Although decision trees have been in development and used for over 50 years, many new forms are evolving that promise to provide exciting new capabilities in areas of Data Mining and Machine Learning.
Learn the importance and concept of Decision Tree Analysis and how one can analyse data.
This Presentation presents the benefits of Data Science for those in retail broking practice. Employing Machine Learning techniques and text analytics, you not only get that competitive edge but also earn the customer's satisfaction and loyalty
SAS Data Management for Analytics: potenzia le tue analisi e sostieni l’innov...SAS Italy
Ora più che mai le analisi di alta qualità richiedono dati di alta qualità! Con il crescente uso di molteplici e nuove fonti, come Hadoop e l'Internet of Things, i dati che fungono da combustibile per gli Analytics stanno seguendo una spirale ascendente in termini di varietà, volume e complessità nel mondo dei Big Data. Questa presentazione ti aiuterà a capire come la soluzione SAS Data Management può supportarti a migliorare la qualità dei tuoi dati e a ridurne i tempi di preparazione.
During this presentation, we will discuss how SAS can provide an open analytical platform to do artificial intelligence.
We’ll start clarifying what AI and Open mean in the context of driving business value, and we will then illustrate how SAS can support this value creation through the components of the Platform and of the Viya engine. Finally, these different elements will be demonstrated through real life examples and demos.
This Presentation presents the benefits of Data Science for those in retail broking practice. Employing Machine Learning techniques and text analytics, you not only get that competitive edge but also earn the customer's satisfaction and loyalty
SAS Data Management for Analytics: potenzia le tue analisi e sostieni l’innov...SAS Italy
Ora più che mai le analisi di alta qualità richiedono dati di alta qualità! Con il crescente uso di molteplici e nuove fonti, come Hadoop e l'Internet of Things, i dati che fungono da combustibile per gli Analytics stanno seguendo una spirale ascendente in termini di varietà, volume e complessità nel mondo dei Big Data. Questa presentazione ti aiuterà a capire come la soluzione SAS Data Management può supportarti a migliorare la qualità dei tuoi dati e a ridurne i tempi di preparazione.
During this presentation, we will discuss how SAS can provide an open analytical platform to do artificial intelligence.
We’ll start clarifying what AI and Open mean in the context of driving business value, and we will then illustrate how SAS can support this value creation through the components of the Platform and of the Viya engine. Finally, these different elements will be demonstrated through real life examples and demos.
What are the the main areas of analytics and how can they benefit your business? Learn the value of SAS analytics and how you can get better insight into your data to make more profitable decisions.
By getting a better understanding of your data you will know which part of the data can be reliably forecast using time series methods and which cannot. You will also gain an understanding of any hierarchical structure in the data that can be used.
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.
Virtual Sandbox for Data Scientists at Enterprise ScaleDenodo
View the full webinar here: https://goo.gl/rMQEQK
The Virtual Sandbox is an overarching framework to support the enterprise-scale roll out of data science programs using the industry standard, CRISP-DM methodology.
Attend this session to learn how the Virtual Sandbox optimizes analytical model generation, testing, deployment and subsequent refinement by:
• Easing data access for exploration and mash ups via a governed, self-service data access platform.
• Supporting the creation of logical views using data virtualization for reuse across the organization.
• Facilitating quick and repeatable generation of data sets for analytical model testing and refinement.
• Hastening model deployment by operationalizing the model using shared development pipelines.
Agenda:
• Review the challenges faced by enterprise-scale data science programs.
• Overview of the Virtual Sandbox and its benefits.
• Product Demonstration.
• Q&A
Introduction to Machine Learning on IBM Power SystemsDavid Spurway
My second presentation from the IBM i Premier User Group on the 20th July 2017, in IBM Hursley. This was an introduction to Machine Learning and PowerAI, IBM Power Systems pre-integrated offering that makes use of the NVIDIA GPUs and the industry unique NVLink to accelerate the learning stage of Machine Learning
Production model lifecycle management 2016 09Greg Makowski
This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
AI for RoI - How to choose the right AI solution?Abhinav Singhal
Companies looking to adopt AI today are bombarded
with technology companies and start-ups selling advanced
machine learning based solutions built on exciting use
cases. However, before kickstarting newer pilots and
investing in these advanced solutions it is useful to step
back and reflect on the overall intent of using AI for
the organization and the traditional suite of analytical
techniques and resources available.Oneway, CIOs can assess
the suitability of an AI solution is it to break it down into
simpler elements and ask five basic questions.
