This was talks first given at the Online News Association conference in 2013. An adapted version was given a second time for the Asian American Journalists Association in 2014.
First, we will explore the power of a compounding insight machine (as opposed to an ad hoc insight machine):
-Human time is focused on improving logic, rather than executing outcomes
-Less dependent on human biases or frailty
-Robust to and tested by a huge collection of scenarios
Second, we will explore the anatomy of such a machine:
-The roles you need to cast on your team and who to fill them with
-The key processes required for generating and capturing insight and, more importantly, for building upon those insights
-The technology required to enable this approach
Big data is messy because it involves combining different types of information from various sources that do not always align perfectly. Messiness also arises when data is extracted and processed since it is being transformed into something else. Additionally, behind every data point is a person, and incorporating people introduces complexity when analyzing big data. Unlocking insights from big data requires accounting for human behaviors and perspectives that complicate analyses.
The Data Greenhouse DevOps Measurement at Scalesparkagility
This document summarizes a presentation on developing a "Data Greenhouse" to integrate measurement into DevOps programs. The presentation covers:
- Why program leaders often miss targets for data collection due to issues like unstructured data and lack of integration
- Generating leadership interest in unknowns by communicating initial data findings and insights
- Whether measurement efforts should be their own initiative given barriers to improvement
- Signs that measurement is paying off such as teams independently problem-solving and requesting data
- Next steps like partnering with teams on analysis and an improved measurement platform
David Lary and Rick McGeer presented on envisioning the future of the internet and big data visualization. They discussed how the Global Environment for Network Innovations (GENI) and US Ignite are helping to build an internet that can visualize big data fast from any device anywhere in a collaborative manner. The goal is to achieve response times of 0.15 seconds or less to enable an interactive user experience.
Introduction to big data for the EA course at Solvay MBAWim Van Leuven
Introduction to what is big data, what can it do and not do, the importance of datascience and how to architect big data solutions (lambda architecture)
Is big data just a buzzword -Big data simply explainedVivek Srivastava
Big data helps us to uncover and discover those facets of data which we are not aware of . Using predictive science it helps us to provide insights on which actions can be taken and suggests those actions which will impact the business significantly boosting the revenue or market reach.For example, using large amount of data and appropriate tools, we can categorize different strata of population and build customize products. So whether companies deploy it or not, all depends on what factor constitute the value of company and where the center of value creation lies. It may be money or it may be geographic reach. - Watch this video at https://www.youtube.com/watch?v=ELyOl0fkqNM
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
The document discusses the concept of "Big Data" and argues that there is no such thing. It notes that the term is primarily a buzzword used in IT and defines the 4Vs typically associated with Big Data. However, it states that most companies actually have "Big, Data Problems" rather than true Big Data problems, and that traditional databases can still solve many problems. It advocates focusing first on properly defining, storing, and understanding data before worrying about issues of scale or using new technologies. Engineering, the right tools, asking the right questions, building strong teams, and continuous learning are more important than prematurely pursuing Big Data.
First, we will explore the power of a compounding insight machine (as opposed to an ad hoc insight machine):
-Human time is focused on improving logic, rather than executing outcomes
-Less dependent on human biases or frailty
-Robust to and tested by a huge collection of scenarios
Second, we will explore the anatomy of such a machine:
-The roles you need to cast on your team and who to fill them with
-The key processes required for generating and capturing insight and, more importantly, for building upon those insights
-The technology required to enable this approach
Big data is messy because it involves combining different types of information from various sources that do not always align perfectly. Messiness also arises when data is extracted and processed since it is being transformed into something else. Additionally, behind every data point is a person, and incorporating people introduces complexity when analyzing big data. Unlocking insights from big data requires accounting for human behaviors and perspectives that complicate analyses.
The Data Greenhouse DevOps Measurement at Scalesparkagility
This document summarizes a presentation on developing a "Data Greenhouse" to integrate measurement into DevOps programs. The presentation covers:
- Why program leaders often miss targets for data collection due to issues like unstructured data and lack of integration
- Generating leadership interest in unknowns by communicating initial data findings and insights
- Whether measurement efforts should be their own initiative given barriers to improvement
- Signs that measurement is paying off such as teams independently problem-solving and requesting data
- Next steps like partnering with teams on analysis and an improved measurement platform
David Lary and Rick McGeer presented on envisioning the future of the internet and big data visualization. They discussed how the Global Environment for Network Innovations (GENI) and US Ignite are helping to build an internet that can visualize big data fast from any device anywhere in a collaborative manner. The goal is to achieve response times of 0.15 seconds or less to enable an interactive user experience.
