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Big Data Analytics

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  • Introduction to Blockchain: Bitcoin, Ethereum, Ledgers, and more
    Introduction to Blockchain: Bitcoin, Ethereum, Ledgers, and more David Siegel, Blockchain, decentralization and business agility expert Recorded: Oct 9 2017 71 mins
    Still confused about this whole Blockchain thing? Interested in investing in digital currencies, but not sure where to start? Want to get a better idea of the threats and opportunities?

    David Siegel is a Blockchain, decentralization and business agility expert who has been a high-level management & strategy consultant to companies like Sony, Hewlett Packard, Amazon, NASA, Intel, and many start-ups. David has been praised for being able to explain Blockchain in the most simple and interesting way.

    What you will learn:
    -What is Bitcoin?
    -What is the blockchain?
    -What is Ethereum? What is Ether?
    -What is a distributed application?
    -What is a smart contract?
    -What is a triple ledger?
    -What about identity and security?
    -What business models are at risk?
    -What are the opportunities?
    -What should we do?
  • The Cognitive Bank: Leveraging Advanced Analytics and Artificial Intelligence
    The Cognitive Bank: Leveraging Advanced Analytics and Artificial Intelligence Vivek Bajaj, Global VP of Solutions for IBM Financial Services Recorded: Sep 21 2017 63 mins
    Today the payments industry faces a rebirth by necessity. Financial institutions process massive volumes of customer and payments transaction data, much of it unstructured and untapped.

    Cognitive Systems have the ability to understand, reason and learn. In Financial Services applying cognitive capabilities to real world payments issues like safer and faster payments is yielding significant results. Furthermore Risk and Compliance and segment of one engagement are areas where ROI is tremendous when leveraging advanced analytics and artificial intelligence in cohesion.

    Learn from real world use cases of how financial institutions globally have gained significant competitive advantage by becoming a truly Cognitive Bank.
  • HDFS on Kubernetes: Lessons Learned
    HDFS on Kubernetes: Lessons Learned Kimoon Kim, Pepperdata Software Engineer Recorded: Sep 19 2017 46 mins
    HDFS on Kubernetes: Lessons Learned is a webinar presentation intended for software engineers, developers, and technical leads who develop Spark applications and are interested in running Spark on Kubernetes. Pepperdata has been exploring Kubernetes as potential Big Data platform with several other companies as part of a joint open source project.

    In this webinar, Kimoon Kim will show you how to: 

    –Run Spark application natively on Kubernetes
    –Enable Spark on Kubernetes read and write data securely on HDFS protected by Kerberos
  • A Guide to Machine Learning Patterns and Data Visualizations
    A Guide to Machine Learning Patterns and Data Visualizations Dr. Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs Recorded: Aug 24 2017 55 mins
    Data visualization must be intuitive in order for non-IT business leaders to see data patterns. Representing data in a graphical or pictorial format is easy, but constructing the data in the best and most logical way can be tricky.

    In this session, Umesh will talk about how to represent data simply to make quicker and better business decisions. He will walk through several data visualization techniques through business cases and examples. By the end of the session, you will not only know different data visualization techniques, but also have an understanding of circumstances under which each technique should be used and the best way to represent particular data sets for different business cases.
  • Data Visualization for Predictive Analytics
    Data Visualization for Predictive Analytics Andy Kriebel, Eva Murray, Benedetta Tagliaferri Recorded: Aug 24 2017 58 mins
    What does the future hold?

    Predictive Analytics - everyone is talking about it and many organisations claim to be doing it. But are they? And what insights do they gain to then make tactical or strategic changes? How can analysts work with decision makers by sharing results in a visually effective and meaningful way while also informing them about possible courses of action?

    This webinar is presented by Andy Kriebel, Head Coach at the Data School and Eva Murray, Tableau Evangelist at Exasol. Our guest speaker on Predictive Analytics is Benedetta Tagliaferri, Consulting Analyst at The Information Lab.

    The webinar will look at some examples of predictive analysis and will show data visualization examples that are actionable and can drive further questions and discussions in an organisation.
  • Ask the Visualization Expert: Live Q&A on Advanced Data Visualization
    Ask the Visualization Expert: Live Q&A on Advanced Data Visualization Carl Edwards, BI Consultant, Brett Churchill, BI Consultant Recorded: Aug 24 2017 44 mins
    Looking to take your graphs to the next level? Want to make sure you choose the right visualization? Plagued by the challenges of geospatial heat maps?

    Get your questions ready and join this session where data experts Carl and Brett will go over the common questions they get asked and answer all the data visualization issues you've been plagued with, including how to:

    -Use location-based data to put your visualization on the map
    -Uncover new relationships, patterns and opportunities
    -Identify emerging trends
    -Answering comparative business questions with set analysis
    -Understand best practices for creating an aesthetically-pleasing and useful visualization
  • How Many Analysts Does It Take To Change a Lightbulb?
    How Many Analysts Does It Take To Change a Lightbulb? Steve Adams, Senior Consultant, Visual DJ, Ltd. Recorded: Aug 24 2017 49 mins
    When analysis needs to be used by decision makers that didn’t create it, the communication of the information and the message it conveys becomes critical. There is a plethora of ways to layout reports and dashboards, even within a single company.
    Enter the SUCCESS formula, that “lightbulb” moment.

    Introduced by the IBCS Association (International Business Communication Standards) the SUCCESS formula provides conceptual, perceptual and semantic rules that enable faster, better, and less-costly results in all stages of business communications and decision-making processes.

    This webinar will introduce the 7 Rules of SUCCESS that provides a toolkit to aid analysts in designing their visualisations for better reach and decisions in their target audience.

    The webinar will also introduce The Philips journey to implementing IBCS principles in their global "Accelerate!” Initiative.
  • Tensorflow machine learning library and sample of application
    Tensorflow machine learning library and sample of application Marwa Ayad Mohamed ( Founder of YourChildCode ,Team lead software Engineer, Women Techmakers Cairo Lead) Recorded: Aug 24 2017 46 mins
    Tensorflow is an open source software library for numerical computation and machine learning.

    Join this session where Marwa will discuss:

    -Introduction to Artificial intelligence, machine learning and deep learning
    -Sample of machine learning applications
    -Tensorflow Story, Model and windows installation steps with object recognition demo.
  • Artificial Intelligence: Methods, Applications and Impacts
    Artificial Intelligence: Methods, Applications and Impacts Arinze Akutekwe, PhD Data Scientist, BAS EMEIA – Intelligent Enterprise - Analytics at Fujitsu Recorded: Aug 23 2017 49 mins
    Artificial intelligence has greatly changed the way we live since the 20th century. It involves the science and engineering of making machines intelligent and autonomous using computer programs.

    The processing power of computers has been on the exponential increase with cost of processors and storage decreasing. This has made research and developments efforts in AI areas such as deep learning, once thought to be impossible possible.

    In this webinar, we will examine current methods, application domains of specific methods, their impacts on our daily lives and try to answer questions on ethics of applying these technologies.
  • Hunting Criminals with Hybrid Analytics, Semi-supervised Learning, & Feedback
    Hunting Criminals with Hybrid Analytics, Semi-supervised Learning, & Feedback David Talby, CTO, Atigeo Recorded: Aug 23 2017 62 mins
    Fraud detection is a classic adversarial analytics challenge: As soon as an automated system successfully learns to stop one scheme, fraudsters move on to attack another way. Each scheme requires looking for different signals (i.e. features) to catch; is relatively rare (one in millions for finance or e-commerce); and may take months to investigate a single case (in healthcare or tax, for example) – making quality training data scarce.

    This talk will cover a code walk-through, the key lessons learned while building such real-world software systems over the past few years. We'll look for fraud signals in public email datasets, using IPython and popular open-source libraries (scikit-learn, statsmodel, nltk, etc.) for data science and Apache Spark as the compute engine for scalable parallel processing.

    David will iteratively build a machine-learned hybrid model – combining features from different data sources and algorithmic approaches, to catch diverse aspects of suspect behavior:

    - Natural language processing: finding keywords in relevant context within unstructured text
    - Statistical NLP: sentiment analysis via supervised machine learning
    - Time series analysis: understanding daily/weekly cycles and changes in habitual behavior
    - Graph analysis: finding actions outside the usual or expected network of people
    - Heuristic rules: finding suspect actions based on past schemes or external datasets
    - Topic modeling: highlighting use of keywords outside an expected context
    - Anomaly detection: Fully unsupervised ranking of unusual behavior

    Apache Spark is used to run these models at scale – in batch mode for model training and with Spark Streaming for production use. We’ll discuss the data model, computation, and feedback workflows, as well as some tools and libraries built on top of the open-source components to enable faster experimentation, optimization, and productization of the models.

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