The document summarizes Dr. Brand Niemann's presentation at the 2012 International Open Government Data Conference. It discusses open data principles and provides an example using EPA data. It also describes Niemann's beautiful spreadsheet dashboard for EPA metadata and APIs. Finally, it outlines Niemann's data science analytics approach for the conference, including knowledge bases, data catalog, and using business intelligence tools to analyze linked open government data.
In this presentation, Wes Eldridge will provide a general overview on data science. The talk will cover a variety of topics, Wes will start with the dirty history of the field which will help add context. After learning about the history of data and data science Wes will discuss the common roles a data scientist holds in business and organizations. Next, he will talk about how to use data in your organization and products. Finally, he'll cover some tools to help you get started in data science. After the presentation, Wes will stick around for Q/A and data discussion.
Everybody has heard of Big Data, and its promise as the next great frontier for innovation. However, Big Data is neither new nor easily defined. What are the key drivers that make Big Data so critically important today? What is the single idea behind Big Data that promises such game changing outcomes for capable organizations? Who are the skilled talent that deliver Big Data results?
This presentation briefly reviews the opportunities, motivation and trends that are driving Big Data disruption. Data science is introduced as the enabling engine for Big Data transformation via the creation of new Data Products. The data scientist is defined and his tools, workflow and challenges are reviewed. Finally, practical tips are presented for approaching data product development.
Key takeaways include:
- Big Data disruption is driven by four megatrends
- Data is the essential raw material for creating valuable Data Products
- Data scientists are heterogeneous by role & skill set, but share common tools, workflows and challenges
- Data science talent is more important than raw data for Big Data success
These slides are modified from an invited presentation for the Gwinnett Chamber of Commerce on March 18, 2014. An excerpt was presented at the Georgia Pacific Social Media Working Session on March 19, 2014.
What is Big Data? What is Data Science? What are the benefits? How will they evolve in my organisation?
Built around the premise that the investment in big data is far less than the cost of not having it, this presentation made at a tech media industry event, this presentation will unveil and explore the nuances of Big Data and Data Science and their synergy forming Big Data Science. It highlights the benefits of investing in it and defines a path to their evolution within most organisations.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
In this presentation, Wes Eldridge will provide a general overview on data science. The talk will cover a variety of topics, Wes will start with the dirty history of the field which will help add context. After learning about the history of data and data science Wes will discuss the common roles a data scientist holds in business and organizations. Next, he will talk about how to use data in your organization and products. Finally, he'll cover some tools to help you get started in data science. After the presentation, Wes will stick around for Q/A and data discussion.
Everybody has heard of Big Data, and its promise as the next great frontier for innovation. However, Big Data is neither new nor easily defined. What are the key drivers that make Big Data so critically important today? What is the single idea behind Big Data that promises such game changing outcomes for capable organizations? Who are the skilled talent that deliver Big Data results?
This presentation briefly reviews the opportunities, motivation and trends that are driving Big Data disruption. Data science is introduced as the enabling engine for Big Data transformation via the creation of new Data Products. The data scientist is defined and his tools, workflow and challenges are reviewed. Finally, practical tips are presented for approaching data product development.
Key takeaways include:
- Big Data disruption is driven by four megatrends
- Data is the essential raw material for creating valuable Data Products
- Data scientists are heterogeneous by role & skill set, but share common tools, workflows and challenges
- Data science talent is more important than raw data for Big Data success
These slides are modified from an invited presentation for the Gwinnett Chamber of Commerce on March 18, 2014. An excerpt was presented at the Georgia Pacific Social Media Working Session on March 19, 2014.
What is Big Data? What is Data Science? What are the benefits? How will they evolve in my organisation?
Built around the premise that the investment in big data is far less than the cost of not having it, this presentation made at a tech media industry event, this presentation will unveil and explore the nuances of Big Data and Data Science and their synergy forming Big Data Science. It highlights the benefits of investing in it and defines a path to their evolution within most organisations.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Keynote talk by David Dietrich, EMC Education Services at ICCBDA 2013 : International Conference on Cloud and Big Data Analytics
http://twitter.com/imdaviddietrich
http://infocus.emc.com/author/david_dietrich/
Ordinary people included anyone who is not a Geek like myself. This book is written for ordinary people. That includes manager, marketers, technical writers, couch potatoes and so on.
Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein.
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Keynote talk by David Dietrich, EMC Education Services at ICCBDA 2013 : International Conference on Cloud and Big Data Analytics
http://twitter.com/imdaviddietrich
http://infocus.emc.com/author/david_dietrich/
Ordinary people included anyone who is not a Geek like myself. This book is written for ordinary people. That includes manager, marketers, technical writers, couch potatoes and so on.
Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein.
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
OSINT Black Magic: Listen who whispers your name in the dark!!!Nutan Kumar Panda
Open Source Intelligence is the art of collecting information which is scattered on publicly available sources. With evolution of social media and digital marketplaces a huge amount of information is constantly generated on the Internet (sometimes even without our conscious consent). This is of great concern for organizations and businesses as chances of confidential data floating in the public domain may seriously harm their business integrity. All recent hacks are related to internal source code disclosure, API keys leakage, known vulnerability in third party plugin, data dump leaks etc. Based on experience and robust research in this domain, for this talk the speakers have created a tool which will help all kind of organizations to monitor cyberspace effectively without much investment. This tool is simple but an effective solution which is capable of hearing digital whispers which are usually missed or ignored but shouldn’t be.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3aXysas
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?
Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist.
Watch this webinar to learn:
- How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles.
- How integrated Denodo Data Science notebook provides for a unified environment
- How Denodo uses AI/ML internally to drive the value of the data and expose insights
- How customers have used Data Virtualization in their Data Science initiatives.
Preconference Overview of data visualisation and technologyJen Stirrup
In this module, we will look at an overview of theory and scientific evidence about data visualisation. Understanding the ‘why’ can help to make us better at the ‘how’, regardless of the technology.We will also look at an overview of the Power BI suite of tools.
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...Denodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/Bvmvc9
Data prep and data blending are terms that have come to prominence over the last year or two. On the surface, they appear to offer functionality similar to data virtualization…but there are important differences!
In this session, you will learn:
• How data virtualization complements or contrasts technologies such as data prep and data blending
• Pros and cons of functionality provided by data prep, data catalog and data blending tools
• When and how to use these different technologies to be most effective
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Every day we roughly create 2.5 Quintillion bytes of data; 90% of the worlds collected data has been generated only in the last 2 years. In this slide, learn the all about big data
in a simple and easiest way.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
Where does Data Democracy begin? [Segment-Synapse, 2019]aj_cache
Data Democracy is a global vision. This talk explores what this means within the context of large businesses with examples from the restructuring of Sun Basket's Data Science & Engineering team.
Visualizing Healthcare Data with Tableau (Toronto Central LHIN Presentation)Stefan Popowycz
This is the presentation I gave to the Toronto Central LHIN about using Tableau to visualizing healthcare metrics (April 16 2013). I also have a section on how Information Design best practices can be leveraged in order to effectively communicate your key messages to your end users.
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
Guest Speaker in the 2nd National level webinar titled "Big Data Driven Solutions to Combat Covid 19" on 4th July 2020, Ethiraj College for Women(Auto), Chennai.
Similar to Department of Commerce App Challenge: Big Data Dashboards (20)
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Affordable Stationery Printing Services in Jaipur | Navpack n PrintNavpack & Print
Looking for professional printing services in Jaipur? Navpack n Print offers high-quality and affordable stationery printing for all your business needs. Stand out with custom stationery designs and fast turnaround times. Contact us today for a quote!
Kseniya Leshchenko: Shared development support service model as the way to ma...Lviv Startup Club
Kseniya Leshchenko: Shared development support service model as the way to make small projects with small budgets profitable for the company (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
LA HUG - Video Testimonials with Chynna Morgan - June 2024Lital Barkan
Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
We will dig deeper into:
1. How to capture video testimonials that convert from your audience 🎥
2. How to leverage your testimonials to boost your sales 💲
3. How you can capture more CRM data to understand your audience better through video testimonials. 📊
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Department of Commerce App Challenge: Big Data Dashboards
1. Department of Commerce App
Challenge: Big Data Dashboards
International Open Government Data Conference: Virtual Conference
Best Practices From Around the World in Putting Data to Work
Dr. Brand Niemann
Director and Senior Enterprise Architect – Data Scientist
Semantic Community
http://semanticommunity.info/
AOL Government Blogger
http://gov.aol.com/bloggers/brand-niemann/
April 27, 2012. Updated April 30, 2012. Updated July 7, 2012.
http://semanticommunity.info/AOL_Government/2012_International_Open_Government_Data_Conference
http://semanticommunity.info/AOL_Government/Department_of_Commerce_App_Challenge
1
2. International Open Government Data
Conference: Virtual Conference
• Questions to ask each presenter to supply afterwards for a directory - are you
doing these things?
– The way to document the public benefits with Open Data is to be able to answer the points
below:
• OPEN DATA
– O: Not previously Open to the public (lots of the "Open data" has already been available and is
just being re-advertised)
– P: Serves a Purpose (there is a reason the data was collected that clearly serves a real purpose
- e.g. Congressional redistricting)
– E: Educates citizens and politicians to take action (results that provide a valid basis for action)
– N: Made Newsworthy by journalists (results are communicated objectively and effectively)
– D: The plural of Dataum - something given or admitted especially as a basis for reasoning or
inference
– A: Actual numbers that a citizen, scientist, statistician, etc. can understand and work with
– T: Transparent (see where the data came from, how it was analyzed, where the results came
from, etc.)
– A: Answers questions posed by the above
2
3. Open Data Example
• O: Not previously Open to the public (lots of the "Open data" has already been available and is just
being re-advertised)
– EPA Envirofacts Warehouse APIs (slow large queries and bulk downloads before)
• P: Serves a Purpose (there is a reason the data was collected that clearly serves a real purpose - e.g.
Congressional redistricting)
– EPA Envirofacts data is Congressionally mandated for protection of human health and welfare
• E: Educates citizens and politicians to take action (results that provide a valid basis for action)
– EPA Envirofacts Web Site (over 2500 Web pages)
• N: Made Newsworthy by journalists (results are communicated objectively and effectively)
– My AOL Government Story is one of many such efforts
• D: The plural of Dataum - something given or admitted especially as a basis for reasoning or
inference
– EPA has data standards and quality assurance methods for these data
• A: Actual numbers that a citizen, scientist, statistician, etc. can understand and work with
– Yes
• T: Transparent (see where the data came from, how it was analyzed, where the results came from,
etc.)
– Yes, metadata is provided and combined with the new data APIs
• A: Answers questions posed by the above
– See my AOL Government Story with summary results as one of many such efforts
3
4. Beautiful Spreadsheet Data for EPA Envirofacts
Warehouse Metadata and API Dashboard
• Built for my former EPA CIO, Malcolm Jackson (a mobile app - iPad)
• Always wanted to do since my early days in the EPA Data Standards
Branch (2000-2002)
• Built a beautiful spreadsheet for public use and Spotfire application
• The format is both linked metadata and linked data
• Search all the metadata and get API data (but for only 9 of 13
systems and for only 5000 rows at a time)
• Find key fields for data integration and build many apps
• Metadata results:
– Models: 15
– Tables: 227
– Rows: 2518
– Types: 40
– Columns (Data Elements): 1662
4
6. Data Science Analytics for 2012 IOGDC
“More data beats clever algorithms but
better data beats more data.” Monica
• IOGDC Conference
Rogati @ Strata 2012 Knowledge Bases
• IOGDS Catalog Data
Sets
• IOGDS Data Analytics
with BI Tools
– Exploiting Linked Data
with Business
Intelligence Tools
• Acknowledgement:
Kingsley Idehen, CEO,
OpenLink Software
6
10. An Information Platform
• An Information Platform is the critical
infrastructure component for building a Learning
Organization. The most critical human
component for accelerating the learning process
and making use of the Information Platform is
taking the shape of a new role: the Data Scientist.
– Jeff Hammerbacher, in Chapter 5: Information
Platforms and the Rise of the Data Scientist in the His
Book “Beautiful Data” (July 2009) (see Linked Data
reference below)
http://semanticommunity.info/AOL_Government/Beautiful_Data#Information_Platforms_As_Dataspaces
10
11. Jeff Hammerbacker
• The number two data scientist in the world, according to Tim
O’Reilly, is Jeff Hammerbacker, who built the data science team at
Facebook and is now at Cloudera, driving the success of Hadoop as
a standard tool for processing large, unstructured data sets with a
network of commodity computers. Jeff also teaches ”Introduction
to Data Science”, at UC Berkeley, and in his opening lecture
organizes reason's for doing so into three parts as follows:
– 1. Personal - Jeff's training and job experiences
– 2. Putting Data to Work - Theme of the 2012 International Open
Government Data Conference
– 3. The Emergence of Data Science - Dominate theme of future
conferences according to Robert Ames, Senior VP for Technology at In-
Q-Tel, at the FCW Executive Briefing on Big Data and the Government
Enterprise, June 21, 2012
http://www.forbes.com/pictures/lmm45emkh/tim-oreilly-is-the-founder-of-oreily-media/#gallerycontent
11
12. My Mission Statement
• 1. Personal:
– Senior Data Scientist at the US EPA:
• Completed Data Science Academic Training and Many EPA Data Products
– Detail to Data.gov:
• Built Data.gov in An Information Platform
• 2. Putting Data To Work:
– Data Journalist for Federal Computer Week and AOL Government:
• Published Many Data Science Products and Built Own Data Journalism Handbook
– Data as a First Class Citizen: Data Science and Journalism for Analytic
Standards and Audit of Open Data Sites:
• Working with CKAN, DoD, IC, NCOIC, NIST, OASIS, OMG, OSTP, W3C, etc.