Introduction to big data for the EA course at Solvay MBAWim Van Leuven
Introduction to what is big data, what can it do and not do, the importance of datascience and how to architect big data solutions (lambda architecture)
Is big data just a buzzword -Big data simply explainedVivek Srivastava
Big data helps us to uncover and discover those facets of data which we are not aware of . Using predictive science it helps us to provide insights on which actions can be taken and suggests those actions which will impact the business significantly boosting the revenue or market reach.For example, using large amount of data and appropriate tools, we can categorize different strata of population and build customize products. So whether companies deploy it or not, all depends on what factor constitute the value of company and where the center of value creation lies. It may be money or it may be geographic reach. - Watch this video at https://www.youtube.com/watch?v=ELyOl0fkqNM
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
The document discusses the concept of "Big Data" and argues that there is no such thing. It notes that the term is primarily a buzzword used in IT and defines the 4Vs typically associated with Big Data. However, it states that most companies actually have "Big, Data Problems" rather than true Big Data problems, and that traditional databases can still solve many problems. It advocates focusing first on properly defining, storing, and understanding data before worrying about issues of scale or using new technologies. Engineering, the right tools, asking the right questions, building strong teams, and continuous learning are more important than prematurely pursuing Big Data.
Is big data handicapped by "design"? Seven design principles for communicatin...Zach Gemignani
Is big data handicapped by "design"? This presentation shares the seven design principles for effective data communication. Good and bad examples for data visualizations highlight the choices designers make in helping non-analytical audiences understand the meaning in data.
Beyond Data Visualization: What's next in communicating with data?Zach Gemignani
We've made great progress in learning how to visualize data, yet a gap still remains between the data experts and the data consumers who might take action on the data. This presentation, shared at the Nashville Analytics Summit, explains how we can bring people into the process of communicating data and guide them to informed actions.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
DISUMMIT - Rishi Nalin Kumar from DatakindDigitYser
1) The document discusses lessons learned from DataKind's work using data to solve social problems over the past 3 years.
2) Some key lessons included celebrating all outcomes, whether intended or not; working with social organizations instead of just focusing on data; and ensuring the problem is well-framed and data is relevant.
3) Successful data for good projects require a partnership between social actors and data experts to identify a well-defined problem, relevant data sources, and solutions that are impactful but also understandable.
DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making DigitYser
Dr. Kirk Borne is a Principal Data Scientist at Booz Allen Hamilton. With a rich background in Astrophysics and Computational Science, he was a precursor on implementing courses of big data in academia. He is one of the most important promotors of data literacy in the world.
About Kirk and his view on data literacy and evolution
On his first visit to Brussels, Kirk first activity was sharing his best practices to promote data literacy. While enjoying a magnificent view of Brussels from the ING headquarter building, Kirk playfully (with a pair of socks!) explained how subjectivity plays a major role in the way that data is understood, derived by the wide variety of involved. This keynote was delivered at the speakers reception, which took place the day before the DI Summit.
The following day, Kirk wrapped up the DI summit with his closing keynote on how data has shifted into something that is sense-making, following the evolution from “data” to “big data” into “smart data” composed by both enriched and semantic data and essential for IoT. He also discussed the levels of maturity in a self-driving enterprise, wrapping up his participation sharing this equation:
Big Data + IoT + Citizen Data Scientists = Partners in Sustainability
Kirk’s impression on the DI Summit was that it was a fun and informative event to join. His favorite format were the 5” pitches, as they were properly structured, providing the most critical information to the attendees. He also think that the networking dynamic ensured that all attendees met interesting people.
A takeaway from Kirk’s presentation
“Big data is not about how big it is, but the value you extract from it”
We look forward to have Kirk sometime soon back in Brussels!
Kirk’s interview:
Kirk’s presentation recording:
Kirk’s decks:
Kirk’s presentation drawing:
2) Here are some video interviews that I have done:
https://www.youtube.com/watch?v=ku2na1mLZZ8
https://www.youtube.com/watch?v=iXjvht91nFk
Here is my TedX talk: https://www.youtube.com/watch?v=Zr02fMBfuRA
How to Speak Human - Turning Big Data Insights into Actionable Business StrategyLuciano Pesci, PhD
Big Data has failed to deliver on its promise because decision-makers and technical practitioners aren't speaking the same language. Cryptic data outputs have to be translated into simple strategy recommendations to turn this trend around.
