Choosing a data visualization tool is like being a barista serving coffee: everyone wants their data, their way, personalized, fast, and perfect. Many organizations have a cottage industry of data visualization tools, and it's difficult to know what tool to use, and when. Different tools exist in different departments, and if it doesn't meet the user requirements, the default position is to go back to Excel and move the data around there.
This session will examine data visualization tools such as SSRS Excel, Tableau, QlikView, Datazen, Kibana and PowerBI, in order to craft and blend your data visualization tools to serve your data customers better.
Business Intelligence Barista: What DataViz Tool to Use, and When?Jen Stirrup
Choosing a data visualization tool is like being a barista serving coffee: everyone wants their data, their way, personalized, fast, and perfect. Many organizations have a cottage industry of data visualization tools, and it's difficult to know what tool to use, and when. Different tools exist in different departments, and if it doesn't meet the user requirements, the default position is to go back to Excel and move the data around there.
This session will examine data visualization tools such as SSRS Excel, Tableau, QlikView, Datazen, Kibana and PowerBI, in order to craft and blend your data visualization tools to serve your data customers better.
Guidelines for data visualisation: eye vegetables and eye candyJen Stirrup
What's your data visualization vegetables? What's your candy? This session will look at data visualization theory and practice of hot data visualization topics such as: how can you choose which chart to choose and when?
How can you best structure your dashboard?
What about pie charts? What is the fuss about, and when are they best used?
Color blindness - how can you cater for the 1 out of 12 color blind males (and not forgetting the 1 out of 100 color blind females?)
To 3D or not to 3D? Why is it missing in Power View? And any other data visualization topics you care to mention! Come along for dataviz fun, and to learn the "why" along with practical advice.
SQLBits Module 2 RStats Introduction to R and StatisticsJen Stirrup
SQLBits Module 2 RStats Introduction to R and Statistics. This is a 90 minute segment of a full preconference workshop, focusing on data analytics with R.
PASS Summit Data Storytelling with R Power BI and AzureMLJen Stirrup
How can we use technology to help the organization make data-driven decision-making part of its organizational DNA, while retaining the context of the business as a whole? How can we imprint data in the culture of the organization and make it easily accessible to everyone? Microsoft directly empowers businesses to derive insights and value from little and big data, through its release of user-friendly analytics through Azure Machine Learning (ML) combined with its acquisition of Revolution Analytics. Power BI can be used to create compelling visual stories around the analysis so that the work is not left to the data consumer. Together, these technologies can be used to make data and analytics part of the organization's DNA.
There are no prerequisites, but attendees are welcome to follow along with the demo if they have an Azure ML and Power BI account and R installed. Files will be released before the session.
Sql rally amsterdam Aanalysing data with Power BI and HiveJen Stirrup
Analyzing Data with Power View (Level 100)
Jen Stirrup
Come learn about the best ways to present data to your Business Intelligence data consumers, and see how to apply these principles in Power View, Microsoft's data visualization tool. Using demos, we will investigate Power View based on current cognitive research around data visualization principles from such experts as Stephen Few, Edware Tufte, and others. We will then examine how data can be analyzed with Power View and look at where Power View is supplemented by other parts of the Microsoft Business Intelligence stack.
Data Visualisation with Hadoop Mashups, Hive, Power BI and Excel 2013Jen Stirrup
This session focused on data visualisation using Power BI, based on big data. Some examples of Hive and HDFS file storage are given. An overview of Microsoft HDInsight is supplied.
Data Culture Series - Keynote - 16th September 2014Jonathan Woodward
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
Drive a Data Culture within your organisation
Business Intelligence Barista: What DataViz Tool to Use, and When?Jen Stirrup
Choosing a data visualization tool is like being a barista serving coffee: everyone wants their data, their way, personalized, fast, and perfect. Many organizations have a cottage industry of data visualization tools, and it's difficult to know what tool to use, and when. Different tools exist in different departments, and if it doesn't meet the user requirements, the default position is to go back to Excel and move the data around there.
This session will examine data visualization tools such as SSRS Excel, Tableau, QlikView, Datazen, Kibana and PowerBI, in order to craft and blend your data visualization tools to serve your data customers better.
Guidelines for data visualisation: eye vegetables and eye candyJen Stirrup
What's your data visualization vegetables? What's your candy? This session will look at data visualization theory and practice of hot data visualization topics such as: how can you choose which chart to choose and when?
How can you best structure your dashboard?
What about pie charts? What is the fuss about, and when are they best used?
Color blindness - how can you cater for the 1 out of 12 color blind males (and not forgetting the 1 out of 100 color blind females?)
To 3D or not to 3D? Why is it missing in Power View? And any other data visualization topics you care to mention! Come along for dataviz fun, and to learn the "why" along with practical advice.
SQLBits Module 2 RStats Introduction to R and StatisticsJen Stirrup
SQLBits Module 2 RStats Introduction to R and Statistics. This is a 90 minute segment of a full preconference workshop, focusing on data analytics with R.
PASS Summit Data Storytelling with R Power BI and AzureMLJen Stirrup
How can we use technology to help the organization make data-driven decision-making part of its organizational DNA, while retaining the context of the business as a whole? How can we imprint data in the culture of the organization and make it easily accessible to everyone? Microsoft directly empowers businesses to derive insights and value from little and big data, through its release of user-friendly analytics through Azure Machine Learning (ML) combined with its acquisition of Revolution Analytics. Power BI can be used to create compelling visual stories around the analysis so that the work is not left to the data consumer. Together, these technologies can be used to make data and analytics part of the organization's DNA.
There are no prerequisites, but attendees are welcome to follow along with the demo if they have an Azure ML and Power BI account and R installed. Files will be released before the session.
Sql rally amsterdam Aanalysing data with Power BI and HiveJen Stirrup
Analyzing Data with Power View (Level 100)
Jen Stirrup
Come learn about the best ways to present data to your Business Intelligence data consumers, and see how to apply these principles in Power View, Microsoft's data visualization tool. Using demos, we will investigate Power View based on current cognitive research around data visualization principles from such experts as Stephen Few, Edware Tufte, and others. We will then examine how data can be analyzed with Power View and look at where Power View is supplemented by other parts of the Microsoft Business Intelligence stack.
Data Visualisation with Hadoop Mashups, Hive, Power BI and Excel 2013Jen Stirrup
This session focused on data visualisation using Power BI, based on big data. Some examples of Hive and HDFS file storage are given. An overview of Microsoft HDInsight is supplied.
Data Culture Series - Keynote - 16th September 2014Jonathan Woodward
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
Drive a Data Culture within your organisation
Synapse is a solution provider with an innovative alternative to commercial off-the-shelf IT applications. Empowering business professionals to shape business processes without being chained to IT applications.
Kyvos Insights is unlocking the power of Big Data analytics with “OLAP on Hadoop” technology.
Kyvos is a solution which brings a new model of online analytical processing (OLAP) to Big Data that allows users to visually create and analyze cubes on Hadoop. This technology enables users to easily derive valuable insights for better, more informed business decisions through previously unattainable levels of scalability and interactivity.
Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and An...Benjamin Nussbaum
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down.
Connected data is the key to your business succeeding and growing in today’s connected world.
Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide:
- Increased visibility between internal groups
- Efficiency gains
- Cross-functional data collaboration
- Core complete and reliable business insights
- Better customer engagement
The live presentation and discussion can be found here: https://youtu.be/7vBdlXzhs_4
Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connected-data-is-more-useful/
Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com
Over 90% of today’s data has been generated in the last two years, and growth rates continue to climb. In this session, we’ll step through challenges and best practices with data capturing, how to derive meaningful insights to help predict the future, and common pitfalls in data analysis.
Come discover how integrated solutions involving Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning/Deep Learning result in effective data systems for data scientists and business users, alike.
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
Data Con LA 2019 - Big Data Modeling with Spark SQL: Make data valuable by Ja...Data Con LA
In this Data age, business applications generate big data. To generate value out of large scale data applications, data models are the key. Data models serve various purposes, and it is essential to show reliable insights in a timely fashion. This session will cover the technical aspect of leveraging Spark's distributed engine to process Big data to generate insights. It includes a few aspects to optimize processes with Spark SQL. Come join me to explore the process of making data interesting!
The Rise of the DataOps - Dataiku - J On the Beach 2016 Dataiku
Many organisations are creating groups dedicated to data. These groups have many names : Data Team, Data Labs, Analytics Teams….
But whatever the name, the success of those teams depends a lot on the quality of the data infrastructure and their ability to actually deploy data science applications in production.
In that regards a new role of “DataOps” is emerging. Similar, to Dev Ops for (Web) Dev, the Data Ops is a merge between a data engineer and a platform administrator. Well versed in cluster administration and optimisation, a data ops would have also a perspective on the quality of data quality and the relevance of predictive models.
Do you want to be a Data Ops ? We’ll discuss its role and challenges during this talk
Visually transform and blend your data in Hadoop. Kyvos is disrupting the Business Intelligence and Big Data Analytics market. Kyvos has built an unprecedented OLAP-on-Hadoop technology that is massively scalable and responds to queries in record-short time in the order of single-digit seconds.
Here are some of the things our Data Analytics team can doLoren Moss
Using tools like Alteryx, AWS Quicksight, and methods such as RegEx, JSON, Python, SQL and SPARQL we can help extract the knowledge hidden in your data. www.unidodigital.com
Restructuring Technical Debt - A Software and System Quality ApproachAdnan Masood
Agile Software Architecture based overview of the technical debt metaphor … idea is that developers sometimes accept compromises in a system in one dimension (e.g., modularity) to meet an urgent demand in some other dimension (e.g., a deadline), and that such compromises incur a "debt": on which "interest" has to be paid and which the "principal" should be repaid at some point for the long-term health of the project. (ACM)
Synapse is a solution provider with an innovative alternative to commercial off-the-shelf IT applications. Empowering business professionals to shape business processes without being chained to IT applications.
Kyvos Insights is unlocking the power of Big Data analytics with “OLAP on Hadoop” technology.
Kyvos is a solution which brings a new model of online analytical processing (OLAP) to Big Data that allows users to visually create and analyze cubes on Hadoop. This technology enables users to easily derive valuable insights for better, more informed business decisions through previously unattainable levels of scalability and interactivity.
Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and An...Benjamin Nussbaum
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down.
Connected data is the key to your business succeeding and growing in today’s connected world.
Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide:
- Increased visibility between internal groups
- Efficiency gains
- Cross-functional data collaboration
- Core complete and reliable business insights
- Better customer engagement
The live presentation and discussion can be found here: https://youtu.be/7vBdlXzhs_4
Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connected-data-is-more-useful/
Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com
Over 90% of today’s data has been generated in the last two years, and growth rates continue to climb. In this session, we’ll step through challenges and best practices with data capturing, how to derive meaningful insights to help predict the future, and common pitfalls in data analysis.
Come discover how integrated solutions involving Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning/Deep Learning result in effective data systems for data scientists and business users, alike.
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
Data Con LA 2019 - Big Data Modeling with Spark SQL: Make data valuable by Ja...Data Con LA
In this Data age, business applications generate big data. To generate value out of large scale data applications, data models are the key. Data models serve various purposes, and it is essential to show reliable insights in a timely fashion. This session will cover the technical aspect of leveraging Spark's distributed engine to process Big data to generate insights. It includes a few aspects to optimize processes with Spark SQL. Come join me to explore the process of making data interesting!
The Rise of the DataOps - Dataiku - J On the Beach 2016 Dataiku
Many organisations are creating groups dedicated to data. These groups have many names : Data Team, Data Labs, Analytics Teams….
But whatever the name, the success of those teams depends a lot on the quality of the data infrastructure and their ability to actually deploy data science applications in production.
In that regards a new role of “DataOps” is emerging. Similar, to Dev Ops for (Web) Dev, the Data Ops is a merge between a data engineer and a platform administrator. Well versed in cluster administration and optimisation, a data ops would have also a perspective on the quality of data quality and the relevance of predictive models.
Do you want to be a Data Ops ? We’ll discuss its role and challenges during this talk
Visually transform and blend your data in Hadoop. Kyvos is disrupting the Business Intelligence and Big Data Analytics market. Kyvos has built an unprecedented OLAP-on-Hadoop technology that is massively scalable and responds to queries in record-short time in the order of single-digit seconds.
Here are some of the things our Data Analytics team can doLoren Moss
Using tools like Alteryx, AWS Quicksight, and methods such as RegEx, JSON, Python, SQL and SPARQL we can help extract the knowledge hidden in your data. www.unidodigital.com
Restructuring Technical Debt - A Software and System Quality ApproachAdnan Masood
Agile Software Architecture based overview of the technical debt metaphor … idea is that developers sometimes accept compromises in a system in one dimension (e.g., modularity) to meet an urgent demand in some other dimension (e.g., a deadline), and that such compromises incur a "debt": on which "interest" has to be paid and which the "principal" should be repaid at some point for the long-term health of the project. (ACM)
Digital Pragmatism with Business Intelligence, Big Data and Data VisualisationJen Stirrup
Contact details:
Jen.Stirrup@datarelish.com
In a world where the HiPPO’s (Highest Paid Person’s Opinion) is final, how can we use technology to drive the organisation towards data-driven decision making as part of their organizational DNA? R provides a range of functionality in machine learning, but we need to expose its richness in a world where it is made accessible to decision makers. Using Data Storytelling with R, we can imprint data in the culture of the organization by making it easily accessible to everyone, including decision makers. Together, the insights and process of machine learning are combined with data visualisation to help organisations derive value and insights from big and little data.
System Quality Attributes for Software ArchitectureAdnan Masood
Software Quality Attributes are the benchmarks that describe system’s intended behavior. These slides go through an overview of what some of these attributes are and how to evaluate them.
How Universities Use Big Data to Transform EducationHortonworks
Student performance data is increasingly being captured as part of software-based and online classroom exercises and testing. This data can be augmented with behavioral data captured from sources such as social media, student-professor meeting notes, blogs, student surveys, and so forth to discover new insights to improve student learning. The results transcend traditional IT departments to focus on issues like retention, research, and the delivery of content and courses through new modalities.
Hortonworks is partnering with Microsoft to show you how the Hortonworks Data Platform (HDP) running on the Microsoft stack enables you to develop a “single view of a student”.
Intorducing Big Data and Microsoft AzureKhalid Salama
The purpose of these slides is to give a high-level overview of Big Data concepts and techniques, as well as its related tools and technologies, focusing on Microsoft Azure. It starts by defining what Big Data is, as well as why Big Data platforms are needed. Fundamental components of a Big Data Platform are discussed, followed by a little bit of theory about Distributed Processing & CAP Theorem, and its relevance to how Big Data Solutions compare to Traditional RDBMS. Use case of how Big Data fits in Enterprise Data Platforms are shown. The Hadoop Ecosystem is briefly reviewed before Big Data on Microsoft Azure is discussed. Then some directions of How to get started with Big Data.
How to Use Apache Zeppelin with HWX HDBHortonworks
Part five in a five-part series, this webcast will be a demonstration of the integration of Apache Zeppelin and Pivotal HDB. Apache Zeppelin is a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more. This webinar will demonstrate the configuration of the psql interpreter and the basic operations of Apache Zeppelin when used in conjunction with Hortonworks HDB.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Dynamic Column Masking and Row-Level Filtering in HDPHortonworks
As enterprises around the world bring more of their sensitive data into Hadoop data lakes, balancing the need for democratization of access to data without sacrificing strong security principles becomes paramount. In this webinar, Srikanth Venkat, director of product management for security & governance will demonstrate two new data protection capabilities in Apache Ranger – dynamic column masking and row level filtering of data stored in Apache Hive. These features have been introduced as part of HDP 2.5 platform release.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks
How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services?
Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business.
Join Hortonworks and Informatica as we discuss:
- What is a data lake?
- The modern data architecture for a data lake
- How Hadoop fits into the modern data architecture
- Innovative use-cases for a data lake
Data science with Windows Azure - A Brief IntroductionAdnan Masood
Data Science with Windows Azure is an introduction to HDInsight and Hadoop offerings from Microsoft Machine Learning and Big Data Cloud based platform. This was presented at Microsoft Data Science Group – Tampa Analytics Professionals.
Spark with Azure HDInsight - Tampa Bay Data Science - Adnan Masood, PhDAdnan Masood
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
In this presentation we discuss Microsoft HDInsight offering of Spark. Azure HDInsight, Microsoft’s managed Hadoop and Spark cloud service that runs the Hortonworks Data Platform. Spark for Azure HDInsight offers customers an enterprise-ready Spark solution that’s fully managed, secured, and highly available and made simpler for users with compelling and interactive experiences.
This presentation was given to the Tech Change Technology for Monitoring and Evaluation Diploma course on 25th September 2015. It covers:
Why visualise data?
Where to start?
Which tools to use?
It ends with an overview of Kwantu's approach to this area and the technology choices that we've made.
The New Frontier: Optimizing Big Data ExplorationInside Analysis
The Briefing Room with Dr. Robin Bloor and Cirro
Live Webcast on February 11, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=0ec1fa381886313cc06d841015c65898
As information ecosystems continue to expand, businesses are searching for ways to combine traditional analytics with a new source of insight: Big Data. But with data flooding in from all kinds of sources, fast access and performance at scale can easily become an issue. One effective approach for solving this challenge is data federation, a method that involves taking the analytical processing to the data, allowing streamlined access to multiple data sources without the expensive ETL overhead or building of semantic layers.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains how the prevalence of distributed data calls for a new approach to Big Data. He will be briefed by Mark Theissen of Cirro, who will tout his company’s Data Hub, a data federation solution that provides a single point of access to all enterprise data assets without excessive data movements, preprocessing or staging. He will discuss how data federation differs from virtualization and ETL approaches, and demonstrate how a Cirro deployment solves the analytics challenge of integrating data silos across the data center – and the cloud – using the BI tools you already have on your desktop for real-time distributed analytics.
Visit InsideAnlaysis.com for more information.
Education Analytics solution based on open source including KPIs, reports, OLAP Analysis, Dashboards, Scorecards, Big Data and Machine Learning with 'predefined templates, dashboards and KPIs/ratios' and fully customizable environment
This is a Powerpoint Presentation based on the comparison of various available analytical tools. This includes various tools for business analytics and their detailed description.
Data Visualization - UC Analytics Conference 2018Russell Spangler
Data visualization presentation provided during the University of Cincinnati Data and Analytics conference. Discusses how to build out a data visualization group from scratch.
Riga dev day 2016 adding a data reservoir and oracle bdd to extend your ora...Mark Rittman
This talk focus is on what a data reservoir is, how it related to the RDBMS DW, and how Big Data Discovery provides access to it to business and BI users
Public Administration Analytics solution based on open source including KPIs, reports, OLAP Analysis, Dashboards, Scorecards, Big Data and Machine Learning with 'predefined templates, dashboards and KPIs/ratios' and fully customizable environment
Telco and IT Analytics solution based on open source including KPIs, reports, OLAP Analysis, Dashboards, Scorecards, Big Data and Machine Learning with 'predefined templates, dashboards and KPIs/ratios' and fully customizable environment
Pharma Analytics solution based on open source including KPIs, reports, OLAP Analysis, Dashboards, Scorecards, Big Data and Machine Learning with 'predefined templates, dashboards and KPIs/ratios' and fully customizable environment
Human Resources Analytics solution based on open source including KPIs, reports, OLAP Analysis, Dashboards, Scorecards, Big Data and Machine Learning with 'predefined templates, dashboards and KPIs/ratios' and fully customizable environment
Jen Vaughan will walk you through readying yourself to apply for jobs using Tableau. From what to look for in a candidate, resume and how to gain a competitive edge.
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
A design system can vastly improve your team's productivity, but most of all, it leads to better products! The challenge lies in creating a mature system and leading its adoption across the company successfully. Let's talk about how we learned to meet the needs of different designers and developers on different products, on different tech stacks, on different platforms. Attendees will go home with tips they can use to improve design systems of any stage.
Similar to Business Intelligence Barista: What DataViz Tool to Use, and When? (20)
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
AI Applications in Healthcare and Medicine.pdfJen Stirrup
This session was delivered for the Global Business Roundtable. The topic: AI applications in Healthcare and Medicine. In this session, Jennifer Stirrup takes people through a general process of adopting AI in their organisations.
BUILDING A STRONG FOUNDATION FOR SUCCESS WITH BI AND DIGITAL TRANSFORMATIONJen Stirrup
The objective of Digital Transformation is improve the quality and resilience of digital services to serve customers better, and data is a cruel part of fulfilling that ambition. As the organisation moves forward in pursuit of its strategic ambitions, it will need to remain focused on the stabilisation and improvement of existing technology and data foundations. To succeed, organisations need continuously strive to improve data, systems and processes for people using digital solutions; it is not simply digitising paper processes. The challenge of digital transformation is to work with people, but how can you build systems that serve them well to achieve and deliver more in a customer-focused way? Innovators will relish the opportunity to adopt new technology, but laggars are often waiting for proof that this will help them deliver better services or products. The challenge is that the adoption of digital solutions varies significantly from one person to the next, one team to the next and one organisation to the next. In this keynote, there will be a discussion of the industry landscape followed by takeaways that will help digital transformation in your organization.
1. Do more than get the basics right
2. Build confidence in changes through better use of data
3. How to oversee delivery while considering strategy
CuRious about R in Power BI? End to end R in Power BI for beginners Jen Stirrup
In this session, we will start R right from the beginning, from installing R through to datatransformation and integration, through to visualizing data by using R in PowerBI. Then, we will move towards powerful but simple to use datatypes in R such as data frames. We will also upgrade our data analysis skills by looking at Rdata transformation using a powerful set of tools to make things simple: the tidyverse. Then, we will look at integrating our R work into Power BI, and visualizing our data using beautiful visualizations with R and Power BI. Finally, we will share our work by publishing our Power BI project, with our R code, to the Power BI service. We will also look at refreshing our dataset so that our new dashboard has refreshed data.
This session is aimed at getting beginners up to speed as gently and quickly as possible. Join this session if you are curious about R and want to know more. If you are already a Power BI expert, join this session to open up a whole new world of Power BI to add toyour skill set. If you are new to Power BI, you will still get value from this session since you'll be able to see a Power BI dashboard being built in an end-to-end solution.
Artificial Intelligence Ethics keynote: With Great Power, comes Great Respons...Jen Stirrup
Artificial Intelligence has been receiving some bad press recently, with respect to its ethical consequences in terms of changes to working conditions, deepfake technology and even job losses. Organizations are concerned about bias in their data, perpetuating stereotypes and neglecting responsibility. How can AI systems treat all people fairly? What about concerns of safety and reliability?
In this keynote, we will explore the toolkits available in Azure to help businesses to navigate the complex ethics environment. Join this session to understand what Microsoft can offer in terms of supporting organisations to consider ethics as an integral part of their AI solutions.
1 Introduction to Microsoft data platform analytics for releaseJen Stirrup
Part 1 of a conference workshop. This forms the morning session, which looks at moving from Business Intelligence to Analytics.
Topics Covered: Azure Data Explorer, Azure Data Factory, Azure Synapse Analytics, Event Hubs, HDInsight, Big Data
Comparing Microsoft Big Data Platform TechnologiesJen Stirrup
In this segment, we look at technologies such as HDInsight, Azure Databricks, Azure Data Lake Analytics and Apache Spark. We compare the technologies to help you to decide the best technology for your situation.
Introduction to Analytics with Azure Notebooks and PythonJen Stirrup
Introduction to Analytics with Azure Notebooks and Python for Data Science and Business Intelligence. This is one part of a full day workshop on moving from BI to Analytics
When looking at Sales Analytics, where should you start? What should you measure? This session provides ideas on sales metrics, implemented in Power BI
Diversity and inclusion for the newbies and doersJen Stirrup
This presentation is aimed at people who want to *do* something positive for diversity and inclusion in their workplaces and communities, but don't know where to start to have a quick impact. I've made up a checklist of 7 'E's to help people along. We cover crucial topics such as: • What can we do to tackle unconscious bias in our systems, solutions and interactions with others? • How can we be more inclusive towards others? • How can we encourage and mentor younger generations to get involved in STEM topics and technical roles both as leaders and in the communities of people who surround us? I hope you enjoy this interactive and thought-provoking discussion of diversity and inclusion, aimed at people who want to get started and do something positive and impactful to help others.
Artificial Intelligence from the Business perspectiveJen Stirrup
What is AI from the Business perspective? In this presentation, Jen Stirrup discusses the 8 'C's of Artificial Intelligence from the business leadership perspective.
How to be successful with Artificial Intelligence - from small to successJen Stirrup
Keynote from AI World Congress in October 2019. Artificial Intelligence isn't just for the technies; it is crucial that business-oriented individuals adopt this technology, which can be conceived as the fourth industrial age. Artificial intelligence is becoming closer to being a a part of our daily lives through the use of technologies like virtual assistants such as Alexa, smart homes, and automated customer service. Now, we are running the race not just to win, but to survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and futurist ideas are developing into reality at accelerated rates.
How can you help your your company to evolve, adapt and succeed using Artificial Intelligence to stay at the forefront of the competition, and win the race for AI adoption in your organization? What are the potential issues, complications and benefits that artificial intelligence could bring to us and our organisations? In this session, Jen Stirrup will explain the quick wins to win the Red Queen's Race in Artificial Intelligence.
Artificial Intelligence: Winning the Red Queen’s Race Keynote at ESPC with Je...Jen Stirrup
Artificial Intelligence is popularised in fiction films such as “The Terminator” and “AI: Artificial Intelligence”. Now, artificial intelligence is becoming closer to being a part of our daily lives through the use of technologies like virtual assistants such as Cortana, smart homes, and automated customer service.
Now, we are running the Red Queen’s race not just to win, but to survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and futurist ideas are developing into reality at accelerated rates.
How can you help your your company to evolve, adapt and succeed using Artificial Intelligence to stay at the forefront of the competition, and win the Red Queen’s Race? What are the potential issues, complications and benefits that artificial intelligence could bring to us and our organisations?
In this keynote, Jen Stirrup explains the quick wins to win the Red Queen’s Race, using demos from Microsoft technologies such as AutoML to help you and your organisation win the Red Queen’s race.
Data Visualization dataviz superpower! Guidelines on using best practice data visualization principles for Power BI, Excel, SSRS, Tableau and other great tools!
R - what do the numbers mean? #RStats This is the presentation for my Demo at Orlando Live60 AILIve. We go through statistics interpretation with examples
Artificial Intelligence and Deep Learning in Azure, CNTK and TensorflowJen Stirrup
Artificial Intelligence and Deep Learning in Azure, using Open Source technologies CNTK and Tensorflow. The tutorial can be found on GitHub here: https://github.com/Microsoft/CNTK/tree/master/Tutorials
and the CNTK video can be found here: https://youtu.be/qgwaP43ZIwA
Blockchain Demystified for Business Intelligence ProfessionalsJen Stirrup
Blockchain is a transformational technology with the potential to extend digital transformation beyond an organization and into the processes it shares with suppliers, customers, and partners.
What is blockchain? What can it do for my organization? How can your organisation manage a blockchain implementation? How does it work in Azure?
Join this session to learn about blockchain and see it in action. We will also discuss the use cases for blockchain, and whether it is here to stay.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
23. Why R?
• most widely used data analysis software - used by 2M + data scientist,
statisticians and analysts
• Most powerful statistical programming language
• flexible, extensible and comprehensive for productivity
• Create beautiful and unique data visualisations - as seen in New York Times,
Twitter and Flowing Data
• Thriving open-source community - leading edge of analytics research
• Fills the talent gap - new graduates prefer R.
24. Growth in Demand
• Rexer Data Mining survey, 2007 - 2013
• R is the highest paid IT skill Dice.com, Jan 2014
• R most used-data science language after SQL -
O'Reilly, Jan 2014
• R is used by 70% of data miners. Rexer, Sept 2013
25. Growth in Demand
• R is #15 of all programming languages. REdMonk, Jan
2014
• R growing faster than any other data science language.
KDNuggs.
• R is in-memory and limited in the size of data that you
can process.
26. What do I need to install?
• Install R – www.r-project.org
• Install Rstudio – www.rstudio.com
• Handy Shortcuts
• Tab – autocomplete of available functions
• Control and Up Arrow – History
• Control and enter – executes the line of code
27. What tools do we have in R?
• 80% of your time will be spent preparing and wrangling data
• The remainder of your time will be spent complaining about it.
• dplyr: the essential data manipulation toolset
• In data wrangling, what are the main tasks?
• – Filtering rows
– Selecting columns of data
– Adding new variables
– Sorting
– Aggregating
36. Kibana
• It is highly customizable dashboarding
• It is constituted of panels:
– Time picker / Query / Filtering
– Charts / Table / Text
37. Flexible analytics and visualization platform
Real-time summary and charting of streaming
data
Intuitive interface for a variety of users
Instant sharing and embedding of dashboards
38. To better understand large volumes of data..
• easily create bar charts
• line and scatter plots
• Histograms
• pie charts
• maps.
39. To better understand large volumes of data..
• easily create bar charts
• line and scatter plots
• Histograms
• pie charts
• maps.
We will look at: introductory R and why it's useful, and where to go for more information. We will learn statistics and R by looking at: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. We will loosely base the curriculum on the Khan Academy statistics course, but we aim to help the curious, the scared, and the rookie.
Effective: the viewer gets it (ease of interpretation)
Accurate: sufficient for correct quantitative evaluation. Lie factor = size of visual effect/size of data effect
Efficient: minimize data-ink ratio and chart-junk, show data, maximize data-ink ratio, brase non-data-ink, brase redundant data-ink
Aesthetics: must not offend viewer's senses (e.g. moire patterns)
Adaptable: can adjust to serve multiple needs
Effective: the viewer gets it (ease of interpretation)
Accurate: sufficient for correct quantitative evaluation. Lie factor = size of visual effect/size of data effect
Efficient: minimize data-ink ratio and chart-junk, show data, maximize data-ink ratio, brase non-data-ink, brase redundant data-ink
Aesthetics: must not offend viewer's senses (e.g. moire patterns)
Adaptable: can adjust to serve multiple needs