutorials for engineers! Like, follow, Share and don't be afraid to drop me a comment/message with feedback, questions, or video suggestions! Thanks for watching!
How to take advantage of scale out graph in Azure Cosmos DB  : Build 2018
Real-world data is naturally connected. Learn how to create graph database applications on Azure Cosmos DB and explore the different solutions that it provides to common data scenarios in the enterprise. We will also cover customer cases that currently leverage graph databases in their day-to-day workloads. Create a Free Account (Azure): https://aka.ms/azft-cosmos
Views: 906 Microsoft Developer
Bioinformatics and Biospecimen Workshop 2013 - Methylation Data by Dr. Bodour Salhia
A brief introduction to epigenetics and DNA methylation, followed by a detailed description of how to determine if specific genes are differentially methylated in biospecimen samples. Instructions are provided to learn how to download large datasets from the TCGA web portal.
Views: 2512 nmsuaces
Data Wrangling, Normalization & Preprocessing: Part I Signals
Dr. Joseph Picone from Temple University presents a lecture on "Data Wrangling, Normalization & Preprocessing: Part I Signals" Lecture Abstract Data wrangling is defined as the process of mapping data from an unstructured format to another format that enables automated processing. State of the art deep learning systems require vast amounts of annotated data to achieve high performance, and hence, this is often referred to as a Big Data problem. Many decision support systems in healthcare can be successfully automated if such big data resources existed. Therefore, automated data wrangling is crucial to the application of deep learning to healthcare. In this talk, we will discuss data wrangling challenges for physiological signals commonly found in healthcare, such as electroencephalography (EEG) signals. For signal and image data to be useful in the development of machine learning systems, identification and localization of events in time and/or space plays an important role. Normalization of data with respect to annotation standards, recording environments, equipment manufacturers and even standards for clinical practice, must be accomplished for technology to be clinically relevant. We will specifically discuss our experiences in the development of a large clinical corpus of EEG data, the annotation of key events for which there is low inter-rater agreement (such as seizures), and the development of technology that can mitigate the variability found in such clinical data resources. In a companion talk to be given on December 2, data wrangling of unstructured text, such as that found in electronic medical records, will be discussed. View slides from this lecture https://drive.google.com/open?id=0B4IAKVDZz_JUekljOVVRa3RaNmc About the speaker Joseph Picone is currently a professor in the Department of Electrical and Computer Engineering at Temple University. He received his Ph.D. in Electrical Engineering in 1983 from the Illinois Institute of Technology. His primary research interests are machine learning approaches to acoustic modeling in speech recognition. Recently, he has been focusing on the commercialization of technology to automatically interpret EEGs. He has spent significant portions of his career in academia, research and the government, giving him a very balanced perspective on management of R&D. Dr. Picone is a Senior Member of the IEEE and has been active in several professional societies related to human language technology. He has authored numerous papers on the subject and holds several patents in this field. See www.isip.piconepress.com and www.nedcdata.org to learn more about his research and teaching. Joseph Picone is a co-PI on an NIH BD2K grant titled “Automatic discovery and processing of EEG cohorts from clinical records” which is a collaboration between Temple University and the University of Texas at Dallas. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
Kibana 5 Introduction
[ You can find a visual transcript of this video on my blog: https://www.timroes.de/2016/10/23/kibana5-introduction/ ] In this video we'll cover all the basics you need to get started with Kibana 5 and kickstart into visualizing and analyzing your data. More Kibana tutorials can be found on https://www.timroes.de All Kibana tutorials are available in the Kibana Tutorials playlist: https://www.youtube.com/playlist?list=PLWOeloPQaz1C91x7ioqFO8SnaV7xNFvjo The mentioned post about the detailed explanation of queries can be found at: https://www.timroes.de/2016/05/29/elasticsearch-kibana-queries-in-depth-tutorial/ Some tutorials about timelion: - Short introduction: https://www.youtube.com/watch?v=-sgZdW5k7eQ - More detailed talk from DevoxxFR: https://www.youtube.com/watch?v=L5LvP_Cj0A0
Views: 130154 Tim Roes
Importing Data and Working With Data in R (R Tutorial 1.6)
Learn how to import a dataset into R and begin to work with data. You will learn the "read.table", "header", "sep", "file.choose", "dim", "head", "tail", "as.factor", "attach", "detach", "levels", and [] commands. This video is a tutorial for programming in R Statistical Software for beginners. You can access and download the "LungCapData" dataset from our website: http://www.statslectures.com/index.php/r-stats-datasets or here: Excel format: https://bit.ly/LungCapDataxls Tab Delimited Text File: https://bit.ly/LungCapData Here is a quick overview of the topics addressed in this video; You can click on the time stamp to jump to the specific topic. 0:00:07 how to read a dataset into R using "read.table" command and save it as an object 0:00:27 how to access the help menu in R 0:01:02 how to let R know that the first row of our data is headers by using "header" argument 0:01:14 how to let R know how the observations are separated by using "sep" argument 0:02:03 how to specify the path to the file using "file.choose" argument on the "read.table" command 0:03:15 how to use Menu options to import data into R when working with RStudio 0:05:23 how to use Excel to prepare the data for using in R 0:06:15 how to know the dimensions (the number of rows and columns) of the data in R using the "dim" command 0:06:35 how to see the first six rows of the data in R using the "head" command 0:06:45 how to see the last six rows of the data in R using the "tail" command 0:07:18 how to check if the data was read correctly into R using square brackets and subsetting data 0:08:21 how to check the variable names in R using the "names" command http://statslectures.com/index.php
Views: 328372 MarinStatsLectures
4. How To Identify Stock Market Direction (Trends) Part 1
Want to learn how to gauge the future price of your stock? Part 2: http://www.youtube.com/watch?v=0wL0McpX-l0 Visit: http://marketscientist.in ----------------------------------------­--------------- Learn To Trade - How MarketScientist works: http://marketscientist.in/how-marketscientist-works-faqs/ MarketScientist Courses: http://marketscientist.in/courses/ Follow Prateek's Trading day @ mentor posts : http://marketscientist.in/category/prateek-singh-s-analysistrades-resident-marketscientist/ ----------------------------------------­-------------------------- Transcript market direction is actually referred to in the technical world as "trends" So a stock moving upwards, is in an uptrend And a stock moving downwards is in a downtrend sometimes stocks reach in a no trade zone or a sideways and this happens because as soon as markets go up it forces a situation of supply and when markets fall down it forces a situation of demand coming in. This was seen in the earlier half of December 2012 on the nifty hourly charts. Lets move on, when we use concepts of supply and demand over long periods of time you must realise that psychology exists on all timeframes, Except of course in tick-charts; wherever you have good volume, markets will always behave in the same way if your concept is technically sound. So let's see how you can become your own amateur financial analyst, determining whether your stock that you are stuck in or making a profit, might continue to move up or might continue to move down. Si the first thing we are going to learn is about a rally and a decline A rally and decline are seen on a per bar basis, meaning we look at one bar and then the next. Simply put a rally is an upmove A Decline is simply a down move They together form something more important, which we will discuss later lets look at a rally first, So this is one bar this isn't enough information, the next bar breaks the previous bars high and this continues to happen Now you will notice that every bar is breaking the previous bars high and its also having a higher low. This means the market is in rally mode. Also remember in a real market situation this may not happen consecutively but a general move up is still considered a rally. A decline is just the opposite, and I'm sure intuitively u have understood what I'm about to draw here. So the market falling down each consecutive bar breaking the previous bars low and making a lower low every bar So that's very simple, here is another rally, which makes a new high and here is another decline. so now that we have that, you can see that we have formed a wave structure, markets will always move in waves, markets will never plunge down or move up unless it's an erratic day or days. Over general long periods of time, markets will always move in waves and this is very healthy. So now that we have understood a rally and decline let's move on to swing highs and a swing low. Simply put the meeting point of a rally an upmove and the immediate decline; this tent, mountain or this peak is called a swing high. the opposite of this is a swing low, meaning the meeting point of a decline and the immediate rally is a swing low. Now trends are made up of swing highs and lows, people call these by different names but all technicals follow this because a swing high is a naturally place of resistance, it basically means that the markets rallied hit a supply point, either buying diminished of too much selling happened and we fell, now the longer time frame between a swing high is untouched the more important it becomes. At MarketScientist we follow trend following methods/systems, so awhat we discuss in this video and the next is extremely important, if you don't understand please rewind or you can ask questions by emailing us or writing it in the comments below. Here is a real example of a chart, this chart belongs to nifty and it is basically in downtrend, but what we have to look now is the swing highs and swing lows. I want you to take am moment and try to find the latest swing highs u can see here I'm helping you a bit and marking all of the swing highs on this chart. I've marked them with green circles. Next step is to identify swing lows, now before we proceed I want you to pause and take your time and look at the swing highs and know that you have understood this. We are basically looking for peaks (swing highs) and crests (swing lows). I'm marking the first the swing lows for you and I want you to mark the resting your head or write it down somewhere. Pause this video and find out all the swing lows, we will meet in the next video with the answers.... I'll be waiting for you then.
Views: 394908 LearnApp
The Secrets Hidden In Plain Sight
**🎺 WELCOME TO AUTHENTIC INTENT 🎺** Until you realize how easily it is for your mind to be manipulated, you remain the puppet of someone else's game Original Video(s) can be found HERE https://www.youtube.com/user/Secretsinplainsight Midwest/Minnesota Meet Up information - https://youtu.be/CMFB1sD0d3Y Mark Sargent Promo ** https://youtu.be/aZL98SbersA ** Thank you for your continued support in ALL forms you provide 😎 🗣WE ARE THE RESISTANCE🗣 Like 👍 Dislike 👎 Share 🎁 ╔═╦╗╔╦╗╔═╦═╦╦╦╦╗╔═╗ ║╚╣║║║╚╣╚╣╔╣╔╣║╚╣═╣ ╠╗║╚╝║║╠╗║╚╣║║║║║═╣ ╚═╩══╩═╩═╩═╩╝╚╩═╩═╝  And HIT that 🔔 Contact me for questions, comments, concerns, compliments & suggestions here: jswifty1981 AT gmail.com IF YOU ENJOY WHAT I DO AND WOULD LIKE TO SUPPORT ME https://www.patreon.com/AuthenticIntent  https://www.paypal.me/AuthenticIntent  SUPPORT THE CHANNEL FOR NEW SIGNAGE, LOCATIONS AND HANDOUTS: https://www.gofundme.com/AuthenticIntentYTC Tales of Tomorrow - Age of Peril - Hypnosis and plausible deniability https://youtu.be/ZEKZqqGqOrE Tales of Tomorrow - Test Flight - Elon Musk? https://youtu.be/1CsKjHQPm9w The Plutonium Files - https://youtu.be/VkMNfRxKQLs Holograms you can TOUCH!!! -- https://youtu.be/_b-nigL3lV4 Sports Idolatry -- https://youtu.be/2MbkH8EOlQU NASA HUMILIATED - https://youtu.be/Mzq-ptYkDAE ThePottersClay - NASA Lies  https://youtu.be/ZT8ViN3sk5A Brian Mullin - How do planes work on a spinning ball?  https://youtu.be/aehel0lBDqQ TigerDan - 75 Bible Verses "proving" Flat Earth? https://youtu.be/JQAjfTeb4Y4 TETs Truth Tube - Fakeryland in Fakeville, USA https://youtu.be/DszumfCnF2g *OTHER TOPICS YOU NEED TO KNOW* 9/11 WAS AN INSIDE JOB, a False Flag and A HOAX: https://youtu.be/5QlWZQMYdfA