How to build a data science team 20115.03.13v6Zhihao Lin
Teralytics provides real-time insights into human behavior globally using data from 350 million profiles and 180 billion daily events. They have built a data science team in Singapore that develops one of their three products deployed worldwide. The presentation outlines how to build an effective data science team, including finding team members through diverse sources, evaluating them through a multi-stage interview process, convincing them to join by emphasizing the work, data, and team environment, and getting the team working cohesively through collaborative projects with clear goals and deadlines.
Big data refers to the large amounts of data collected from various sources as part of regular business operations. When analyzed, big data can reveal patterns and insights that would otherwise go unnoticed. Collecting big data from employee interactions through a customizable app allows businesses to better understand factors that impact engagement. Analyzing correlations in big data, while not proving causation, can highlight unexpected relationships worth further investigation to improve the workplace.
Data science is having a growing effect on our lives, from the content we see on social media feeds to the decisions businesses are making. Along with successes, data science has inspired much hype about what it is and what it can do. So I plan to try and demystify data science and have a discussion about what it really is. What does a day-in-the-life look like? What tools and skills are needed? How is data science successfully applied in the real world? In this talk, I’ll be providing insight into these questions and also speculate the future of data science and its place in business and technology.
Presented at OpenWest 2018
BIG DATA MANAGEMENT - forget the hype, let's talk about the facts! Lisa Lang
This is a panel/workshop session developed for NEXT 2014 in Berlin.
Guests:
Lisa Lang (Twilio) Anke Domscheit-Berg (Opengov.me) Olga Steidl (Linko ) Ivan P. Yamshchikov (Yandex) Felienne Hermans (TU Delft)
----
Content:
Everyone is talking about Big Data – but what’s really behind it and how can you make data work for your business?
Collecting data is just one part of the puzzle. To source the right information, read it so it makes sense and -finally- how to execute on it is the most important task for successful big data management.
At this panel workshop we’ll listen to a lot of examples from big companies who’re dealing with massive amount of data on a daily basis. Each panel member will give a short demo and insight to their strategies and might revile some surprising facts.
This workshop is organised in cooperation with Berlin Geekettes.
This document outlines an agenda for a two-day workshop on data storytelling for social change. Day 1 focuses on asking questions of data, finding stories within data, and techniques for telling stories. Activities include analyzing case studies, sketching stories, and building a data sculpture. Day 2 covers making arguments with data and hands-on practice with sample datasets in Tableau. The goal is to provide a process for using data to further social causes from brainstorming questions to assessing the impact of stories.
The speaker discusses the importance of evaluating big data analysis to improve projects. They recommend getting a second opinion on methodology or using another data source for verification. A case study on estimating traffic congestion is presented where ground truth sensor data was collected and compared to estimates using metrics to provide feedback. Lessons include ground truth not being easy to obtain, using the right tools like Python and pandas for evaluation, and having an iterative workflow for timely feedback.
Big data is a crowded and confusing space with differing definitions. While some companies are deriving value from big data, others are struggling to find value due to a lack of talent and solutions. The speaker argues that we are still in the early days of big data, but that major trends around increasing data volumes, decreasing hardware costs, and the rise of data-driven businesses mean that the hype around big data will eventually be realized. Developers will play a key role in building applications and vendor solutions that allow more organizations to access and take advantage of big data.
Presentation at FlowFactor 2019. Another look at the data science hierarchy of needs specifically looking at chasing Artificial Intelligence and Machine Learning.
IBM envisions the future of workspaces being transformed by cognitive technologies. Cognitive systems can understand vast amounts of data, including unstructured information like language. They learn from each interaction which allows them to develop expertise over time. Cognitive assistants will be able to understand a person's work, offer answers and insights, prioritize tasks, and surface recommendations to help workers be more productive. IBM is developing cognitive solutions like expert advisors and personal assistants to assist with collaboration and tasks in a new cognitive-assisted workspace.
Business leaders everywhere are looking to data to inform their decision making. Accompanying this demand are misunderstandings of what it takes to transform data into something that can inform a decision. What is the data infrastructure required? In this talk, I'll dispel some of these misunderstandings and discuss what it takes to build good data infrastructure. I'll discuss the components of a good data infrastructure. The best practices and available tools for gathering data, processing it, storing it, analyzing it and communicating the results. The goal is for these components to create a data infrastructure which can evolve from simple reporting to sophisticated insights for decision making.
Presented at OpenWest 2018
The document provides a description of data scientist positions at three levels - Data Scientist I, II, and III. It outlines the general characteristics and responsibilities expected for each level, with level III involving the most complex work, responsibilities for leading projects, and experience/education qualifications. Key responsibilities include data analysis, modeling, collaborating with stakeholders, and communicating results.
The document summarizes a research project using big data to help avoid weather-related flight delays. It discusses how researchers gathered over 10 years of hourly weather and flight data and are using advanced analytics to identify patterns that could help airlines better manage delays. The goal is to allow airlines to anticipate delays before they happen by predicting how weather in one location may impact flights elsewhere. This could help airlines proactively adjust schedules and resources to minimize disruptions. The researchers believe this analysis of massive data sets could significantly improve the travel experience for passengers and airlines.
This document is a seminar report submitted by Pushkar Zagade to Savitribai Phule Pune University in partial fulfillment of a computer engineering course. The report discusses global wireless e-voting, including its importance, requirements, techniques, and how such a system could work. It covers topics like voter eligibility verification via retina scanning, advantages and disadvantages of e-voting, and challenges to implementing such a system. The goal is to improve voting methods and prevent fraud.
Is big data handicapped by "design"? Seven design principles for communicatin...Zach Gemignani
Is big data handicapped by "design"? This presentation shares the seven design principles for effective data communication. Good and bad examples for data visualizations highlight the choices designers make in helping non-analytical audiences understand the meaning in data.
Beyond Data Visualization: What's next in communicating with data?Zach Gemignani
We've made great progress in learning how to visualize data, yet a gap still remains between the data experts and the data consumers who might take action on the data. This presentation, shared at the Nashville Analytics Summit, explains how we can bring people into the process of communicating data and guide them to informed actions.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
DISUMMIT - Rishi Nalin Kumar from DatakindDigitYser
1) The document discusses lessons learned from DataKind's work using data to solve social problems over the past 3 years.
2) Some key lessons included celebrating all outcomes, whether intended or not; working with social organizations instead of just focusing on data; and ensuring the problem is well-framed and data is relevant.
3) Successful data for good projects require a partnership between social actors and data experts to identify a well-defined problem, relevant data sources, and solutions that are impactful but also understandable.
DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making DigitYser
Dr. Kirk Borne is a Principal Data Scientist at Booz Allen Hamilton. With a rich background in Astrophysics and Computational Science, he was a precursor on implementing courses of big data in academia. He is one of the most important promotors of data literacy in the world.
About Kirk and his view on data literacy and evolution
On his first visit to Brussels, Kirk first activity was sharing his best practices to promote data literacy. While enjoying a magnificent view of Brussels from the ING headquarter building, Kirk playfully (with a pair of socks!) explained how subjectivity plays a major role in the way that data is understood, derived by the wide variety of involved. This keynote was delivered at the speakers reception, which took place the day before the DI Summit.
The following day, Kirk wrapped up the DI summit with his closing keynote on how data has shifted into something that is sense-making, following the evolution from “data” to “big data” into “smart data” composed by both enriched and semantic data and essential for IoT. He also discussed the levels of maturity in a self-driving enterprise, wrapping up his participation sharing this equation:
Big Data + IoT + Citizen Data Scientists = Partners in Sustainability
Kirk’s impression on the DI Summit was that it was a fun and informative event to join. His favorite format were the 5” pitches, as they were properly structured, providing the most critical information to the attendees. He also think that the networking dynamic ensured that all attendees met interesting people.
A takeaway from Kirk’s presentation
“Big data is not about how big it is, but the value you extract from it”
We look forward to have Kirk sometime soon back in Brussels!
Kirk’s interview:
Kirk’s presentation recording:
Kirk’s decks:
Kirk’s presentation drawing:
2) Here are some video interviews that I have done:
https://www.youtube.com/watch?v=ku2na1mLZZ8
https://www.youtube.com/watch?v=iXjvht91nFk
Here is my TedX talk: https://www.youtube.com/watch?v=Zr02fMBfuRA
How to Speak Human - Turning Big Data Insights into Actionable Business StrategyLuciano Pesci, PhD
Big Data has failed to deliver on its promise because decision-makers and technical practitioners aren't speaking the same language. Cryptic data outputs have to be translated into simple strategy recommendations to turn this trend around.
How to build a data science team 20115.03.13v6Zhihao Lin
Teralytics provides real-time insights into human behavior globally using data from 350 million profiles and 180 billion daily events. They have built a data science team in Singapore that develops one of their three products deployed worldwide. The presentation outlines how to build an effective data science team, including finding team members through diverse sources, evaluating them through a multi-stage interview process, convincing them to join by emphasizing the work, data, and team environment, and getting the team working cohesively through collaborative projects with clear goals and deadlines.
Big data refers to the large amounts of data collected from various sources as part of regular business operations. When analyzed, big data can reveal patterns and insights that would otherwise go unnoticed. Collecting big data from employee interactions through a customizable app allows businesses to better understand factors that impact engagement. Analyzing correlations in big data, while not proving causation, can highlight unexpected relationships worth further investigation to improve the workplace.
Data science is having a growing effect on our lives, from the content we see on social media feeds to the decisions businesses are making. Along with successes, data science has inspired much hype about what it is and what it can do. So I plan to try and demystify data science and have a discussion about what it really is. What does a day-in-the-life look like? What tools and skills are needed? How is data science successfully applied in the real world? In this talk, I’ll be providing insight into these questions and also speculate the future of data science and its place in business and technology.
Presented at OpenWest 2018
BIG DATA MANAGEMENT - forget the hype, let's talk about the facts! Lisa Lang
This is a panel/workshop session developed for NEXT 2014 in Berlin.
Guests:
Lisa Lang (Twilio) Anke Domscheit-Berg (Opengov.me) Olga Steidl (Linko ) Ivan P. Yamshchikov (Yandex) Felienne Hermans (TU Delft)
----
Content:
Everyone is talking about Big Data – but what’s really behind it and how can you make data work for your business?
Collecting data is just one part of the puzzle. To source the right information, read it so it makes sense and -finally- how to execute on it is the most important task for successful big data management.
At this panel workshop we’ll listen to a lot of examples from big companies who’re dealing with massive amount of data on a daily basis. Each panel member will give a short demo and insight to their strategies and might revile some surprising facts.
This workshop is organised in cooperation with Berlin Geekettes.
This document outlines an agenda for a two-day workshop on data storytelling for social change. Day 1 focuses on asking questions of data, finding stories within data, and techniques for telling stories. Activities include analyzing case studies, sketching stories, and building a data sculpture. Day 2 covers making arguments with data and hands-on practice with sample datasets in Tableau. The goal is to provide a process for using data to further social causes from brainstorming questions to assessing the impact of stories.
The speaker discusses the importance of evaluating big data analysis to improve projects. They recommend getting a second opinion on methodology or using another data source for verification. A case study on estimating traffic congestion is presented where ground truth sensor data was collected and compared to estimates using metrics to provide feedback. Lessons include ground truth not being easy to obtain, using the right tools like Python and pandas for evaluation, and having an iterative workflow for timely feedback.
Big data is a crowded and confusing space with differing definitions. While some companies are deriving value from big data, others are struggling to find value due to a lack of talent and solutions. The speaker argues that we are still in the early days of big data, but that major trends around increasing data volumes, decreasing hardware costs, and the rise of data-driven businesses mean that the hype around big data will eventually be realized. Developers will play a key role in building applications and vendor solutions that allow more organizations to access and take advantage of big data.
Presentation at FlowFactor 2019. Another look at the data science hierarchy of needs specifically looking at chasing Artificial Intelligence and Machine Learning.
IBM envisions the future of workspaces being transformed by cognitive technologies. Cognitive systems can understand vast amounts of data, including unstructured information like language. They learn from each interaction which allows them to develop expertise over time. Cognitive assistants will be able to understand a person's work, offer answers and insights, prioritize tasks, and surface recommendations to help workers be more productive. IBM is developing cognitive solutions like expert advisors and personal assistants to assist with collaboration and tasks in a new cognitive-assisted workspace.
Business leaders everywhere are looking to data to inform their decision making. Accompanying this demand are misunderstandings of what it takes to transform data into something that can inform a decision. What is the data infrastructure required? In this talk, I'll dispel some of these misunderstandings and discuss what it takes to build good data infrastructure. I'll discuss the components of a good data infrastructure. The best practices and available tools for gathering data, processing it, storing it, analyzing it and communicating the results. The goal is for these components to create a data infrastructure which can evolve from simple reporting to sophisticated insights for decision making.
Presented at OpenWest 2018
The document provides a description of data scientist positions at three levels - Data Scientist I, II, and III. It outlines the general characteristics and responsibilities expected for each level, with level III involving the most complex work, responsibilities for leading projects, and experience/education qualifications. Key responsibilities include data analysis, modeling, collaborating with stakeholders, and communicating results.
The document summarizes a research project using big data to help avoid weather-related flight delays. It discusses how researchers gathered over 10 years of hourly weather and flight data and are using advanced analytics to identify patterns that could help airlines better manage delays. The goal is to allow airlines to anticipate delays before they happen by predicting how weather in one location may impact flights elsewhere. This could help airlines proactively adjust schedules and resources to minimize disruptions. The researchers believe this analysis of massive data sets could significantly improve the travel experience for passengers and airlines.
