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Google Analytics Data Mining with R (includes 3 Real Applications)
 
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R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 28196 Tatvic Analytics
A GCP developer's guide to building real-time data analysis pipelines (Google Cloud Next '17)
 
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In this video, you'll learn how to build a real-time event-driven data processing and analysis pipeline on Google Cloud Platform (GCP). Rafael Fernandez and Slava Chernyak show you how to ingest large-scale data streams and how to perform real-time normalization, aggregation and complex computation. You'll also learn how to create real-time dashboards and how to advance your data warehouse and machine learning skills. They cover tools and features in Google Cloud Pub/Sub, Cloud Dataflow, Cloud Bigtable, BigQuery and Cloud Machine Learning. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k
Views: 4870 Google Cloud Platform
Analyzing Big Data in less time with Google BigQuery
 
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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: 55369 Google Cloud Platform
Live Data Streaming in Power BI
 
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The speed of creating an Analysis or an Overview of the KPIs in a company is growing in significance. To make quality decisions, one needs both quality data and up-to-date data. Without accurate live data, it’s difficult to steer the ship. This session will look at today’s methods of live data streaming and in doing that we’re going to look at the famous IoT world. This session will incorporate samples using live key-strokes input, followed by more real-life samples. Excel, Visual Studio, Azure Data Streaming, PowerShell and Power BI will be shown. The session will require a camera to show Phone and Tablet Dashboard. Follow us on Twitter - https://twitter.com/mspowerbi More questions? Try asking the Power BI Community @ https://community.powerbi.com/
Views: 39648 Microsoft Power BI
Data Mining using Google Correlate
 
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In this video you will be introduced to the Google product "Google Correlate"". You can find which search word trend is matching with the real world time series data. Contact us [email protected]
Views: 1556 Analytics University
Demo - Using REST Source to Get Data from Google Analytics
 
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Learn more about Task Factory and download a free trial: http://success.pragmaticworks.com/try-task-factory Pragmatic Works Task Factory REST Source helps pull the data you need from Google Analytics. Use REST Source to create dashboards and reports on the data.
Views: 5255 Pragmatic Works
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 137212 SciShow
AI for Marketing & Growth #1 - Predictive Analytics in Marketing
 
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AI for Marketing & Growth #1 - Predictive Analytics in Marketing Download our list of the world's best AI Newsletters 👉https://hubs.ly/H0dL7N60 Welcome to our brand new AI for Marketing & Growth series in which we’ll get you up to speed on Predictive Analytics in Marketing! This series you-must-watch-this-every-two-weeks sort of series or you’re gonna get left behind.. Predictive analytics in marketing is a form of data mining that uses machine learning and statistical modeling to predict the future. Based on historical data. Applications in action are all around us already. For example, If your bank notifies you of suspicious activity on your bank card, it is likely that a statistical model was used to predict your future behavior based on your past transactions. Serious deviations from this pattern are flagged as suspicious. And that’s when you get the notification. So why should marketers care? Marketers can use it to help optimise conversions for their funnels by forecasting the best way to move leads down the different stages, turning them into qualified prospects and eventually converting them into paying customers. Now, if you can predict your customers’ behavior along the funnel, you can also think of messages to best influence that behavior and reach your customer’s highest potential value. This is super-intelligence for marketers! Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This’ll allow you to focus your team’s efforts on leads with the highest ROI. Which will also imply a shift in mindset. Going from quantity metrics, or how many leads you can attract, to quality metrics, or how many good leads you can engage. You can now easily predict your OMTM or KPIs in real-time and finally push vanity metrics aside. For example, based on my location, age, past purchases, and gender, how likely are you to buy eggs I if you just added milk to your basket? A supermarket can use this information to automatically recommend products to you A financial services provider can use thousands of data points created by your online behaviour to decide which credit card to offer you, and when. A fashion retailer can use your data to decide which shoes to recommend as your next purchase, based on the jacket you just bought. Sure, businesses can improve their conversion rates, but the implications are much bigger than that. Predictive analytics allows companies to set pricing strategies based on consumer expectations and competitor benchmarks. Retailers can predict demand, and therefore make sure they have the right level of stock for each of their products. The evidence of this revolution is already around us. Every time we type a search query into Google, Facebook or Amazon we’re feeding data into the machine. The machine thrives on data, growing ever more intelligent. To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place. 1. The right questions 2. The right data 3. The right technology 4. The right people Ok.. let’s look at some use cases of businesses that are already leveraging predictive analytics. Other topics discussed: Ai analytics case study artificial intelligence big data deep learning demand forecasting forecasting sales machine learning predictive analytics in marketing data mining statistical modelling predict the future historical data AI Marketing machine learning marketing machine learning in marketing artificial intelligence in marketing artificial intelligence AI Machine learning ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/company/growth-tribe Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Snapchat: growthtribe Video URL: https://youtu.be/uk82DHcU7z8
Views: 13511 Growth Tribe
Historical Data Mining & Live Order Flow Analysis with Sharedata Futures Analytics
 