We all know that consumer behavior has changed dramatically. How consumers engage with companies, do research and even purchase leaves a deluge of data that companies have never had. Those companies that can parse that data drive business results like never before. This session presentation at Dog Food Con 2016 helps you to learn how Big Data technology can drive business outcomes from data ingestion to cloud and talks about one company’s journey to Customer 360 and their decision process when moving to the cloud.
Deploying Enterprise Scale Deep Learning in Actuarial Modeling at NationwideDatabricks
The traditional approach to insurance pricing involves fitting a generalized linear model (GLM) to data collected on historical claims payments and premiums received. The explosive growth in data availability and increasing competitiveness in the marketplace are challenging actuaries to find new insights in their data and make predictions with more granularity, improved speed and efficiency, and with tighter integration among business units to support strategic decisions.
In this session we will share our experience implementing deep hierarchical neural networks using TensorFlow and PySpark on Databricks. We will discuss the benefits of the ML Runtime, our experience using the goofys mount, our process for hyperparameter tuning, specific considerations for the large dataset size and extreme volatility present in insurance data, among other topics.
Authors: Bryn Clark, Krish Rajaram
SAS Clinical Trials Programmer Certification: Why SAS is the best choice for ...Aspire Techsoft Academy
If you are planning to learn SAS Certification Join the Best Clinical SAS Certification Training Institute in Pune with Placements. If you are interested in pursuing a SAS course in Pune then Aspire Techsoft - SAS Authorized Training Partner in Pune is the right place to learn SAS Certification Course. So, After Bpharm or Mpharm - If you want to pursue a career in clinical SAS then go ahead with Clinical Trials Which includes SAS Base, Advance SAS, SAS Report, SAS Macros, and Statistics1.
AUSOUG - NZOUG-GroundBreakers-Jun 2019 - AI and Machine LearningSandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. This presentation takes a view on our current state of Diagnostic methodology in the Autonomous Database Cloud services and how do we process this data to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. Some of the use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. We will cover techniques to analyze database issues using Machine learning techniques like Kmeans , TFIDF, Random Forests, and z-scores to predict if a spike in the CPU is a normal or abnormal spike. We will also talk about RNN’s with LSTM/GRU as some of the applications of how to predict faults before they happen. Some of the other use cases are to use convolution filters to determine maintenance windows within the database workloads, determine best times to do database backups, security anomaly timelines and many others. This is a production service and this can be used if you have a customer SR/defect today. The service is much more extensive inside the Oracle Autonomous Database Cloud. This presentation will accompany several examples with how to apply these techniques, machine learning knowledge is preferred but not a prerequisite
Datarobot, 자동화된 분석 적용 시 분석 절차의 변화 및 효용 - 홍운표 데이터 사이언티스트, DataRobot :: AWS Sum...Amazon Web Services Korea
스폰서 발표 세션 | Datarobot, 자동화된 분석 적용 시 분석 절차의 변화 및 효용
홍운표 데이터 사이언티스트, DataRobot
데이터로봇은 기존 분석 소프트웨어와 달리 자동화된 분석 플랫폼입니다. 현업 담당자는 데이터 정의만 완료되면 자신의 업무에 AI를 적용하여 업무 효율을 얻을 수 있고, 데이터 과학자도 기존 분석업무 대비 수십배의 효율성을 얻을 수 있습니다. 데이터로봇은 이렇게 기업 업무에 AI를 쉽게 적용하여, 비지니스 가치를 실현하도록 도와드릴 수 있습니다. 본 세션에서는 데이터로봇이 제공하는 자동화된 분석의 세부 기능을 살펴보고 제품 데모를 통해 자동화된 분석이 어떻게 분석 결과물의 품질을 높이고, 기존 분석 작업보다 훨씬 효율적인 업무를 수행할 수 있게 도와드리는지 확인하실 수 있습니다.
#askSAP Analytics Innovations Community Call: Reimagine Analytics for the Dig...SAP Analytics
http://sap.com/predictive - New digital technologies allow companies to reimagine business models, rise to disruptive market entrants and squeeze more productivity from less resources. Companies embracing digital transformation by investing in advanced analytics are winning. They create more revenue, greater market valuations, and growing profitability.
SAP BusinessObjects Predictive Analytics is a game changer in the predictive space by helping you create, deploy, and maintain thousands of predictive models that anticipate future outcomes and guide better, more profitable decision-making across your digital enterprise.