This document is a seminar report submitted by Pushkar Zagade to Savitribai Phule Pune University in partial fulfillment of a computer engineering course. The report discusses global wireless e-voting, including its importance, requirements, techniques, and how such a system could work. It covers topics like voter eligibility verification via retina scanning, advantages and disadvantages of e-voting, and challenges to implementing such a system. The goal is to improve voting methods and prevent fraud.
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
This document proposes a system for global wireless e-voting. It summarizes the current voting system and its disadvantages like re-elections and inability to check voter eligibility. The proposed system uses retina scanning, radio waves, and a remote server for secure authentication and storage of votes to allow people to vote from anywhere using the internet. It describes the technical components like interface devices, encryption algorithms, and distributed servers to address security, efficiency, and geographical challenges in implementing such a system. Future enhancements could enable voting via mobile phones or the internet for more accessibility.
This document provides an overview of fuzzy logic and its applications. It begins with motivations for fuzzy logic by discussing limitations of crisp sets and fuzzy sets as an alternative approach. It then defines fuzzy sets and fuzzy logic operations. It describes how fuzzy logic systems work by combining fuzzy sets and logic operations. Several example applications are mentioned, including industrial control systems and modeling human decision making. The document concludes by noting fuzzy logic has been applied in many domains and there are ongoing developments in fuzzy logic approaches.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It is used in control systems to mimic how humans apply fuzzy concepts like "cold" or "hot" temperature. Some key applications of fuzzy logic include temperature controllers, washing machines, air conditioners, and anti-lock braking systems. Fuzzy logic controllers use if-then rules to determine outputs based on fuzzy inputs and degrees of membership rather than binary logic.
The document provides an abstract for a paper on the Hadoop framework. It discusses how Hadoop is a software framework that supports data-intensive distributed applications under an open source license. It was inspired by Google's MapReduce and Google File System papers. The paper will represent the history, development, and current situation of Hadoop technology. It is now maintained by the Apache Software Foundation via Cloudera. The paper will include chapters on an introduction to Hadoop, its history, key technologies like MapReduce and HDFS, other related Apache projects, and instructions for setting up a single node Hadoop cluster.
The document discusses big data and distributed computing. It provides examples of the large amounts of data generated daily by organizations like the New York Stock Exchange and Facebook. It explains how distributed computing frameworks like Hadoop use multiple computers connected via a network to process large datasets in parallel. Hadoop's MapReduce programming model and HDFS distributed file system allow users to write distributed applications that process petabytes of data across commodity hardware clusters.
This document provides an overview of data science including its importance, what data scientists do, how the field has emerged, and how to become a data scientist. It notes that by 2018 the US could face shortages of people with data analytics skills. It then discusses how LinkedIn's early growth in 2006 exemplifies the data science process of framing questions, collecting and processing data, exploring patterns, and communicating results. Finally, it outlines the tools used in data science like SQL, analytics software, and machine learning and discusses getting started in the field through education, curiosity, and ongoing learning with mentorship support.
This document provides an overview of data science including its importance, what data scientists do, how the field has emerged, and how to become a data scientist. It discusses how data science can help answer important business questions using LinkedIn in 2006 as a case study. It also outlines the typical data science process of framing questions, collecting and cleaning data, exploring patterns, and communicating results. Finally, it introduces some common data science tools like SQL, analytics software, and machine learning algorithms and discusses options for continuing education in data science.
Getting started in Data Science (April 2017, Los Angeles)Thinkful
The document discusses the rise of data science and the skills needed for data scientists. It defines data science as the intersection of engineering, statistics, and communication. Data scientists analyze large datasets to answer important business questions. The document uses LinkedIn in 2006 as a case study, outlining how a data scientist there framed questions, collected and processed user data, explored patterns, and communicated results to improve the user experience and growth. It highlights tools like SQL, analytics software, and machine learning that data scientists use and stresses the importance of curiosity, technical skills, and strong communication for those interested in the field.
This document discusses how making data more human can benefit managers. It suggests analyzing employee feedback and work culture data to understand sentiment and maintain a good environment. Retention data can be used to modify policies and counseling based on employee needs and wishes. Performance can be optimized by analyzing project data to reduce wasted time and identify efficient solutions. Strategic planning can leverage data analysis to predict the future, estimate events, and gain powerful insights backed by facts. Overall, taking a human-centric approach to data by relating it to human behaviors and contexts can improve comprehension and help address management challenges.
This document summarizes an introductory presentation on data science. It introduces the presenter and their background in data and analytics. The goals of the presentation are to define what a data scientist is, how the field has emerged, and how to become one. It discusses the growing demand and salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn and Netflix. The presentation covers big data, Hadoop, data processing techniques, machine learning algorithms, and tools used in data science. Finally, attendees are encouraged to consider Thinkful's data science bootcamp program.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
2017 06-14-getting started with data scienceThinkful
The document provides an overview of getting started with a career in data science. It introduces the author Jasjit Singh and discusses what a data scientist does, how the field has emerged to analyze big data. Examples are given of how companies like LinkedIn and Uber use data science. The data science process is explained through the steps of framing a question, collecting and processing data, exploring patterns in the data, and communicating findings. Tools used include SQL, data visualization software, and machine learning algorithms. The document encourages the reader that becoming a data scientist is achievable through learning statistics, algorithms, and software skills.
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
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.
Check out more of our Data-Ed webinars here: www.datablueprint.com/webinar-schedule
Storytelling with Data (Global Engagement Summit at Northwestern University 2...Sara Hooker
The document provides guidance on effective storytelling with data in 3 key areas: answering important questions early, focusing on good data collection, and understanding your audience. It emphasizes answering why the audience should care, why you are best positioned to address the problem, and what the desired outcome is. Good data collection requires defining core metrics and sustainability. Understanding your audience involves considering their relationship to you and how to communicate to them. The document encourages borrowing from other effective examples and focusing on telling a compelling story with data rather than just presenting charts.
This document appears to be a presentation on big data and analytics. It includes slides on topics like how big data is measured, where it comes from, how it will impact learning systems, and examples of big data in areas like social networks, wikis, and recommendations. It also includes slides on techniques like linear regression, stochastic gradient descent, and responses from students on big data and their interest in seeing it incorporated into courses.
This document appears to be a presentation on big data and analytics. It includes slides on topics like how big data is measured, where it comes from, how it will impact learning systems, and examples of big data in areas like social networks, wikis, and recommendations. It also includes slides on techniques like linear regression, stochastic gradient descent, and responses from students on big data and their interest in seeing it incorporated into courses.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Presentation: Big Data 101, What It Means for Business
Presented by: David Ray, Corporate Vice President, Corporate Internet, New York Life Insurance Company
Big Data is the latest buzzword inside the C-suite, but what does it mean, how are other industries using it to competitive advantage, and what are the real opportunities for business? Does big data require massive amounts of data to be considered or is there success to be found in unifying myriad data sources? Join us for an interesting peek.
www.bdionline.com
Grow Your Own - How to Create a Data Culture at Your OrganizationLuciano Pesci, PhD
80% of data projects fail. How can something so promising be failing so badly? While organizations are scrambling to stay competitive by hiring data-talent, they don't fully understand the types available, how to integrate them into existing workflows, what to expect from their efforts, and how to gauge success.
You can watch the accompanying Webinar here: https://youtu.be/MUv-tqMHbvs
This document provides an overview of data science. It defines data as facts such as numbers, words, measurements, and descriptions. Data science involves developing methods to analyze and extract useful insights from both structured and unstructured data. While data mining focuses on analyzing large datasets, data science covers the entire data lifecycle. There is a growing demand for data scientists as every industry relies on data. Data scientists use various statistical techniques to find patterns in data and gain knowledge. Netflix is used as a case study to show how it has become a data-driven business that uses data science to power recommendations and improve the customer experience.
What Big Data Means for PR and Why It Matters to UsMSL
Invited to sit on a panel together with Paul Holmes at the PR Forum held in Bucharest March 26, Pascal shared thoughts about the Big Data tsunami which is deeply transforming marketing, communications and PR. What is "Big Data" exactly, what does it mean to businesses, why does it matter to us, and what potential issues could arise from it?
This document provides an overview of data science and how to get started in the field. It defines data science and the roles of data scientists. It also outlines the data science process of framing questions, collecting and cleaning data, exploring patterns, and communicating results. Examples are given of how companies like LinkedIn and Netflix used data science to improve their businesses. The document recommends learning tools like SQL, analytics software, and machine learning algorithms and describes a data science bootcamp program for gaining these skills through mentorship and hands-on projects.
This document provides an overview of data science and how to get started in the field. It defines data science and the roles of data scientists. It also outlines the data science process of framing questions, collecting and cleaning data, exploring patterns, and communicating results. Examples are given of how companies like LinkedIn and Netflix used data science to improve their businesses. The document recommends learning tools like SQL, analytics software, and machine learning algorithms and describes a data science bootcamp program for gaining these skills through mentorship and hands-on projects.
