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Data Visualization Needs to Die

Learn how to view the whole world as your data-plotting device.
Recorded Dec 3 2014 36 mins
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Presented by
Thomas Levine, Sr. Innovation Hadoop Scientist
Presentation preview: Data Visualization Needs to Die

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  • Visualizing Smart Cities: How Data Visualization can help shape our communities Jun 23 2017 12:00 pm UTC 60 mins
    Andy Kriebel, Head Coach at The Data School, Tableau Zen Master & Eva Murray, Tableau Evangelist at EXASOL, Tableau Trainer
    Whenever there is data, there is the chance to visualize it and gain valuable insights that can drive change and improvements. Governments have realized the potential that data holds for transforming our towns, cities, living spaces and communities to better address the needs of our modern society.

    Governments may want to change public transport services to suit commuters who move away from city centers due to increasing living costs, or develop programs that deliver more support services to areas showing high incidences of mental illnesses, or simply monitor bike traffic to assess the necessity of additional cycle lanes and bike share programs in our capitals. Data and data visualization can help us identify the needs of our communities and can support us in addressing them effectively.

    In this webinar Andy and Eva will present examples of Government using data visualization to improve services for communities and will share how you can get involved through analyzing open data and becoming part of the wider 'dataviz' community.
  • Fog Computing in Mobile Network Jun 22 2017 8:00 am UTC 45 mins
    Adnyesh Dalpati, Director Solutions Architect at Alef Mobitech
    Fog computing has the potential to resolve the issues with network latency since the media rich content can be delivered through such nodes directly.

    Fog Computing inside the mobile network providers opens up a window of revenue opportunities for MNO's and creates a innovative space in content & application delivery platform.

    Join this webinar to learn how to tackle the different challenges with fog computing and its role in the IoT cycle.
  • SAP Cloud Analytics - get control on BigData Jun 21 2017 10:00 am UTC 60 mins
    Iver van de Zand
    BigData requires processing performance but even more it requires agility of your cloud analytics. Iver will demonstrate how today's SAP BusinessObjects Cloud has leading capabilities when used in a highly complex and dynamic environment accessing extreme data volumes.
  • Big Data analytics for IoT: Making sense of data from sensors Jun 21 2017 8:00 am UTC 45 mins
    Muralidhar Somisetty, Co-founder and CTO, Innohabit
    Big data analytics is undoubtedly one of the most exciting areas in computing today, and remains an area of fast evolution. Thanks to the data deluge from millions of sensors from IoT networks, it is humanly impossible to analyse and make sense of the data from sensors without analytics tools and processes.

    In this webinar, we will go over basics of big-data analytics, how analytics is different from traditional data warehouses or business intelligence systems, different tiers of data analytics etc., We will also see different use-cases of IoT from Smart Home to Transporation to Smart City context and how analytics can be applied for various use-cases for actionable insights.

    Webinar also briefly touches upon machine learning tools / techniques that are available as-a-service on cloud today.
  • Makeover Monday: improving the way we visualize data, one chart at a time May 15 2017 3:00 pm UTC 60 mins
    Andy Kriebel, Head Coach at The Data School, Tableau Zen Master & Eva Murray Tableau Evangelist at EXASOL, Tableau Trainer
    Join Andy Kriebel and Eva Murray to hear about #MakeoverMonday, the popular social data project linking hundreds of members from the global data visualization community in an effort to create better charts and more useful data stories.

    In this webinar Andy and Eva will share how Makeover Monday not only results in thousands of better data visualizations, but also helps people find their 'voice' in the community and land their dream jobs all while becoming better analysts and story tellers.

    They will also discuss the challenge for week 20, present their own makeovers, and the design and thought process that went into them.
  • Apache Zeppelin in the Enterprise: Build, Secure & Reuse Data Pipelines w Spark May 15 2017 2:00 pm UTC 45 mins
    Eric Charles, Founder at Datalayer
    Apache Zeppelin is a great entry point for Data Scientist to explore and model Data.

    In an enterprise environment, this exploration tool can be used to assemble pipeline of notes and deploy them in a production system.