• 3. The Emergence of Data Science:
– Built a Data Science Team for the Government Community:
• “Killer Semantic Web Application” (Semantic MedLine on the new Cray Graph Computer)
for the Federal Big Data Senior Steering Group
– Challenges and Contests Using the Best High Quality Data Sets:
• Heritage Provider Network Health Prize, Health Data Initiative Forums, TedMed,
Department of Commerce App Challenge, etc.
12
13. Data Scientist
• A data scientist is a job title for an employee or business intelligence (BI)
consultant who excels at analyzing data, particularly large amounts of
data, to help a business gain a competitive edge.
• The title data scientist is sometimes disparaged because it lacks specificity
and can be perceived as an aggrandized synonym for data analyst.
Regardless, the position is gaining acceptance with large enterprises who
are interested in deriving meaning from big data, the voluminous amount
of structured, unstructured and semi-structured data that a large
enterprise produces.
• A data scientist possesses a combination of analytic, machine learning,
data mining and statistical skills as well as experience with algorithms and
coding. Perhaps the most important skill a data scientist possesses,
however, is the ability to explain the significance of data in a way that can
be easily understood by others.
Source: http://searchbusinessanalytics.techtarget.com/definition/Data-scientist
13
14. Dr. Brand Niemann
• Former Senior
Enterprise Architect and
Data Scientist, US
Environmental
Protection Agency
(1980-2010).
• Current
Husband, Father, and
Grandfather Enjoying
the Golden Years!
14
15. Semantic Community
• Our Mantra is: Data Science Precedes the Use of SOA,
Cloud, and Semantic Technologies! We use data science to
help marketing and business development efforts.
• Our Mission is like Googles: Organize the world’s
information and make it universally accessible and useful.
• Our Method is like Be Informed 4: Architectural Diagrams
and Questions and Answers are not enough, you need
Dynamic Case Management!
• Our Sound Byte: It is not just where you put your data
(cloud), but how you put it there!
• Our Work: Semantically enhancing your data and writing
data science stories about it.
15
16. Introduction
• I heard about this several months ago, but put it off until
yesterday. I finished it today because I am a very good Data
Scientist!
• Well I almost finished it. I need the Patent data in a format
that I can more readily work with and I am in
communication with the USPTO about that.
• I create Knowledge Bases about my Data Science work so
others can follow what I do and even reproduce it
themselves. My apps also work on mobile devices like
iPads.
• My goal was, and still is, to create a set of multiple
interactive dashboards of DoC data like they have
for Foreign Trade.
16
17. Data Science Knowledge Base
http://semanticommunity.info/AOL_Government/Department_of_Commerce_App_Challenge
17
19. Spotfire Dashboards
• U.S. Census Bureau Geographic Names
Information System
• U.S. International Trade in Goods and Services
• Data.Gov Data Catalog for US Department of
Commerce
• U.S. Bureau of Economic Analysis
• U.S. Patent & Trademark Office
19
24. U.S. Patent & Trademark Office
• Methodology:
– Overview: Apply Gall's Law and start with the end in mind (Mashups
and Decision Support) and work out the details in a simple and small
content example for my next AOL Government Story! Give everything
a well-defined URL for a semantically enhanced index in a Dashboard
(see next slide).
• 1. Follow Gall's Law which says: "A complex system that works is invariably
found to have evolved from a simple system that worked. The inverse
proposition also appears to be true: a complex system designed from scratch
never works and cannot be made to work. You have to start over, beginning
with a simple system." - John Gall, systems theorist
• 2. Copy to MindTouch and add structure to the Web Pages
– See
http://semanticommunity.info/AOL_Government/Department_of_Commerce_App_Chall
enge/DOC_USPTO_Apps_for_Innovation
• 3. Look at one ZIP file under each section and subsection to see what it
contains and how to use it in MindTouch (in process)
– See
http://semanticommunity.info/AOL_Government/Department_of_Commerce_App_Chall
enge/DOC_USPTO_Apps_for_Innovation/Electronic_Data_Products
24
26. MindTouch
DoC USPTO Apps for Innovation
http://semanticommunity.info/AOL_Government/Department_of_Commerce_App_Challenge/DOC_USPTO_Apps_for_Innovation
26
27. MindTouch
Electronic Data Products
http://semanticommunity.info/AOL_Government/Department_of_Commerce_App_Challenge/DOC_USPTO_Apps_for_Innovation/Electronic_Data_Products
27
28. Work Plan in Process
• Mash-Ups:
– Combine USPTO applicant/inventor information with other USPTO datasets (e.g., with USPTO
assignments (ownership) data):
• Google or USPTO Daily and USPTO Retro
– Combine USPTO patent grants and patent application publications with other DOC data (e.g.,
Census or Economic data)
• Innovative Ideas:
– Homogenize the patent grant bibliographic text data (i.e., make it all the same format).
– Same for the patent application publication bibliographic data.
– Capture patent grant bibliographic text data from 1790 to 1975 using the image data.
– Build a text searchable database (updated weekly) that includes both of the datasets
discussed in the Webinar. Search queries can be saved. Result sets can be
saved/extracted/tailored.
– Build a text searchable database (updated weekly) that includes subsets of both of the
datasets discussed in the Webinar. (e.g., Green Technology related).
– Same ideas as above, but use full-text (75 MB/104 MB per week) or full-text with embedded
images (1.4 GB/1.5GB per week): http://www.google.com/googlebooks/uspto-patents.html
Source: http://semanticommunity.info/AOL_Government/Department_of_Commerce_App_Challenge/DOC_USPTO_Apps_for_Innovation#Innovative_Ideas
28
29. More Questions For Todd Park
About Big Data
http://gov.aol.com/2012/04/25/more-questions-for-todd-park-about-big-data/
29
30. Conclusions and Recommendations
• A Data Science approach to the App Challenge
provided examples for improvements in data
dissemination and visualization.
• Most of the data sets are “big data” when it
comes to the app developer community working
on simple mobile apps using smaller data sets.
• The Patent data dissemination offers the most
challenge for improvement and opportunity for
creative piloting using a Data Science approach.
For details see: http://semanticommunity.info/AOL_Government/Department_of_Commerce_App_Challenge#Submission
30
31. Postscript
• Presentation to Federal Big Data Senior Steering Group
for Big Data, September 27, 2012:
– A Data Science team comprised of NLM (Tom
Rindflesch), Noblis (Victor Pollara), Cray (Steve
Reinhardt), and Semantic Community (Brand Niemann), is
working to make what Dr. George Strawn refers to as “the
killer semantic web application for government”, Semantic
Medline, more well-know, and functional for medical
research by putting the Semantic Medline RDF database
into the new Cray Graph Computer and demonstrating its
usefulness.
– The background for this project is at:
• http://semanticommunity.info/A_NITRD_Dashboard/Semantic_M
edline
31
32. BusinessUSA.gov Their APIs Can be
Data Interfaces
http://gov.aol.com/2012/07/02/why-apis-arent-enough-to-make-businessusa-gov-useful/
http://semanticommunity.info/AOL_Government/BusinessUSA.gov_Their_APIs_Can_be_Data_Interfaces
32
33. Imagination at Work! Unleash Your
Creativity with Our Census API
http://semanticommunity.info/AOL_Government/Data_Services_for_Developers
33
34. Digital Agenda For Europe:
Data As First-Class Citizen
http://gov.aol.com/2012/06/29/digital-agenda-for-europe-data-as-first-class-citizen/
http://semanticommunity.info/AOL_Government/Digital_Agenda_for_Europe 34
35. Data Science Spring 2012 Exercise 1:
2012 Presidential Campaign Finance Data
http://semanticommunity.info/AOL_Government/Beautiful_Data#Spotfire_Dashboard
35
36. Data Science Spring 2012 Exercise 3:
Evaluate Models of R Package Recommendations
http://semanticommunity.info/AOL_Government/Beautiful_Data#Spotfire_Dashboard_2
36
37. Big Data and The Government Enterprise
• “More data beats clever
algorithms but better data
beats more data.” Monica
Rogati @ Strata 2012
• “Big Data in memory is
necessary to avoid loss of
information from filtering
and aggregation and a data
scientist knows the data
science and the technology
to do that.” Brand Niemann
@ Big Data and the
Government Enterprise
http://semanticommunity.info/AOL_Government/Big_Data_and_the_Government_Enterprise
37
38. Big Data and The Government Enterprise
http://semanticommunity.info/AOL_Government/Big_Data_and_the_Government_Enterprise
38