ग्रेटर मुंबई के नगर आयुक्त को एक खुले पत्र में याचिका दायर कर 540 से अधिक मुंबईकरों ने सभी अवैध और अस्थिर होर्डिंग्स, साइनबोर्ड और इलेक्ट्रिक साइनेज को तत्काल हटाने और 13 मई, 2024 की शाम को घाटकोपर में अवैध होर्डिंग के गिरने की विनाशकारी घटना के बाद अपराधियों के खिलाफ सख्त कार्रवाई की मांग की है, जिसमें 17 लोगों की जान चली गई और कई निर्दोष लोग गंभीर रूप से घायल हो गए।
Shark Tank Jargon | Operational ProfitabilityTheUnitedIndian
Don't let fancy business words confuse you! This blog is your cheat sheet to understanding the Shark Tank Jargon. We'll translate all the confusing terms like "valuation" (how much the company is worth) and "royalty" (a fee for using someone's idea). You'll be swimming with the Sharks like a pro in no time!
Recent years have seen a disturbing rise in violence, discrimination, and intolerance against Christian communities in various Islamic countries. This multifaceted challenge, deeply rooted in historical, social, and political animosities, demands urgent attention. Despite the escalating persecution, substantial support from the Western world remains lacking.
18062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
15062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
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लालू यादव की जीवनी LALU PRASAD YADAV BIOGRAPHYVoterMood
Discover the life and times of Lalu Prasad Yadav with a comprehensive biography in Hindi. Learn about his early days, rise in politics, controversies, and contribution.
Federal Authorities Urge Vigilance Amid Bird Flu Outbreak | The Lifesciences ...The Lifesciences Magazine
Federal authorities have advised the public to remain vigilant but calm in response to the ongoing bird flu outbreak of highly pathogenic avian influenza, commonly known as bird flu.
Slide deck with charts from our Digital News Report 2024, the most comprehensive exploration of news consumption habits around the world, based on survey data from more than 95,000 respondents across 47 countries.
#WenguiGuo#WashingtonFarm Guo Wengui Wolf son ambition exposed to open a far...rittaajmal71
Since fleeing to the United States in 2014, Guo Wengui has founded a number of projects in the United States, such as GTV Media Group, GTV private equity, farm loan project, G Club Operations Co., LTD., and Himalaya Exchange.
16062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
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13062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
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Why We Chose ScyllaDB over DynamoDB for "User Watch Status"ScyllaDB
Yichen Wei and Adam Drennan share the architecture and technical requirements behind "user watch status" for a major global media streaming service, what that meant for their database, the pros and cons of the many options they considered for replacing DynamoDB, why they ultimately chose ScyllaDB, and their lessons learned so far.
12062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
Christian persecution in Islamic countries has intensified, with alarming incidents of violence, discrimination, and intolerance. This article highlights recent attacks in Nigeria, Pakistan, Egypt, Iran, and Iraq, exposing the multifaceted challenges faced by Christian communities. Despite the severity of these atrocities, the Western world's response remains muted due to political, economic, and social considerations. The urgent need for international intervention is underscored, emphasizing that without substantial support, the future of Christianity in these regions is at grave risk.
https://ecspe.org/the-rise-of-christian-persecution-in-islamic-countries/
3. 3 things that are true
Every story can be a data story.
Every journalist can be a data journalist.
Every newsroom can produce kick-ass data
journalism projects.
4. With every data project:
Ask what the goal is
● Drive traffic
● Social sharing/virality
● Public edification
● Social justice
● New editorial product
● Win awards
5. What challenge is your newsroom
facing?
● We don’t know where to get data/what to do
with the data we do have
● We don’t have the resources to do intensive
data analysis
● We don’t have the in-house tools/skills/talent
for a big fancy presentation
6. Get your staff in a
“data state of mind”
h/t to MaryJo Webster for this term
13. Ask: what’s the narrative? How do
we want the user to explore the
data? How are we communicating
that to them?
14. Common data project holdups
* Data is dirty
* Story isn’t focused
* Package has too many moving pieces
* Breaking news or daily news monopolizes
time of key players
* Lack of coordination
* Data is still dirty
16. Sometimes data means math
* Percent change
* Adjust for inflation
* Medians vs average
* Rates and ratios
Steve Doig’s tipsheet: http://www.ibiblio.org/slanews/conferences/sla2005/programs/mathcrib.htm