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Learn More: https://ninjatraderecosystem.com/webinar/historical-data-mining-live-order-flow-analysis/ Where does asymmetric opportunity reside in WTI Crude Futures? In this presentation by Taylor Ireland of Sharedata Futures Analytics, see how Sharedata's Historical Data Mining Toolset coupled NinjaTrader's Order Flow+ suite can help you identify asymmetric opportunity and dynamically manage risk in WTI Crude Futures.
Views: 431 NinjaTrader
Real-Time Analytics for Data-Driven Applications - Milind Bhandarkar
 
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To provide hyper-personalized digital experiences in the emerging market transformation, innovative enterprises are building modern data-driven applications to deliver continuing value to their always-connected customers. Such applications need to utilize closed-loop deep insights to influence their users' behaviors in real-time. However, the traditional ways of capturing users' interactions, transporting data to large data warehouses or data lakes, further away from applications, and processing these data across multiple slow stages cannot meet the real-time expectations of both customers and businesses. What if one could capture, analyze, and serve data from a highly concurrent, high-performance data store powering these applications? In this talk, we'll present a memory-centric Active Data Store (ADS), powered by Apache Geode, to meet the exigent demands of modern applications while providing operational simplicity. Ampool's ADS allows fast ingest and storage of 'hot' app data, in situ updates and analysis, and data serving from the same scalable distributed in-memory data store. As the data cools (ages), Ampool ADS automatically tiers data to warm and cold secondary stores. By speeding analytics several-fold, Ampool enables feeding actionable insights back to applications, driving decisions in a closed loop. We will demonstrate the applicability of Ampool ADS for such an app by serving all data-access patterns from a single memory-centric store. Slides: TBA Milind Bhandarkar, Founder & CEO, Ampool Recorded at SpringOne Platform 2017
Twitter data mining and analysis in real time - IQLECT's Ampere and Python
 
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This video demonstrates how we can mine data using python script and tweepy API and later use that same data to analyze trends in twitter using IQLECT's Ampere. Ampere is a real time big data analytics platform that can receive data from any source and provide actionable insights for business. The step by step guide shows how the guide can be easily used to mine twitter for specific keywords. For more videos and documentation please visit www.iqlect.com
Views: 201 IQLECT
Ecommerce Analytics - Click Stream Data Analytics
 
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Convert your abandoned shopping cart into sales. Identify segments and target your customers. Increase Conversion using click stream data analytics using predictive modelling techniques. Talk to us [email protected] Learn more about Happiest Minds Ecommerce Solution http://www.happiestminds.com/ecommerce-solutions/ Learn more about Happiest Minds Ecommerce Analytics http://www.happiestminds.com/ecommerce-analytics/ Related Links http://www.happiestminds.com/big-data-analytics/ Website http://www.happiestminds.com/ Have a question? Write to us http://www.happiestminds.com/write%20to%20us Connect with us on Facebook: https://www.facebook.com/happiestminds Twitter : http://twitter.com/#!/happiestminds LinkedIn : http://www.linkedin.com/company/happiest-minds-technologies Slideshare : http://www.slideshare.net/happiestminds Google + : https://plus.google.com/u/0/+happiestminds/posts
Views: 8214 Happiest Minds
AT Internet’s Analytics Suite: Data Query application
 
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AT Internet’s Data Query application makes data mining as easy as dragging and dropping. Cross-calculate, compare, correlate, filter and sort analytics data in just seconds with Data Query. More about Data Query: http://www.atinternet.com/en/product/data-query/ Want to know more about AT Internet? Visit http://www.atinternet.com/en Be sure to check out our blog: http://blog.atinternet.com/en Follow us to stay updated on the latest trends in digital analytics: -Facebook : https://www.facebook.com/atinternet.analytics/ -Twitter : https://twitter.com/AT_Internet -LinkedIn : https://www.linkedin.com/company/at-internet -SlideShare : http://fr.slideshare.net/AT-Internet -Xing : https://www.xing.com/companies/atinternetgmbh -Google + : https://plus.google.com/+ATinternet
Views: 1296 AT Internet
Data Mining - Facebook part 1
 
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This tutorial shows how to access,use and communicate with Facebook API using graph API explorer.It gives a brief idea about what kind of data we can retrieve from Facebook.
Views: 25255 Vikash Khairwal
Interview with a Data Scientist
 
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This video is part of the Udacity course "Intro to Programming". Watch the full course at https://www.udacity.com/course/ud000
Views: 281348 Udacity
The ART of Data Mining – Practical learnings from real-world data mining applications
 
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Machine Learning and data mining is part SCIENCE (ML algorithms, optimization), part ENGINEERING (large-scale modelling, real-time decisions), part PROCESS (data understanding, feature engineering, modelling, evaluation, and deployment), and part ART. In this talk, Dr. Shailesh Kumar focuses on the "ART of data mining" - the little things that make the big difference in the quality and sophistication of machine learning models we build. Using real-world analytics problems from a variety of domains, Shailesh shares a number of practical learnings in: (1) The art of understanding the data better - (e.g. visualization of text data in a semantic space) (2) The art of feature engineering - (e.g. converting raw inputs into meaningful and discriminative features) (3) The art of dealing with nuances in class labels - (e.g. creating, sampling, and cleaning up class labels) (4) The art of combining labeled and unlabelled data - (e.g. semi-supervised and active learning) (5) The art of decomposing a complex modelling problem into simpler ones - (e.g. divide and conquer) (6) The art of using textual features with structured features to build models, etc. The key objective of the talk is to share some of the learnings that might come in handy while "designing" and "debugging" machine learning solutions and to give a fresh perspective on why data mining is still mostly an ART.
Views: 1682 HasGeek TV
Using Google Analytics for Data-Driven Marketing - Dan Stone
 
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Dan Stone, Product Manager at Google Analytics H2O World 2015, Day 2 Join the Movement: open source machine learning software from H2O.ai, go to Github repository https://github.com/h2oai Do you like this? Check out more talks on open source machine learning software at: http://www.slideshare.net/0xdata
Views: 2454 H2O.ai
How to Analyze Google Analytics data
 
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http://outcareyourcompetition.com/how-to-setup-google-analytics/ What data should you be looking at in your Google Analytics account? Watch this video to find out! This is part one of my ten part series on the best free internet marketing tools you should be using.
Views: 4072 Jordan J. Caron
Google Data Studio Tutorial Part #1: Why We Use Data Studio
 
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We’ve all heard the saying "A picture is worth a thousand words." But a picture of your marketing data can be worth much more than words! If you’re using charts or graphs to present your work to your stakeholders or clients, pictures of your data could be worth LOTS OF MONEY. *Read full tutorial - https://www.jeffalytics.com/google-data-studio-tutorial/ Data visualization can also be critical to internal reporting. Using a dashboard can help your team track their progress and achieve their goals quicker. But creating a picturesque, “budget approved” worthy marketing reports can take forever. As I much as I hate to admit it, I used to spend 30+ hours a week building reports for clients. Fortunately, nowadays, there are tools like Google Data Studio (GDS) that can help significantly reduce the time it takes to produce reports. And with the right game plan, we can create reports that allow our clients or our organization to see our grand marketing vision. Reporting is critical to every business. Reports show clients, team members, and stakeholders the results of our marketing efforts. Reporting also plays a vital role in getting budgets approved and contracts renewed. But there are two major obstacles to delivering the kind of reports that get people to take action. The first obstacle is time. Mining data and formatting charts can eat up hours and hours of time. The second obstacle to effective reporting is strategy. Without strategy, reports have a habit of turning into a jumbled mess of disjointed KPIs. It's our job as marketers, to make sure our audience can follow our reports. So we need to create reports that communicate a story about our marketing strategy. Our reports should highlight our results and provide conclusions we can use to inform our decisions making. In this tutorial, we are going learn how to use Google Data Studio to reduce our reporting time and effort. We'll look at how to: - Get started in Data Studio -Use the GDS data connectors to save yourself from repetitive data mining - Create branded charts, graphs and tables without doing hours of data manipulation Once we have some Data Studio basics under our belt, we'll look at how we can use strategy to deliver better reports. I'll introduce my brand new ACES Framework, and we'll look at how to: - Use ACES to picture our entire marketing funnel, top-to-bottom - Create a Data Studio dashboard that reports on our complete marketing funnel - Build targets in your reports so that you can measure your progress toward your goals - Connect Data Studio to platforms outside the Google family of products. Google Data Studio Tutorial - Part #1 - Why should we use Google Data Studio? Live data and integrations are a difference maker There are three key ingredients that make Google Data Studio such a useful platform. More effective graphics The visualizations in Data Studio are easy to use. They also feature a lot of design options. And they about 100x more attention-grabbing than the Excel charts and tables, or the Google Analytics dashboards. Automating data mining Connecting GDS to your other marketing systems can save you from hours of data mining. Data Studio natively connects to over 17 different platforms. These platforms include Google Analytics, Google Ads, YouTube, and Google Sheets. You can also use the Data Studio partner connectors to hook up to over 90 other data sources. As part of this tutorial, we’ll take a quick look at a premium partner connector - Supermetrics - that can help you connect to: Facebook, LinkedIn, Twitter, Pinterest, SEMRush and many other platforms. Live Dashboards Data Studio reports are live. As long as your marketing systems are connected to Data Studio, your dashboard can serve as a real-time picture of your KPIs. Follow along with the rest of our Data Studio Tutorial - https://www.jeffalytics.com/google-data-studio-tutorial/
Views: 419 Jeffalytics
How to Apply Machine Learning (R,  Apache Spark, H2O.ai) To Real Time Streaming Analytics
 
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This video shows how business analysts, data scientists and developers work together to bring an analytic machine learning model into a (real time) production deployment. The beginning explains in two minutes the methodology before a 10min live demo discusses use cases such as customer churn and predictive analytics to demonstrate how different tooling for visual analytics / data discovery (TIBCO Spotfire), advanced analytics / machine learning (TIBCO Spotfire in conjunction with R, H2O.ai, Apache Spark) and stream processing / streaming analytics (TIBCO StreamBase, TIBCO Live Datamart) are combined by leveraging the same analytic model (e.g. clustering, random forest) without redevelopment. You are just beginning your journey with deploying analytic models to real time processing? Feel free to contact me to discuss your architecture, challenges and questions… If you want to discover some components by yourself, please check out our new and growing TIBCO Community Wiki (https://community.tibco.com/wiki). It already contains a lot of information about the discussed components, e.g. the page “Machine Learning in TIBCO Spotfire and TIBCO Streambase” (https://community.tibco.com/wiki/machine-learning-tibco-spotfirer-and-tibco-streambaser). You can also ask questions in the Answers section to get a response by a TIBCO expert or other community members (https://community.tibco.com/answers).
Views: 3923 Kai Wähner
Google and CIA Internet Data Mining Project
 
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Google Ventures, the investment arm of Google, has injected a sum of up to $10 million, as has In-Q-Tel -- which handles investments for the CIA and the wider intelligence network -- into a company called Recorded Future. The company describes its analytics as "the ultimate tool for open-source intelligence". Wired's defence analyst, Noah Schachtman, has a detailed report on the joint venture: "...it scours tens of thousands of websites, blogs and Twitter accounts to find the relationships between people, organizations, actions and incidents — both present and still-to-come. In a white paper, the company says its temporal analytics engine "goes beyond search" by "looking at the 'invisible links' between documents that talk about the same, or related, entities and events." The idea is to figure out for each incident who was involved, where it happened and when it might go down. Recorded Future then plots that chatter, showing online "momentum" for any given event." Recorded Future "continually scans thousands of news publications, blogs, niche sources, trade publications, government web sites, financial databases and more," according to it's portfolio. It sifts through millions of posts and conversations taking place on blogs, YouTube, Twitter and Amazon to "assemble actual real-time dossiers on people." It is also being integrated with Google Earth, which, as Schachtman points out in his piece, was seeded with In-Q-Tel/CIA investment. This integration will allow real time tracking of the locations of persons or groups as part of the overall intelligence dossier. Recorded Future takes in vast amounts of personal information such as employment changes, personal education and family relations. The video also shows categories covering pretty much everything else, including entertainment, music and movie releases, as well as other innocuous things like patent filings and product recalls.
Views: 6228 the1dutchmaster
Downloading Data from Google Trends And Analyzing It With R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUOKu In this video, I introduce Google Trends by querying it directly through the web, downloading a comma-delimited file of the results, and analyzing it in R. Full walkthrough and code: http://amunategui.github.io/google-trends-walkthrough/ Support these videos, check out my in-depth classes on Udemy.com (discounts and specials) at http://amunategui.github.io/udemy/
Views: 20424 Manuel Amunategui
Google Search - Data Mining #4
 
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Searching the Internet using Google custom search API. Starting by setting up a Google Custom Search to search the entire web. Then setting up Custom Search API to access the search service in a with in your code. GitHub/NBViewer Link: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/4.%20Google%20Custom%20Search.ipynb Other important Links: Google Custom Search: https://www.google.com/cse/ Google Developer Console: https://console.developers.google.com/ Custom Search Documentation: https://developers.google.com/custom-search/json-api/v1/reference/cse/list
Views: 9504 Roshan
Real-Time Big Data Analytics with R [AAT-204]
 
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Speaker(s): David Smith Taking data science into action requires deploying statistical models into production environments, usually with real-time processing requirements. Every company that relies on predictive models to drive their applications and operations has a different process for model deployment, but by working with many such companies, a common pattern has emerged. The real-time model deployment process can be broken down into these five stages: * Data distillation * Model development * Model validation and deployment * Model refresh * Real-time model scoring The R language, the lingua franca of data scientists, is widely used for model development. In this talk, we'll focus on the big data capabilities of Revolution R Enterprise and integrate R into this real-time analytics deployment process. We'll also explore how Revolution R Enterprise integrates with other technologies in the real-time analytics deployment process, including Hadoop, database warehousing systems, and end-user applications.
Views: 409 PASStv
DATA & ANALYTICS: Analyzing 25 billion stock market events in an hour with NoOps on GCP
 
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Recorded on Mar 23 2016 at GCP NEXT 2016 in San Francisco. Watch how FIS & Google are working to build a next-generation stock market reconstruction system that aims to bring transparency to the US financial markets and drive innovation across financial services. In this video we dive into the proposed system architecture and show how products like Cloud Bigtable, Cloud Dataflow and BigQuery enable this process. As part of the exercise, we ran a load test to process, validate, and link 25 billion US equities and options market events in 50 minutes, generating some impressive statistics in the process. Speakers: Neil Palmer and Todd Ricker from FIS and Carter Page from Google.
Views: 19845 Google Cloud Platform
Real-time data mining 1.usa.gov
 
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Real time datamining from 1.usa.gov
Views: 39 Jacob Thomas
How Big Data Is Used In Amazon Recommendation Systems | Big Data Application & Example | Simplilearn
 
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This Big Data Video will help you understand how Amazon is using Big Data is ued in their recommendation syatems. You will understand the importance of Big Data using case study. Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspectives, business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 24689 Simplilearn
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 231417 Siraj Raval
Machine learning models + IoT data = a smarter world (Google I/O '18)
 
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With the IoT market set to triple in size by 2020, and massive increases in computing power on small devices, the intersection of IoT and machine learning is a trend that all developers should pay attention to. This talk will cover three core use cases, including: how to manage sourcing data from IoT devices to drive machine-learned models; how to deploy and use trained models on mobile devices; and how to do on-device training with a Raspberry Pi computer. Rate this session by signing-in on the I/O website here → https://goo.gl/rYcGev Watch more IoT sessions from I/O '18 here → https://goo.gl/xfowJ8 See all the sessions from Google I/O '18 here → https://goo.gl/q1Tr8x Subscribe to the Google Developers channel → http://goo.gl/mQyv5L #io18
Views: 17584 Google Developers
Search Engine Optimization in Hindi Urdu Part 62 Google Analytic Tools   Real Time
 
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Views: 18 SEO Teacher
DATA & ANALYTICS - Build smart applications with your new superpower: cloud machine learning
 
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Recorded on Mar 24 2016 at GCP NEXT 2016 in San Francisco. Visual effects rendering is a computationally intensive process where one second of screen-time can require thousands of cores and terabytes of frame data. Learn how Academy Award-winning and recognized studios take advantage of cloud economics and Google's on-demand computing to realize their creative visions and expand this digital medium for storytelling. Speakers: Julia Ferraioli, Google & David Zuckerman, Wix
Views: 130833 Google Cloud Platform
Getting started with Firebase Analytics, BigQuery - Firecasts
 
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Read the documentation: https://cloud.google.com/bigquery/docs Read the blog post: https://goo.gl/WU0KQ1 iOS Sample Data: https://goo.gl/l3Jd46 Android Sample Data: https://goo.gl/xmBEMd Want to get even more from your Firebase Analytics reports? Todd shows you how you can export your data to BigQuery and run customized queries on event properties to get exactly the data you need to understand what users are doing in your app. Add the Firecasts playlist! https://goo.gl/n2XqG1 Subscribe to the Firebase Channel: https://goo.gl/9giPHG Music by http://terramonk.com
Views: 21903 Firebase
Measure Matters Episode 1: Machine Learning
 
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In a new series, Analytics Advocates Krista Seiden and Louis Gray will present live from Google headquarters in Mountain View. In episode one, the pair will discuss how machine learning is changing the measurement industry. Join us and bring your questions. #measurematters
Views: 10920 Google Analytics
R - Twitter Mining with R (part 1)
 
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Twitter Mining with R part 1 takes you through setting up a connection with Twitter. This requires a couple packages you will need to install, and creating a Twitter application, which needs to be authorized in R before you can access tweets. We quickly go through this entire process which may take some flexibility on your part so be patient and be ready troubleshoot as details change with updates. Warning: You are going to face challenges setting up the twitter API connection. The steps for this part have been known to change slightly over time for a variety of reasons. Follow the general steps and expect a few errors along the way which you will have to troubleshoot. It is hard to solve these issues remotely from where I am.
Views: 62968 Jalayer Academy
Data analytics with Microsoft Azure
 
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Explore the comprehensive set of services Microsoft Azure has for ingesting, storing and analyzing data of almost all types of scales, spanning table, file, streaming and other data types. The Azure platform provides tools across the data analytics' life-cycle. www.azure.com/essentials (Azure Essentials)
Views: 14767 Microsoft Mechanics
Predicting Stock Prices - Learn Python for Data Science #4
 
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In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 484419 Siraj Raval
Campaign Tagging: Finding Campaign Data in Google Analytics (Part 2)
 
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I've just set up my campaign tags. Now what? In this episode of Whole Whale TV, get ready to track all the campaign data you have been looking for in Google Analytics! 5 Campaign Tagging Tips 1. Be consistent with naming. If you capitalize one letter, and someone else keeps them all lowercase, the data will show up in two different lines in your reports. 2. Don’t tag everything Save time, and don’t tag what is defaulted if you don’t have to! 3. Don’t campaign tag within your own site If you want to see how someone is moving from one site to another, you can see that through Analytics’s “Previous Page Path” or “Next Page Path.” You will overwrite the data of the original source when you add new campaigns. Be sure to use event tracking instead. Nerdy how-to on event tracking here. 4. Avoid Long URLs People are not likely to click long URLs. So, use Bitly to make cute and short URLs, and embed and hyperlink text so that users are not seeing clunky URLs. Check out our campaign tagging spreadsheet! 5. Use “+” to create spaces If you want to use a two worded campaign tag, just add the plus sign and it will appear as two words in your report. Plus, we walk you through how to find campaign data in Google Analytics. More info here: https://www.wholewhale.com/tips/how-to-find-campaign-data-in-google-analytics/ ------- Whole Whale is a digital agency that leverages data and technology to increase the impact of nonprofits. In the same way the Inuits used every part of whale, Whole Whale leverages existing resources to see, "What else can this do for us?" By using data analysis, digital strategy, web development, and training, WW builds a 'Data Culture' within every nonprofit organization they work with. ------- Check us out on Facebook : https://www.facebook.com/WholeWhale Tweet us: https://twitter.com/WholeWhale Visit our website: http://wholewhale.com/
Views: 1065 WholeWhale
"Big Data Revolution" - PBS Documentary
 
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Big Data. This massive gathering and analyzing of data in real time is allowing us to not only address some of humanity biggest challenges but is also helping create a new kind of planetary nervous system. Yet as Edward Snowden and the release of the Prism documents have shown, the accessibility of all these data comes at a steep price. This documentary captures the promise and peril of this extraordinary knowledge revolution - Big Data Revolution. THIS IS A COPY OF ORIGINAL VIDEOCLIP THAT CAN BE FOUND HERE: https://www.youtube.com/watch?v=ahNdJdf867A
Views: 75234 Bitcoin TV
googleAnalyticsR R Shiny  - creating a talking Google Analytics Shiny app
 
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Walks through how to create a Shiny app pulling in Google Analytics data via googleAnalyticsR, then enhances it to talk through statistics of your data using googleLanguageR and present it within a custom HTML file. Working from this GitHub repo https://github.com/MarkEdmondson1234/verbal_ga_shiny Also available in more bite-sized videos: Part I - basic Shiny app with Google Analytics data: https://youtu.be/IqAaCk_3ZKU Part II - adding text-to-speech with googleLanguageR https://youtu.be/Ny0e7vHFu6o Part III - custom skin for the Shiny app using gentellelaShiny https://youtu.be/Dp6Y6Trn97A
Views: 1008 Mark Edmondson
Real-Time Machine Learning with Node.js by Philipp Burckhardt, Carnegie Mellon University
 
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Real-Time Machine Learning with Node.js - Philipp Burckhardt, Carnegie Mellon University Real-time machine learning provides statistical methods to obtain actionable, immediate insights in settings where data becomes available in sequential order. After providing an overview of state of the art real-time machine learning algorithms, we discuss how these algorithms can be leveraged from within a Node.js application. We will see why the powerful API of the core stream module makes Node.js a more attractive platform for such tasks compared to languages traditionally used for scientific computing such as R, Python or Julia. Finally, we will discuss best-practices and common pitfalls that one faces when using these algorithms.
Views: 37846 node.js
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters
 
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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
Views: 22442 THE MIND HEALING
How to perform predictive analysis on your web analytics tool data - 2013 06 19
 