https://youtu.be/BmukRD00KWs?list=PLj8_kFPwRPQCwV6HPRlkOWIELfvDYrKDD Sophia, AI and The mother goddess - https://youtu.be/YXxOgX05UpQ ELF Waves Explained https://youtu.be/rAeqvvBN55E Cell phone tower/ELF Waves -- https://youtu.be/rn1g2V9Oars John Todd Testimony -- https://youtu.be/WKXmKMWrLRE DO NOT VACCINATE YOURSELF OR YOUR FAMILY  Movie VAXXED now available on Amazon -- https://www.amazon.com/Vaxxed-Cover-Up-Catastrophe-Del-Bigtree/dp/B01LZ2L7B1/ref=sr_1_1?ie=UTF8&qid=1519086847&sr=8-1&keywords=vaxxed ** https://youtu.be/zncuOv9VBxw Agenda 2030 - Depopulation -- https://youtu.be/gsHdu5MpYCs ** https://youtu.be/Mz-KY26cpqw Witchcraft in YOUR media -- https://youtu.be/HRngRdjuEoQ MOON LANDING HOAX Wagging the Moondoggie https://youtu.be/JlaLG-3FZm0 ** A Funny Thing Happened... https://youtu.be/xciCJfbTvE4 Human Trafficking -- https://youtu.be/G2uaaAHh1XI Staged News Events -- https://youtu.be/eHDDQVZ-A98?list=PLj8_kFPwRPQDvW6q1DBG9kbF1QjMjAzbV Transhumanism -- https://youtu.be/byc_e6V3cfk?list=PLj8_kFPwRPQDvZvouyyZfOxxdpG2M12Yy The Phoenix Program/MK ULTRA MIND KONTROL 👉Must Watch Me👈 https://youtu.be/rGFPnD75Aaw Chemtrails - https://youtu.be/qg_-WM03X_w Don't forget to check out my "sponsor" (family business) I receive no consistent money, unless I ask my dad. 5555  http://www.gotlefse.com  A level plane Explanation in The Bible: http://www.ic.unicamp.br/~stolfi/misc/misc/FlatEarth/FlatEarthAndBible.html Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted. "Fair Use" guidelines: www.copyright.gov/fls/fl102.html THEY THAT BE WITH US ARE GREATER THAN THEY THAT BE WITH THEM
Views: 2816 Authentic Intent
Cleaning Data in Stata
This video provides a brief introduction to Stata commands used to annotate, subset, and browse a data set.
Views: 52776 Alan Neustadtl
Data Science Tutorial for Beginners - 1 | What is Data Science? | Data Analytics Tools | Edureka
( Data Science Training - https://www.edureka.co/data-science ) Data Science Blog Series: https://goo.gl/1CKTyN http://www.edureka.co/data-science Please write back to us at [email protected] or call us at +91-8880862004 for more information. Data Science is all about extracting knowledge from data. Data Science is the integration of methods from mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modelling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. This interdisciplinary and cross-functional field leads to decisions that move an organization forward in terms of proposed investment, decisions regarding a product or business strategy. Data Science is a buzzword, often used interchangeably with analytics or big data. At times, Analytics is synonymous with Data Science, but at times it represents something else. A Data Scientist using raw data to build a predictive behaviour model, falls in to the category of analytics. Data science is a steadily growing discipline that is driving significant changes across industries and in companies of every size. It is emerging as a critical source for insights for enterprises dealing with massive amounts of data. About the Data Science Course at edureka! - This Data Science course is designed to provide knowledge and skills to become a successful Data Scientist. The course covers a range of Hadoop, R and Machine Learning Techniques encompassing the complete Data Science study. Course Objectives After the completion of the Data science Course at Edureka, you should be able to: Gain an insight into the 'Roles' played by a Data Scientist. Analyse Big Data using Hadoop and R. Understand the Data Analysis Life Cycle. Use tools such as 'Sqoop' and 'Flume' for acquiring data in Hadoop Cluster. Acquire data with different file formats like JSON, XML, CSV and Binary. Learn tools and techniques for sampling and filtering data, and data transformation. Understand techniques of Natural Language Processing and Text Analysis. Statistically analyse and explore data using R. Create predictive using Hadoop Mappers and Reducers. Understand various Machine Learning Techniques and their implementation these using Apache Mahout. Gain insight into the visualisation and optimisation of data. Who should go for this course? This course is designed for all those who want to learn machine learning techniques and wish to apply these techniques on Big Data. The course is amalgamation of two powerful open source tools: 'R' language and Hadoop software framework. You will learn how to explore data quantitatively using tools like Sqoop and Flume, write Hadoop MapReduce Jobs, perform Text Analysis and implement Language Processing, learn Machine Learning techniques using Mahout, and optimize and visualise the results using programming language 'R' and Apache Mahout. This course is for you if you are: A SAS, SPSS Analytics Professional. A Hadoop Professional working on Database management and streaming of Big Data. An 'R' professional who wants to apply Statistical techniques on Big Data. A Statistician who wants to understand Data Science methodologies to implement the statistics methods and techniques on Big data. Any Business Analyst who is working on creating reports and dashboards. Pre-requisites Some of the prerequisites for learning Data Science are familiarity with Hadoop, Machine Learning and knowledge of R (recommended not mandatory as these concepts will also be covered during the course). Also, having a statistical background will be an added advantage. Why Learn Data Science? 'Data Science' is a term which came into popularity in past decade. Data Science is the process of extracting valuable insights from "data". It is the right time to learn Data science because: We are living in the Big Data Era, Data Science is becoming a very promising field to harness and process huge volumes of data generated from various sources. A data scientist has a dual role -- that of an "Analyst" as well as that of an "Artist"! Data scientists are very curious, who love large amount of data, and more than that, they love to play with such huge data to reach important inferences and spot trends. You could be one of them! As 'Data Science' is an emerging field, there is a plethora of opportunities available world across. Just browse through any of the job portals; you will be taken aback by the number of job openings available for Data scientists in different industries, whether it is IT or healthcare, Retail or Government offices or Academics, Life Sciences, Oceanography, etc. Read this blog post on Data Science to know more. http://www.edureka.co/blog/who-is-a-data-scientist/
Views: 200174 edureka!
Kaggle Camera Model Identification (1-2 places) — Artur Fattakhov, Ilya Kibardin, Dmitriy Abulkhanov
Artur Fattakhov, Ilya Kibardin and Dmitriy Abulkhanov share their winner’s solutions of Kaggle Camera Model Identification. In this competition, Kagglers challenged to build an algorithm that identifies which camera model captured an image by using traces intrinsically left in the image. From this video you will learn: - How to get additional photo data - Training Scheme with cyclic learning rate and pseudo labeling - Snapshot Ensembles aka Multi Checkpoint TTA - Training on small crops and finetune on big crops to speed up without loss in quality - Prediction equalization Slides: https://gh.mltrainings.ru/presentations/KibardinFattahovAbulkhanov_KaggleCamera_2018.pdf Github: https://github.com/ikibardin/kaggle-camera-model-identification Yandex hosts biweekly training sessions on machine learning. These meetings offer an opportunity for the participants of data analysis contests to meet, talk, and exchange experience. Each of these events is made up of a practical session and a report. The problems are taken from Kaggle and similar platforms. The reports are given by successful participants of recent contests, who share their strategies and talk about the techniques used by their competitors.
Hidden Aliens
It is often suggested that the reason we don't hear from alien civilizations is that they are in hiding. In this episode, we will explore that notion, and examine why a civilization might hide and how they would go about doing it. Visit our Website: http://www.isaacarthur.net Join the Facebook Group: https://www.facebook.com/groups/1583992725237264/ Support the Channel on Patreon: https://www.patreon.com/IsaacArthur Visit the sub-reddit: https://www.reddit.com/r/IsaacArthur/ Listen or Download the audio of this episode from Soundcloud: https://soundcloud.com/isaac-arthur-148927746/hidden-aliens Cover Art by Jakub Grygier: https://www.artstation.com/artist/jakub_grygier Graphics Team: Edward Nardella Jarred Eagley Justin Dixon Katie Byrne Misho Yordanov Murat Mamkegh Pierre Demet Sergio Botero Stefan Blandin Script Editing: Andy Popescu Connor Hogan Edward Nardella Eustratius Graham Gregory Leal Jefferson Eagley Luca de Rosa Michael Gusevsky Mitch Armstrong MolbOrg Naomi Kern Philip Baldock Sigmund Kopperud Steve Cardon Tiffany Penner Music: Martin Rezny, "Life Light" Markus Junnikkala, "Memory of Earth" Lombus, "Hydrogen Sonata" AJ Prasad, "Staring Through"
Views: 239746 Isaac Arthur
Uncovered: The War on Iraq • FULL DOCUMENTARY • BRAVE NEW FILMS (2004)
The George W. Bush administration intentionally deceived the American people in order to justify going to war in Iraq in 2003. SUBSCRIBE: http://bit.ly/1MlXvgG WATCH MORE: http://bit.ly/1OiiQ14 Using a wealth of different sources, Robert Greenwald's 2004 film investigates the motives and pretexts of the administration of former President George W. Bush for going to war in Iraq after 9/11. Host a FREE screening of ANY of our films: http://bravenewfilms.org/screenings Greenwald's film dissects the different catalysts leading to the war, from the media's treatment of the issue to the practice of "data mining." A panel of experts lend their voices to this examination, including CIA analysts, a former CIA director, previous ambassadors and a weapons inspector. Host a FREE screening of ANY of our films: http://bravenewfilms.org/screenings 3:33 Just one day after the September eleventh attacks, the Bush administration began its plans for the invasion of Iraq, claiming the country had possession of nuclear weapons; despite a complete lack of evidence. 7:11 The Bush Administration rounded up people who could create talking points for the invasion of Iraq. Those who raised questions against them were simply told to "sit down and shut up" 11:50 The Bush Administration edited and censored the official National Intelligence Estimate, modifying the meanings of revealing facts that would damage their facade. 14:15 Despite the claims that the war in Iraq was part of the war on terror, there are no real connections between Saddam Hussein and Osama Bin Laden. In fact, they were even adversaries 17:42 The CIA's so called defector, Ahmed Chalabi was a political exile from his own country. For Ahmed, the war was simply a way back into Iraq 23:08 At the 2003 state of the union speech, Bush distorted and lied left and right, breaking national law. 29:10 Bush's claims of Hussein's uranium deal with Africa were not only untrue, but vengeful - and very nearly deadly - actions were taken against those who spoke out. 36:06 In Secretary of State Powell's Address to The United States we were shown photos of so called "Chemical Bunkers" that Hussein was apparently using to produce biological weapons, and once again: there was no evidence to support these claims. 45:23 Essentially all information regarding weapons of mass destruction came from one sole source, and that source was the pentagon 53:05 Taking a look back in time, we begin to see suspicious conflictions between what was said then, and before the war. 56:25 The United States spends billions on their search for nothing, while refusing to release an informed cost estimate. 59:12 David Kay chronicles his extensive study and search for evidence of weapons of mass destruction in Iraq, and how he found nothing but lies. 1:06:42 For those behind the war, war comes first, not last. For them, The United States’ military power should be used whenever trouble arises; and wherever there’s profit to be made. 1:14:00 War is a blunt tool that doesn't work, it has tarnished our image overseas, created countless more terrorists and ruined governments. It is our job as Americans to speak out against the abuse of power, and question everything. ABOUT BRAVE NEW FILMS Robert Greenwald and Brave New Films are at the forefront of the fight to create a just America. Using new media and internet video campaigns, Brave New Films has created a quick-strike capability that informs the public, challenges corporate media with the truth, and motivates people to take action on social issues nationwide. Like us on Facebook: http://www.Facebook.com/BraveNewFilms Follow us on Instagram http://www.instagram.com/bravenewfilms Follow us on Twitter: http://www.twittter.com/bravenewfilms
Views: 86980 Brave New Films
Proactive Learning and Structural Transfer Learning: Building Blocks of Cognitive Systems
Dr. Jaime Carbonell is an expert in machine learning, scalable data mining (“big data”), text mining, machine translation, and computational proteomics. He invented Proactive Machine Learning, including its underlying decision-theoretic framework, and new Transfer Learning methods. He is also known for the Maximal Marginal Relevance principle in information retrieval. Dr. Carbonell has published some 350 papers and books and supervised 65 Ph.D. dissertations. He has served on multiple governmental advisory committees, including the Human Genome Committee of the National Institutes of Health, and is Director of the Language Technologies Institute. At CMU, Dr. Carbonell has designed degree programs and courses in language technologies, machine learning, data sciences, and electronic commerce. He received his Ph.D. from Yale University. For more, read the white paper, "Computing, cognition, and the future of knowing" https://ibm.biz/BdHErb
Views: 1691 IBM Research
How to transfer data from one workbook to another automatically using Excel VBA
Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday. For details you can visit our website: http://www.familycomputerclub.com You can use Visual Basic for Applications in Excel to transfer data from one workbook to another Excel workbook automatically. The process is simple once you understand the steps. It involves creating 3 variables or containers for data. Now we transfer the data from one workbook to the variables. Next we open the other workbook, locate the correct empty row and then the appropriate cells. Here we finally transfer the data from the variables into the cells and automatically save the workbook. Get a cup of tea or coffee or a diet coke and just work through the code. It's easy! Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V If you are from India you can get this book here: http://amzn.to/2jzJGqU
Views: 400796 Dinesh Kumar Takyar
The Fermi Paradox & the Dyson Dilemma
An in-depth look at the Fermi Paradox in terms of Kardashev 2+ civilizations. This video has been updated, the new version can be seen here: https://www.youtube.com/watch?v=QfuK8la0y6s
Views: 267913 Isaac Arthur
Peter Bailis: MacroBase, Prioritizing Attention in Fast Data Streams | Talks at Google
Professor Peter Bailis of Stanford provides an overview of his current research project, Macrobase, an analytics engine that provides efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. This is part of Google Cloud Advanced Technology Talks, a series dedicated to bringing cutting edge research and prestigious researchers to speak at Google Cloud. All speakers are leading experts and innovators within their given fields of research. Peter Bailis is an assistant professor from Stanford University.