This presentation was hosted by Mr. Gautam Mehra, VP & National Head at Bajaj Capital Ltd on the topic - 'Value Based Leadership'. He emphasised on how the rules of leadership are changing in the new age. If you have any questions, please mail them at ngasce@nmims.edu
Mr. Eswaranatarajan N – Chief Operating Officer at Kotak Mahindra General Insurance, highlights the mind-set required to become a leader in today's corporate world.
The webinar talks about:
• The significance of leadership in today’s corporate world
• The importance of incorporating leadership qualities at all levels
• The qualities and core skills required to be a good leader
What are the the main areas of analytics and how can they benefit your business? Learn the value of SAS analytics and how you can get better insight into your data to make more profitable decisions.
By getting a better understanding of your data you will know which part of the data can be reliably forecast using time series methods and which cannot. You will also gain an understanding of any hierarchical structure in the data that can be used.
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.
Virtual Sandbox for Data Scientists at Enterprise ScaleDenodo
View the full webinar here: https://goo.gl/rMQEQK
The Virtual Sandbox is an overarching framework to support the enterprise-scale roll out of data science programs using the industry standard, CRISP-DM methodology.
Attend this session to learn how the Virtual Sandbox optimizes analytical model generation, testing, deployment and subsequent refinement by:
• Easing data access for exploration and mash ups via a governed, self-service data access platform.
• Supporting the creation of logical views using data virtualization for reuse across the organization.
• Facilitating quick and repeatable generation of data sets for analytical model testing and refinement.
• Hastening model deployment by operationalizing the model using shared development pipelines.
Agenda:
• Review the challenges faced by enterprise-scale data science programs.
• Overview of the Virtual Sandbox and its benefits.
• Product Demonstration.
• Q&A
Introduction to Machine Learning on IBM Power SystemsDavid Spurway
My second presentation from the IBM i Premier User Group on the 20th July 2017, in IBM Hursley. This was an introduction to Machine Learning and PowerAI, IBM Power Systems pre-integrated offering that makes use of the NVIDIA GPUs and the industry unique NVLink to accelerate the learning stage of Machine Learning
Production model lifecycle management 2016 09Greg Makowski
This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
AI for RoI - How to choose the right AI solution?Abhinav Singhal
Companies looking to adopt AI today are bombarded
with technology companies and start-ups selling advanced
machine learning based solutions built on exciting use
cases. However, before kickstarting newer pilots and
investing in these advanced solutions it is useful to step
back and reflect on the overall intent of using AI for
the organization and the traditional suite of analytical
techniques and resources available.Oneway, CIOs can assess
the suitability of an AI solution is it to break it down into
simpler elements and ask five basic questions.
We all know that consumer behavior has changed dramatically. How consumers engage with companies, do research and even purchase leaves a deluge of data that companies have never had. Those companies that can parse that data drive business results like never before. This session presentation at Dog Food Con 2016 helps you to learn how Big Data technology can drive business outcomes from data ingestion to cloud and talks about one company’s journey to Customer 360 and their decision process when moving to the cloud.
Deploying Enterprise Scale Deep Learning in Actuarial Modeling at NationwideDatabricks
The traditional approach to insurance pricing involves fitting a generalized linear model (GLM) to data collected on historical claims payments and premiums received. The explosive growth in data availability and increasing competitiveness in the marketplace are challenging actuaries to find new insights in their data and make predictions with more granularity, improved speed and efficiency, and with tighter integration among business units to support strategic decisions.
In this session we will share our experience implementing deep hierarchical neural networks using TensorFlow and PySpark on Databricks. We will discuss the benefits of the ML Runtime, our experience using the goofys mount, our process for hyperparameter tuning, specific considerations for the large dataset size and extreme volatility present in insurance data, among other topics.
Authors: Bryn Clark, Krish Rajaram
SAS Clinical Trials Programmer Certification: Why SAS is the best choice for ...Aspire Techsoft Academy
If you are planning to learn SAS Certification Join the Best Clinical SAS Certification Training Institute in Pune with Placements. If you are interested in pursuing a SAS course in Pune then Aspire Techsoft - SAS Authorized Training Partner in Pune is the right place to learn SAS Certification Course. So, After Bpharm or Mpharm - If you want to pursue a career in clinical SAS then go ahead with Clinical Trials Which includes SAS Base, Advance SAS, SAS Report, SAS Macros, and Statistics1.