    In this webinar, you will learn how to:

    + Create functional notes corresponding to each step of the analysis.
    + Call a note from another note.
    + Pipe multiple notes together.
    + Create a deployable unit and run this unit on a remote cluster
  • Power BI Data Analytics and Visualization May 8 2017 8:00 am UTC 45 mins
    Priyanka Mane, Technology Consultant at Saviant Consulting
    Join this webinar to learn:

    1. What is Power BI?
    a. More power to Business Inteligence

    2. Why Power BI?
    a. Analytics
    b. Visualization

    3. How?
    a. Which data sources we can connect and analyse with Power BI?
    b. How to connect those data sources to Power BI?
    c. What is the role of R as a data source?
    d. How to visualise and Analyse?
    i. Design skills
    ii. Tips and tricks
    iii. Analytics patterns
    1. Custom visuals
    2. R Custom visuals (Predictive Analytics)

    4. What about Backend Management?
    a. Excel / Azure SQL / Azure Storage or Other
    b. Custom Tables / Columns / Measures
    i. What / Why / How?

    5. Power BI Updates
    a.Monthly Updates
    b. Power BI Community for queries and request/issues updates
    i. How to use this?
  • Tensorflow: Architecture and use case Recorded: Apr 21 2017 49 mins
    Gema Parreño Piqueras. AI product developer
    The webinar drives into the introduction of the architecture of Tensorflow and the designing of use case.

    You will learn:
    -What is an artificial neuron?
    -What is Tensorflow? What are its advantages? What's it used for?
    -Designing graphs in Tensorflow
    -Tips & tricks for designing neural nets
    -Use case
  • The Secrets to WINNING with Machine Learning Recorded: Apr 13 2017 39 mins
    Patrick Rice, CEO, Lumidatum
    It’s easy to get caught in the excitement of machine learning and start optimizing RMSE, AUC or recall, but machine learning success starts with aligning to the business.

    Join Patrick Rice, CEO of Lumidatum, as he leverages his decade of experience in big data analytics including implementing machine learning solutions at Amazon to outline the secrets to winning at machine learning and delivering real ROI to the business.
  • The Ways Machine Learning and AI Can Fail Recorded: Apr 13 2017 48 mins
    Brian Lange, Partner and Data Scientist, Datascope
    Good applications of machine learning and AI can be difficult to pull off. Join Brian Lange, Partner and Data Scientist at data science firm Datascope, as he discusses a variety of ways machine learning and AI can fail (from technical to human factors) so that you can avoid repeating them yourself.
  • Different Strategies of Scaling H2O Machine Learning on Apache Spark Recorded: Apr 13 2017 46 mins
    Jakub Hava, Software Engineer at H2O.ai
    Sparkling Water integrates H2O, open source distributed machine learning platform, with the capabilities of Apache Spark. It allows users to leverage H2O’s machine learning algorithms with Apache Spark applications via Scala, Python, R or H2O’s Flow GUI which makes Sparkling Water a great enterprise solution.

    Sparkling Water 2.0 was built to coincide with the release of Apache Spark 2.0 and introduces several new features. One of the latest and largest features is the ability to configure Sparkling Water for different workloads, scale and optimize the platform according to your data and needs.

    In this talk we will introduce the basic architecture of Sparkling Water, go over different scaling strategies and explain the pros and cons of each solution. We will also compare the approaches with regards to the specific use cases and provide the rationale why or why not each solution may be a good fit for the desired use case.

    This talk will finish with a live demo demonstrating the mentioned approaches and should give you a real time experience of configuring and running Sparkling Water for your use case(s)!
  • Machine Learning Is Set To Unlock The Power of Business Intelligence Recorded: Apr 13 2017 46 mins
    Boaz Farkash - Head of Product Management, Sisense and Philip Lima - CEO, Mashey
    Machine learning can identify patterns that humans tend to overlook or can’t find easily in masses of data.

    When it comes to business intelligence, machine learning brings real opportunity to:

    - Automatically uncover business insights
    - Help make products more suggestive in nature
    - Empower users to get value faster out of their BI projects
    - Reduce implementation complexities
    - Generate predictive models

    Organizations have begun to notice that by using machine learning, they are able to make new discoveries, as well as identify and solve issues faster, gaining the competitive edge over their competition.

    Join Boaz Farkash, Sisense Head of Product Management and Philip Lima, Mashey’s CEO, as they explore how Machine Learning is unlocking the power of Business Intelligence.

    To be discussed:

    - The Rise of the All-in-One Machines
    - Machine Learning is Revolutionizing Immediate Decision Making
    - The Power of Business Intelligence Bots
    - How Smart Can One Machine Be?
    - What Lies Ahead
  • Data Science in Modern Banking Recorded: Apr 13 2017 45 mins
    Charlie Leahy, Head of Software Architecture and Data Science (Hufsy)
    Banks have a vast wealth of mineable data available to them, but traditionally have provided their customers with little feedback beyond a balance and list of transactions.

    In this talk Charles Leahy, Tech Lead at Hufsy, looks at ways in which tools such as visualisation and machine learning can be employed to give users meaningful insights, helping them make the most of their money.
  • Applied Data Science Recorded: Apr 13 2017 49 mins
    Giovanni Lanzani, Chief Science Officer at GoDataDriven
    Now that the Data Science hype is levelling out, many companies are wondering what went wrong as they could not extract values from their data science efforts.

    In this webinar we will explore what does it take to apply data science and machine learning in the real world.

    Key takeaways include:
    - How can you go beyond the traditional data warehouse when doing machine learning
    - How should you adapt your processes to keep monetizing on your data
    - How to close the feedback loop between your customers and your machine learning models
    - What kind of profiles are essential to successfully become a data driven organization
  • Powering Possibilities in Machine Learning and Advanced Analytics Recorded: Apr 13 2017 49 mins
    Wim Stoop, Senior Product Marketing Manager EMEA, Cloudera, Dr. Chris Royles, Systems Engineer, Cloudera
    Machine learning is all about the data, but it's often out of reach for analytics teams working at scale. Cloudera customers such as Wargaming.net can store, process and analyse 550 million events each day to help them improve gamers’ experiences and increase their customer lifetime value.

    Whether you are new to machine learning and advanced analytics, or you already take advantage of the possibilities, this session will explore practical examples and give you some new ideas to take away. Discover how enterprise organisations can accelerate machine learning from exploration to production by empowering their data scientists with R, Python, Spark and more in one unified platform.
  • How Machine Learning Helps Predict Equipment Failure Recorded: Apr 13 2017 23 mins
    Yaroslav Nedashkovskyi, System Architect at SoftElegance
    We are going to discuss a case study on a unified data lake for the oil industry -- it is a software architecture and a set of microservices that are used to get business values from the data that are generated during the oil production. Math models were developed to make failure prediction of rod pumps during the oil artificial lifting.

    We used modern capabilities of Big Data Architecture, based on Apache Spark set of technologies, machine learning, archived data, and streaming data from wells to build a unified math model to predict failure of that kind of industrial equipment.

    Join this webinar to learn:
    -- How machine learning can help to predict failure of industrial equipment

    -- Architecture to handle near real-time data-flow from oil wells
  • How to Build Chat-Bots Using Machine Learning and NLP Recorded: Apr 12 2017 64 mins
    Dr. Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs
    Machine Learning, Artificial Intelligence and Natural Language Processing (NLP) are transforming the technological landscape in a wide range of applications. How Machine Learning frameworks have been applied in the real world continues to evolve and affect our daily lives, especially with chat bots.

    In this session Dr. Hodeghatta Rao will explain the fundamental concepts of Natural Language Processing (NLP), what are the practical applications of NLP, how machine learning is adopted to process the natural language and finally end with an overview of QnA (chat) system.
  • Semantic Natural Language Understanding w/ Spark, ML Annotators & DL Ontologies Recorded: Apr 12 2017 62 mins
    David Talby, CTO, Atigeo
    A text-mining system must go way beyond indexing and search to appear truly intelligent. First, it should understand language beyond keyword matching. (e.g. distinguishing between “Jane has the flu,” “Jane may have the flu,” “Jane is concerned about the flu," “Jane’s sister has the flu, but she doesn’t,” or “Jane had the flu when she was 9” is of critical importance.) This is a natural language processing problem. Second, it should “read between the lines” and make likely inferences even if they’re not explicitly written. (e.g. if Jane has had a fever, a headache, fatigue, and a runny nose for three days, not as part of an ongoing condition, then she likely has the flu.) This is a semisupervised ML problem. Third, it should automatically learn the right contextual inferences to make. (e.g. learning on its own that fatigue is sometimes a flu symptom—only because it appears in many diagnosed patients—without a human ever explicitly stating that rule.) This is an association-mining problem, which can be tackled via deep learning or via more guided ML techniques.

    David Talby leads a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records and provides real-time inferencing at scale. The architecture is built out of open source big data components: Kafka and Spark Streaming for real-time data ingestion and processing, Spark for modeling, and Elasticsearch for enabling low-latency access to results. The data science components include spaCy, a pipeline with custom annotators, machine-learning models for implicit inferences, and dynamic ontologies for representing and learning new relationships between concepts.

    David Talby is Atigeo’s CTO, working to evolve its big data analytics platform to solve real-world problems in healthcare, energy, and cybersecurity. David has extensive experience in building & operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams.
  • Panel: AI - The Backbone of the Modern Startup Recorded: Apr 11 2017 54 mins
    Panel: Mike Schmidt (Dovetale); Brett Kuprel (Stanford AI Lab); Vijay Nadadur (Stride.ai); Joshua Montgomery (Mycroft AI)
    Panel discussion on the advent of artificial intelligence and machine learning in startups and business.

    Panelists:
    Mike Schmidt, Co-Founder, Dovetale, Inc.
    Vijay Nadadur, Co-Founder & CEO, Stride.ai, Inc.
    Joshua Montgomery, CEO, Mycroft AI
    Brett Kuprel, Ph.D. Student, Standford AI Lab
  • AI Deep Learning for Banks Recorded: Apr 11 2017 38 mins
    Bhagvan Kommadi, CEO, Architect Corner
    As businesses begin to rely more on data-driven Artificial Intelligence applications, the new applications lead to new business issues, security, and privacy concerns. Each bank also needs to have a transparent system for total audit-ability so one can see who did what, and when. Bank can use AI Deep Learning techniques to identify erroneous or incomplete data to avoid misleading decisions.The new AI applications introduce a number of business, security and privacy issues which will have to be addressed. Neural Network, Natural Language Processing, Image Recognition, Speech Recognition and Sentimental Analysis techniques are Deep Learning techniques used in Banks and Financial Services. AI Deep Learning techniques are used to help with anti-money laundering programs, know-your-customer checks, sanctions list monitoring, billing fraud oversight or other general compliance functions, artificial intelligence can:

    - Improve efficiency
    - Weed out false-positive results
    - Reduce costs and increase profits.
    - Make better use of workers’ time and company resources
    - Help banks handle their compliance monitoring
    - Automate some legal and regulatory work
    - Handle most customer service and improve customer experience
    - Help in detection of Fraud
    - Creates a massive competitive advantage

    Bhagvan Kommadi, Founder, Architect Corner has around 20 years experience spanning in the creation of products & incubation of Product Startups. He has done Masters in Industrial Systems Engineering at Georgia Institute of Technology (1997) and Bachelors in Aerospace Engineering from Indian Institute of Technology, Madras (1993).Architect Corner is in CIO Advisor Top 25 Fast Growing AI startups in APAC for 2017. Architect Corner is part of Citi T4I Growth Accelerator.
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Make smarter moves with your big data management

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  • Title: Data Visualization Needs to Die
  • Live at: Dec 3 2014 7:00 pm
  • Presented by: Thomas Levine, Sr. Innovation Hadoop Scientist
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