Views: 1862 Talks at Google
Using a Lookup in JMP
A quick way to lookup a value in a JMP data table is by using a formula applied to a column with an IF statement. This is similar to VLookup in Excel. Related Blog Post http://blogs.sas.com/content/jmp/2014/04/02/lookup-tables-in-jmp/ A quick way to lookup a value in a JMP data table is by using a formula applied to a column with an IF statement. To Lookup a particular row, or a particular cell of data, in a column you would use a subscript after the Column name. However, this approach can tedious when the If clause is long or complex.  Similar to the VLookup function in Excel, we can apply the same technique using JMP.  We do this by creating a formula or by pasting a JSL script in the formula editor.   In my example with the Candy Bars sample data available from the Help menu in JMP, I have a list of fat measurements in grams, and want to lookup then calculate, then assign them ranges from "No Fat" to "High Fat" . Feel free to open this sample data and work along with me by pausing the video. There’s also a supporting and related blog entry linked below this video.   So with this data table opened  I’m going to create a New Column by double clicking a blank header.   I’ll rename it to “Fat Type” by double clicking the Column header again.   From Column Info I’ll select “Character” because I’ll use a text string.   Then I’ll add a Formula to Lookup a range of values and assign a category based on the value. Open a new script window from the File menu. In JMP a list is designated as open and closed curly braces {} and the items in the list are separated by commas. Enter the list of Fat Types as shown here: {"No Fat", "Some Fat", "Low Fat", "Normal Fat", "Medium Fat", "High Fat"}   JMP has a handy function to find value within a matrix called Loc Sorted.  It has two arguments x and y. In our example, x will be our matrix of values to look up and y will be the column we want to use to look up the values coming from the matrix. In JMP a matrix is designated as open and closed square brackets [ ] and the elements in the matrix are separated by commas. Next I’ll add the matrix of Total Fat in grams that we want to use to look up the values. I’ll enter [0,1,5,10,20,25]   Not Narrated (See page 187 of the JMP Scripting Guide for more information on the loc functions and page 188 for the loc sorted function.) Now we add this to the Loc sorted function as shown here : loc sorted([0,1,5,10,20,25],:Total fat g) To return the value found for each row in the lookup column and write it to the "Fat Type" column we use the row subscript function. Simply enclose the loc sorted function within a square bracket ”[“ and an end with right bracket "]" as shown here. [Loc Sorted([0, 1, 5, 10, 20, 25], :Total fat g)] And here is the full script you can copy. Not Narrated {"No Fat", "Some Fat", "Low Fat", "Normal Fat", "Medium Fat", "High Fat"}[Loc Sorted([0, 1, 5, 10, 20, 25], :Total fat g)] In the Formula Editor, paste that full script and click apply. Here is our new Column that lookups and organizes the Total Fat measurements column into six ranges.   You can now it for yourself by changing the matrix values with the linked data table below or your own data and enjoy using JMP.
Views: 2109 JMPSoftwareFromSAS
Keeping Track of  Qualitative Research Data using Excel
This screen cast demonstrates the use of Microsoft Excel to organize information for qualitative research.
Views: 33022 tamuwritingcenter
I talk with Clif High and Paladin about Crypto Currencies and the Future. CLIF HIGH is the originator of the Webbot system now focusing on crypto currencies. The web bot project has continued to give archetype descriptors of future events such as the anthrax attack in Washington, the crash of American 587, the Columbia disaster, the Northeast Power outage, the Banda Aceh earthquake and most recently the flooding of the Red River. As a continuing project, reports are offered from the extracted archetype information at his web site, http://halfpasthuman.com ALADIN is a forensic financial investigator, a licensed Private Investigator and assisted with in the OJ Simpson trial investigation. He is a key member of a group called The White Hats. whitehatsreport.com/ KERRY CASSIDY PROJECT CAMELOT http://projectcamelot.tv
Views: 55910 Project Camelot
Will APUs stay relevant? Plus: Nvidia rumors, Spectre/Meltdown updates, Q&A | The Full Nerd Ep 41
Join The Full Nerd gang as they talk about the latest PC hardware topics. In today's show we discuss whether or not APUs will stay relevant after the GPU/mining crisis, the new rumors around Nvidia's next graphics cards, some Spectre and Meltown updates, and of course your questions! Check out the audio version of the podcast on iTunes and Google Play so you can listen on the go and be sure to subscribe so you don't miss the latest episode. Follow PCWorld for all things PC! ------------------------------­---- SUBSCRIBE: http://www.youtube.com/subscription_center?add_user=PCWorldVideos FACEBOOK: https://www.facebook.com/PCWorld/ TWITTER: https://www.twitter.com/pcworld WEBSITE: http://www.pcworld.com
Views: 8575 PCWorld
Four-Day Planet by H. Beam Piper
Fenris isn't a hell planet, but it's nobody's bargain. With 2,000-hour days and an 8,000-hour year, it alternates blazing heat with killing cold. A planet like that tends to breed a special kind of person: tough enough to stay alive and smart enough to make the best of it. When that kind of person discovers he's being cheated of wealth he's risked his life for, that kind of planet is ripe for revolution. Chapter 1. The Ship From Terra - 00:00 Chapter 2. Reporter Working - 26:28 Chapter 3. Bottom Level - 50:00 Chapter 4. Main City Level - 1:09:38 Chapter 5. Meeting Out Of Order - 1:29:25 Chapter 6. Elementary My Dear Kivelson - 1:50:33 Chapter 7. Aboard The Javelin - 2:07:03 Chapter 8. Practice, 50mm Gun - 2:23:09 Chapter 9. Monster Killing - 2:40:15 Chapter 10. Mayday, Mayday - 2:53:16 Chapter 11. Darkness and Cold - 3:10:26 Chapter 12. Castaways Working - 3:25:41 Chapter 13. The Beacon Light - 3:37:34 Chapter 14. The Rescue - 3:48:23 Chapter 15. Vigilantes - 4:00:36 Chapter 16. Civil War Postponed - 4:23:41 Chapter 17. Tallow-Wax Fire - 4:38:42 Chapter 18. The Treason of Bish Ware - 4:57:24 Chapter 19. Masks Off - 5:15:43 Chapter 20. Finale - 5:47:41
Views: 1921 Audiobooks Unleashed
Querying and Downloading Data with the Data Portal and Transfer Tool: Genomic Data Commons Workshop
This workshop will help introduce users to the GDC tools for downloading and retrieving data from cancer genomic studies. As an example, we will query and download open access data using the GDC Data Portal and the high performance GDC Data Transfer Tool. We will also review the process for obtaining access to controlled data and demonstrate how to generate a token for downloading controlled access data. http://gdc.cancer.gov/support/gdc-workshops
Views: 3080 NCIwebinars
How to Conduct Sentiment Analysis #brandwatchtips
Hello and welcome to another addition of #brandwatchtips! This week Brit will talk you through another brilliant Brandwatch Analytics feature - Sentiment Analysis. This includes: - How to view sentiment analysis - How to analyse the sentiment - How to change incorrect sentiment Find out more here: http://www.brandwatch.com/2014/09/conduct-sentiment-analysis-brandwatchtips/ _______________________________________ Brandwatch is powerful social media monitoring, analytics and intelligence tool. Grow your presence, track your influence and understand your audience. Subscribe now for regular webinars, expert interviews and #brandwatchtips. • Get a demo of Brandwatch ➡ http://www.brandwatch.com/demo • Brandwatch ESPN Case Study ➡ http://www.brandwatch.com/case-study-espn/ • Brandwatch Consumer Tech Report ➡ http://www.brandwatch.com/report-consumer-technology/
Views: 1839 Brandwatch
BigQuery, IPython, Pandas and R for data science, starring Pearson
In this Cloud episode of Google Developers Live, Felipe Hoffa hosts Pearson's Director of Data Science Collin Sellman, to celebrate Python Pandas release 0.13 and its Google BigQuery connector. Jacob Schaer and Sean Schaefer join them to demo its capabilities, and how Pearson uses data science to improve education.
Views: 25856 Google Developers
One must-have member of your healthcare data analytics team
The University of Pittsburgh Medical Center's data analytics team includes a professional journalist who takes the raw data and turns it into a clean report anyone can read. Pamela Peele, the chief analytics officer of the Insurance Services Division at UPMC, said simply handing over rows of numbers and charts without a more polished presentation will mean executives will be less likely to consider the information hidden within the numbers.
Views: 397 medcitynews
Batch Image Processing Software - Imagisizer
Batch Image Processing Software.The latest Imagisizer Pro has a host of new features which have been developed to further enhance the already powerful software. The image processing power has been vastly improved and there is now an additional range of effects and filters, watermarking and facebook upload.
Views: 229 Imagisizer
Introduction to Google App Engine Search
In this webinar, Christina Ilvento, a Product Manager on the Google App Engine team, provides an introduction to using the App Engine Search API.
Views: 9017 Google Developers
DDes Conference: “Data Across Scales: Reshaping Design” Part 1
The Harvard Graduate School of Design and the Doctor of Design Studies Program are hosting the international interdisciplinary conference Data Across Scales: Reshaping Design. Bringing together design researchers and practitioners, the conference inquires into the role of "data" in design and how it is steering its practice across all scales. The conference will comprise four panels: “Data-driven design,” “Programming the physical world,” “Urban design and big data” and “Open data and civic media.”
Views: 1046 Harvard GSD
How to make an inventory database in Access (Part 1) - Import External Data and Create Tables
📌 Please check out my Udemy online courses: แนะนำคอร์สเรียนเขียนโปรแกรม VB.Net แบบออนไลน์ ------------------------------------------------------------------------------- 🎓 หลักสูตร "เขียนโปรแกรมด้วยภาษา VB.Net และการใช้งาน Crystal Reports" - https://www.udemy.com/vbnet-crystal-reports ---------------------------------------------------------------------------- ★ Suggested Playlists ★ » VB.Net (MS Access) Inventory Management and Point Of Sale System. ~ https://goo.gl/vpfuvi » VB.Net 2017 Workshop - Banking System (with OOP) : https://goo.gl/oqhTsK _ _ _ » Access 2013 Project - How to make an inventory database in Access 2013 Part 1 - Importing an Excel Table into MS Access Database - Import a Table from Another Access Database - Creating New Tables in MS Access Database Download Exercise Files please visit my blog: http://goo.gl/nvBX41 _ _ _ ★ Follow me on ★ Twitter » https://twitter.com/#!/IBasskung Facebook 1 » https://www.facebook.com/CodeAMinute Facebook 2 #สำหรับคนไทย » https://www.facebook.com/IbasskungTutorial Google+ » https://plus.google.com/u/0/107523775437712370629/posts YouTube Channel #ตัวท็อป » http://www.youtube.com/user/TheRockmankung Dailymotion Channel » http://www.dailymotion.com/Ibasskung-Courses Free Source Code can be found here: » https://goo.gl/UNNzp2 Thank you very much. ขอบคุณครับ. #IbasskungTutorial #IbasskungCourses #Programming #YouTube #Youtuber Ignore My Tags pls. Access VBA Programming For The Beginners Project Students Student Projects Ms Access Small Projects For Students How to create store database on MS Access 2013 How to Create a Microsoft Access Database Inventory Create a Database in Microsoft Access 2013 for Beginners Access Inventory systems project in access, the access project, access microsoft, access project databas , access database, access project file, access project database, access 2007 project, microsoft access project, microsoft project, ms project, ms access project, ms access, project 2007, the access project โปร เจ ค สอนโปรเจคจบ สอนทำโปรเจคจบ สอน ทำโปรเจ็คนักศึกษา เขียนโปรแกรม Microsoft Access VBA โปรเจ็คจบ นักศึกษา
Views: 212795 iBasskung
Realities and Realms: Responsive Technologies in Ecological Systems, Part 1
The Realities and Realms colloquium focuses on the role of computation and robotics in landscape architecture and the expanding sensorial field of the built environment. These hybrid grounds of operation merge anthropogenic perception and technological mediation. As sensing networks expand, data grows exponentially in quantity and ubiquity, building an increasingly abstract landscape of information. How such data is elucidated, curated, and augmented forms new realities for design. This colloquium will explore design methodologies that address concurrent physical and virtual realms and the realities in which they operate. In this context, a realm is a lens through which we sense an environment and a reality is place within which we take action. The Realities and Realms colloquium engages select practitioners, theorists, and academics for an afternoon to explore the future of responsive technologies to interpret and modify environment. Panelists will posit trajectories that frame the role of responsive technologies to imagine, choreograph, and evolve cyborg landscapes and synthetic ecologies. The colloquium will be organized in two panel sessions followed by open discussions, exploring the tools, practice, theories, and futures of responsive technologies in landscape architecture.
Views: 1797 Harvard GSD
Intro to Azure ML & Cloud Computing
Azure Machine Learning Studio is a fully featured graphical data science tool in the cloud. You will learn how to upload, analyze, visualize, manipulate, and clean data using the clean and intuitive interface of Azure ML. -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 2700+ employees from over 400 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2lF2veF See what our past attendees are saying here: http://bit.ly/2o9Atsr -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 2852 Data Science Dojo
Information Flow and Graph Structure in Online Social Networks
Jon Kleinberg of Cornell University presents a model that tracks the sharing and dispersion of information through social media networks.
Using Automated Network Detection & Response to Visualize Malicious IT Events Within Power Systems
This webinar featured Gene Stevens, co-founder and chief technology officer of ProtectWise, on how the proliferation of Internet of Things devices, Internet protocol traffic, and bad actors requires a new approach to the capture, visualization, and management of malicious cyber events. Stevens discusses how new, automated network detection and response tools are needed to support power systems from generation through transmission, all the way to the distribution infrastructure
Views: 501 NREL Learning
Buying Options VS. Selling Options - 5 Year Study
Hey everyone, welcome to another lesson from NavigationTrading! In this video, I want to talk to you about the debate of buying vs. selling options. We're going to debate this by doing a couple of back tested studies. This blog post isn't my opinion of what I think is better. These studies are raw data, numbers, statistics, and back tested data going back five years. In our first back tested study, we’re taking a look at SPY (SPY 500 Index). We utilize SPY because we don't want to use an individual stock. If we did that, we’d have to take into consideration earnings announcements, dividends and those sorts of things. We're looking at a broad-based ETF. SPY is the most liquid ETF out there. We’ll be looking at 5 years of back tested data. In this first study, we're going to buy both the calls and the puts. We're not looking to get directional, because obviously, in a bull market, the bullish strategies would perform better. In a bear market, the bearish strategies would perform better. We want to enter these trades as a delta neutral strategy. That way we can see which is better, buying options or selling options. So, we're going to buy both sides... Watch our video for full details on both studies! Click https://cmlviz.com/navt to get the CML Back-tester today! Happy Trading! The NavigationTrading Team https://www.navigationtrading.com [email protected] Connect with us! YouTube.com/navigationtrading Facebook.com/navigationtrading Twitter @navtrading1 Stocktwits.com/navigationtrading
Views: 159 NavigationTrading
Big Brain Data Science & Predictive Health Analytics
Ivo D. Dinov is a professor and Associate Director for Education & Training at Michigan Institute for Data Science and is the Director of the Statistics Online Computation Resource in the Department of Health Behavior & Biological Sciences at University of Michigan. In this video Dr. Dinov will present a lecture titled, “Big Brain Data Science & Predictive Health Analytics.” Video Description This presentation will focus on Predictive Big Data Analytics. We will define the characteristic properties of Big Data, examine methodological & computational challenges, showcase health science applications, & identify research opportunities. We will utilize general population data, biosocial (e.g., Medicare/Economics) and neurodegenerative disorders (e.g., Parkinson’s Disease) case-studies to demonstrate Specific solutions. The foundations of a Compressive Big Data Analytics (CBDA) technique will be presented that allow generic representation, modeling & inference on large, incongruent, multi-source, incomplete & multi-scale datasets. About the Speaker Dr. Dinov is the Director of the Statistics Online Computational Resource (SOCR) and is an expert in mathematical modeling, statistical analysis, high-throughput computational processing and scientific visualization of large datasets (Big Data). His applied research is focused on neuroscience, nursing informatics, multimodal biomedical image analysis, and distributed genomics computing. Examples of specific brain research projects Dr. Dinov is involved in include longitudinal morphometric studies of development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s disease, Parkinson’s disease). Bio from: nursing.umich.edu/faculty-staff/faculty/ivo-d-dinov View slides from this lecture: https://drive.google.com/open?id=1Lws4RtikJNOOcOHVkxc4A3t3oHXElVLF Visit our webpage to view archived videos covering various topics in data science: https://bigdatau.ini.usc.edu/data-science-seminars
Using nDepth and Reports to Search and Analyze Log Data with Log & Event Manager
For more information about LEM, visit: http://bit.ly/LEM_nDepth In this short video you will learn about IT search and historical reporting for analysis and compliance with SolarWinds Log & Event Manager. SolarWinds Log & Event Manager (LEM) delivers powerful Security Information and Event Management (SIEM) capabilities in a highly affordable, easy-to-deploy virtual appliance. It combines real-time log analysis, event correlation and a groundbreaking approach to IT search to deliver the visibility, security and control you need to overcome everyday IT challenges. SIEM software has never been more affordable or easier to use.
Views: 5396 solarwindsinc