AUSOUG - NZOUG-GroundBreakers-Jun 2019 - AI and Machine LearningSandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. This presentation takes a view on our current state of Diagnostic methodology in the Autonomous Database Cloud services and how do we process this data to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. Some of the use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. We will cover techniques to analyze database issues using Machine learning techniques like Kmeans , TFIDF, Random Forests, and z-scores to predict if a spike in the CPU is a normal or abnormal spike. We will also talk about RNN’s with LSTM/GRU as some of the applications of how to predict faults before they happen. Some of the other use cases are to use convolution filters to determine maintenance windows within the database workloads, determine best times to do database backups, security anomaly timelines and many others. This is a production service and this can be used if you have a customer SR/defect today. The service is much more extensive inside the Oracle Autonomous Database Cloud. This presentation will accompany several examples with how to apply these techniques, machine learning knowledge is preferred but not a prerequisite
Datarobot, 자동화된 분석 적용 시 분석 절차의 변화 및 효용 - 홍운표 데이터 사이언티스트, DataRobot :: AWS Sum...Amazon Web Services Korea
스폰서 발표 세션 | Datarobot, 자동화된 분석 적용 시 분석 절차의 변화 및 효용
홍운표 데이터 사이언티스트, DataRobot
데이터로봇은 기존 분석 소프트웨어와 달리 자동화된 분석 플랫폼입니다. 현업 담당자는 데이터 정의만 완료되면 자신의 업무에 AI를 적용하여 업무 효율을 얻을 수 있고, 데이터 과학자도 기존 분석업무 대비 수십배의 효율성을 얻을 수 있습니다. 데이터로봇은 이렇게 기업 업무에 AI를 쉽게 적용하여, 비지니스 가치를 실현하도록 도와드릴 수 있습니다. 본 세션에서는 데이터로봇이 제공하는 자동화된 분석의 세부 기능을 살펴보고 제품 데모를 통해 자동화된 분석이 어떻게 분석 결과물의 품질을 높이고, 기존 분석 작업보다 훨씬 효율적인 업무를 수행할 수 있게 도와드리는지 확인하실 수 있습니다.
#askSAP Analytics Innovations Community Call: Reimagine Analytics for the Dig...SAP Analytics
http://sap.com/predictive - New digital technologies allow companies to reimagine business models, rise to disruptive market entrants and squeeze more productivity from less resources. Companies embracing digital transformation by investing in advanced analytics are winning. They create more revenue, greater market valuations, and growing profitability.
SAP BusinessObjects Predictive Analytics is a game changer in the predictive space by helping you create, deploy, and maintain thousands of predictive models that anticipate future outcomes and guide better, more profitable decision-making across your digital enterprise.
This presentation was hosted by Mr. Gautam Mehra, VP & National Head at Bajaj Capital Ltd on the topic - 'Value Based Leadership'. He emphasised on how the rules of leadership are changing in the new age. If you have any questions, please mail them at ngasce@nmims.edu
Mr. Eswaranatarajan N – Chief Operating Officer at Kotak Mahindra General Insurance, highlights the mind-set required to become a leader in today's corporate world.
The webinar talks about:
• The significance of leadership in today’s corporate world
• The importance of incorporating leadership qualities at all levels
• The qualities and core skills required to be a good leader
‘Managing People Change in the Digital Era’ with Mr. Manoj Prasad, V.P. – Digital Transformation & People Change, Reliance Industries. He will be discussing why Digital Transformation is important in a VUCA (Volatile, Uncertain, Complex and Ambiguous) world, change management and the key to adopt a digital mind-set.
About Manoj Prasad: An HR Industry veteran with more than 21+ years of global work experience, he has been managing end to end complex HR Transformation & Organization Change program across globe. He is currently leading the recreate, redesign & transform e-digital platform initiatives across Reliance.
Facebook- https://www.facebook.com/NMIMSSCE/
Twitter- https://twitter.com/NMIMS_SCE
LinkedIn- https://www.linkedin.com/school/nmims/
Find out how ‘Dynamic Organisational Structure’ is redefining the way team dynamics are evolving in Indian companies.
Executive Coach and a People Transformation Leader, Dipankar Ghosh, shares his insights on the topic in this eye-opening session.
Speaker: Dipankar Ghosh, Chief Human Resources Officer at Bajaj Corp Ltd
Investor behaviour continues to differ around the country, impacting strategies and returns on the financial markets.
Learn the different types of asset classes, the advantages of Equity as an asset class and the Cultural Shifts and how one can benefit from it with the MD and CEO of Axis Securities, Mr. Varun Thukral.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas