Data Science with Spark by Saeed Aghabozorgi
•
0 likes•822 views
Hi Spark Enthusiasts, We have finalized our agenda for the meetup on Saturday, April 16th. Data Scientist Saeed Aghabozorgi (https://ca.linkedin.com/in/saeedaghabozorgi) from IBM's Toronto Lab will talk about Data Science as it relates to Spark and show some demos. Agenda: • Data Science and Spark • Intro to Big Data University • Data Scientist Workbench • Spark programming with Python • Spark streaming demo Please spread the word and encourage your friends and colleagues to signup for this meetup. Cheers, Spark Technology Center
Report
Share
Report
Share
Recommended
IC-SDV 2019: The Economics of Artificial Intelligence and Machine Learning fo...
Decision points for when to implement automatic indexing or more intensive subject analysis
Machine learning and artificial intelligence approaches to automatic indexing and other aspects of content enrichment have tremendous potential, but there are significant barriers to successful implementations. The economics of these systems are not now generally affordable, which will indefinitely delay widespread adoption. Significant costs are involved in just the training and maintaining systems that chronically under perform and are fail to scale. Cost and performance data will be characterized and presented. Machine learning and artificial intelligence projects are not for the faint of heart, nor for those with small budgets. Key cost elements are identified along with approaches to estimating costs based on actual and reported cases.
Big data analytics
This document discusses big data analytics. It defines big data as high volume information that is difficult to analyze and make decisions on quickly. Big data analytics is the process of organizing, collecting, and analyzing large, complex datasets using techniques like machine learning, natural language processing, and visualization. The document outlines some applications of big data analytics including government, healthcare, and retail. It also discusses challenges of big data like increased storage needs and faster incoming data.
Student Data Privacy: What Teachers and Administrators Need to KNow
An education technology startup called inBloom that collected and stored student data shut down in 2014 due to privacy concerns from parents in New York. In 2015, there was a large amount of legislation proposed or passed related to student data privacy across 46 states, including California's Student Online Personal Information Protection Act. There is ongoing debate around expanding the use of education technology tools in schools while addressing privacy fears over how student data is collected, used, secured, and accessed.
Submit Your Research Articles - International Journal of Information Sciences...
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
AI-SDV 2020: AI, IoT, Blockchain & Co: How to keep track and take advantage o...
The presentation first addresses 6 key questions: Where do we stand with the diversification of AI? Machines are getting better at understanding - where are we with writing? Has AI Research hit a wall in 2020? What's the progress made in AI ethics? Could synthetically created data make AI cheaper? And: Are deep fake deployments of AI becoming more insidious? The presentation then moves on to discuss the 4th generation of AI: Artificial Intuition. It discusses the lines of effort in AI of the US Government and concludes with three actualities on AI-based decision making, COVID-loneliness detection and extrapolation of AI into the long run.
What is the Living room of the future for #mydata2019
https://mydata2019.org/programme-page/public-spaces-private-data/
More and more people live their lives online, and we are encouraged to view the internet as a public space. However the personal data we bring to this space can be used in many inappropriate ways: Instagram stories are scraped to target advertisement; faces in family photographs are used to train the ML systems that will scan crowds for suspects; the devices we thought we owned end up owning us; and our browsing histories are stored and scanned by governments and private companies. This creates a tension for public service organisations as they try to deliver value to audiences and users online.
In this session experts from the BBC Research & Development, Finnish Broadcasting Company YLE, and PublicSpaces will consider how to resolve these tensions, and look at some specific interventions aimed at providing value to audiences and communities through the responsible use of private data in online public spaces.
Submit Your Research Articles - International Journal of Information Sciences...
The International Journal of Information Science & Techniques (IJIST ) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
Informatics Engineering, an International Journal (IEIJ)
The document is a call for papers for the Informatics Engineering, an International Journal. It discusses that informatics is a rapidly developing field involving human-computer interaction and interface design. It aims to bring together researchers from academia and industry working in areas of computing, communication, multimedia, and human-computer interaction. Topics of interest include computational complexity, artificial intelligence, database mining, health informatics and more. Authors are invited to submit original papers by the given deadlines.
Recommended
IC-SDV 2019: The Economics of Artificial Intelligence and Machine Learning fo...