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When: June 19th, 2013 Education Level: Advanced What: It is widely known that traditional web analytics have been a great way to optimize/analyse your website visitor metrics. Many analytics tools, like Google Analytics, are available there and enable you to track basic metrics from your website with ease. Although, most of the time these tools provide data at aggregate level which limits the understanding of interplay amongst different variables. With predictive analytics, you can explore the hidden relationship between many independent variables (e.g. pageviews per visit, quality score of keyword, time of visit), in order to see how these variables affect your KPIs like revenue & transactions. During the webinar we help you to understand the real value of predictive analytics when applied on web analytics data to help improve your understanding relationship between different variables. From this webinar, you will get to know: Part 1 (02m00s) - Analytics disciplines Part 2 (06m02s) - What is R and why should you use this tool? Part 3 (13m01s) - How to extract your Web Analytics data into R? Part 4 (18m35s) - How to build a predictive model using web analytics data with the help of R? How predictive modelling can take your analysis to the next level? Part 3 (46m34s) - How to carry out insightful analysis through visualization? Part 4 (54m28s) - Q&A Round Who should watch: Every web analyst who wants to take his analysis to the next level. Website: www.tatvic.com/webinars
Views: 9318 Tatvic Analytics
YouTube Data API - Data Mining #2
 
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Data mining YouTube using youtube.search.list and youtube.videos.list to forecast the senate races of 2014. And quantifying our probability using 2012 senate races data and stats from YouTube during the same period. Github/NBViewer Link: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/2.%20YouTube%20Data.ipynb
Views: 6285 Roshan
How It Works: Analytics
 
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"Information is flowing like mighty rivers from a trillion connected and intelligent things . . ." Analytics explained though simple narration and illustrations. Words, voice, sound: Chris Luongo Art: Jane Harris
Views: 197839 Social Media
Big Data Tools and Technologies | Big Data Tools Tutorial | Big Data Training | Simplilearn
 
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This Big Data Tools Tutorial will explain what is Big Data?, Big Data challenges and some of the popular Big Data tools involed in Big Data processing and management. The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop technologies and various Big Data tools have emerged to solve the challenges in Big Data environment. There are a lot of Big Data tools, all of them help in some or the other way in saving time, money and in covering business insights. This video will talk about such tools used in Big Data management. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 7260 Simplilearn
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
 
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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 363708 CrashCourse
Real-Time Big Data Analytical Architecture for Remote Sensing Application
 
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Real-Time Big Data Analytical Architecture for Remote Sensing Application TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com The assets of remote senses digital world daily generate massive volume of real-time data (mainly referred to the term “Big Data”), where insight information has a potential significance if collected and aggregated effectively. In today’s era, there is a great deal added to real-time remote sensing Big Data than it seems at first, and extracting the useful information in an efficient manner leads a system toward a major computational challenges, such as to analyze, aggregate, and store, where data are remotely collected. Keeping in view the above mentioned factors, there is a need for designing a system architecture that welcomes both realtime, as well as offline data processing. Therefore, in this paper, we propose real-time Big Data analytical architecture for remote sensing satellite application. The proposed architecture comprises three main units, such as 1) remote sensing Big Data acquisition unit (RSDU); 2) data processing unit (DPU); and 3) data analysis decision unit (DADU). First, RSDU acquires data from the satellite and sends this data to the Base Station, where initial processing takes place. Second, DPU plays a vital role in architecture for efficient processing of real-time Big Data by providing filtration, load balancing, and parallel processing. Third, DADU is the upper layer unit of the proposed architecture, which is responsible for compilation, storage of the results, and generation of decision based on the results received from DPU. The proposed architecture has the capability of dividing, load balancing, and parallel processing of only useful data. Thus, it results in efficiently analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the proposed architecture has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required. Finally, a detailed analysis of remotely sensed earth observatory Big Data for land and sea area are provided using Hadoop. In addition, various algorithms are proposed for each level of RSDU, DPU, and DADU to detect land as well as sea area to elaborate the working of an architecture.
Views: 1477 jpinfotechprojects
Getting Started with Google APIs (Python)
 
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In this session, we build and deploy a simple App Engine application using Google APIs (eg. Google+ API) and OAuth2. We also demonstrate use of APIs Explorer, Quickstart widget, client libraries and App Engine SDK tools that make application development easier. This session demonstrated by Prashant Labhane is in Python and you can find the equivalent Java version at http://www.youtube.com/watch?v=tVIIgcIqoPw by Sachin Kotwani. You can find more references about Google Cloud development at developers.google.com and cloud.google.com.
Views: 120269 Google Developers

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