Decision points for when to implement automatic indexing or more intensive subject analysis
Machine learning and artificial intelligence approaches to automatic indexing and other aspects of content enrichment have tremendous potential, but there are significant barriers to successful implementations. The economics of these systems are not now generally affordable, which will indefinitely delay widespread adoption. Significant costs are involved in just the training and maintaining systems that chronically under perform and are fail to scale. Cost and performance data will be characterized and presented. Machine learning and artificial intelligence projects are not for the faint of heart, nor for those with small budgets. Key cost elements are identified along with approaches to estimating costs based on actual and reported cases.
Big data analytics
This document discusses big data analytics. It defines big data as high volume information that is difficult to analyze and make decisions on quickly. Big data analytics is the process of organizing, collecting, and analyzing large, complex datasets using techniques like machine learning, natural language processing, and visualization. The document outlines some applications of big data analytics including government, healthcare, and retail. It also discusses challenges of big data like increased storage needs and faster incoming data.
Student Data Privacy: What Teachers and Administrators Need to KNow
An education technology startup called inBloom that collected and stored student data shut down in 2014 due to privacy concerns from parents in New York. In 2015, there was a large amount of legislation proposed or passed related to student data privacy across 46 states, including California's Student Online Personal Information Protection Act. There is ongoing debate around expanding the use of education technology tools in schools while addressing privacy fears over how student data is collected, used, secured, and accessed.
Submit Your Research Articles - International Journal of Information Sciences...
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
AI-SDV 2020: AI, IoT, Blockchain & Co: How to keep track and take advantage o...
The presentation first addresses 6 key questions: Where do we stand with the diversification of AI? Machines are getting better at understanding - where are we with writing? Has AI Research hit a wall in 2020? What's the progress made in AI ethics? Could synthetically created data make AI cheaper? And: Are deep fake deployments of AI becoming more insidious? The presentation then moves on to discuss the 4th generation of AI: Artificial Intuition. It discusses the lines of effort in AI of the US Government and concludes with three actualities on AI-based decision making, COVID-loneliness detection and extrapolation of AI into the long run.
What is the Living room of the future for #mydata2019
https://mydata2019.org/programme-page/public-spaces-private-data/
More and more people live their lives online, and we are encouraged to view the internet as a public space. However the personal data we bring to this space can be used in many inappropriate ways: Instagram stories are scraped to target advertisement; faces in family photographs are used to train the ML systems that will scan crowds for suspects; the devices we thought we owned end up owning us; and our browsing histories are stored and scanned by governments and private companies. This creates a tension for public service organisations as they try to deliver value to audiences and users online.
In this session experts from the BBC Research & Development, Finnish Broadcasting Company YLE, and PublicSpaces will consider how to resolve these tensions, and look at some specific interventions aimed at providing value to audiences and communities through the responsible use of private data in online public spaces.
Submit Your Research Articles - International Journal of Information Sciences...
The International Journal of Information Science & Techniques (IJIST ) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
Informatics Engineering, an International Journal (IEIJ)
The document is a call for papers for the Informatics Engineering, an International Journal. It discusses that informatics is a rapidly developing field involving human-computer interaction and interface design. It aims to bring together researchers from academia and industry working in areas of computing, communication, multimedia, and human-computer interaction. Topics of interest include computational complexity, artificial intelligence, database mining, health informatics and more. Authors are invited to submit original papers by the given deadlines.
To share or not to share? machine generated data for science
This document discusses the sharing of machine-generated data for scientific research. It notes that data is being generated everywhere through devices like connected cars, smart home devices, and sensors in farming and power grids. However, companies are often reluctant to share this data without incentives. The document proposes some solutions, including regulation to allow text and data mining of commercial data for public research, as well as encouraging data collaboratives and defining "public interest data" that can be shared. Overall it argues that machine-generated data has huge potential for research but challenges around incentives, risks, and standardization must be addressed.
Rising tide of data update 20171024
This document discusses the rising tide of data in science and the opportunities and challenges it presents. It outlines how scientific breakthroughs are increasingly powered by advanced computing capabilities applied to massive datasets. Open sharing of research data allows for errors to be identified and theories to be supported, rejected or refined, improving reproducibility. However, a survey found that many scientists believe there is a reproducibility crisis. Maximizing the value of data requires making it FAIR (findable, accessible, interoperable, and reusable). Examples provided demonstrate how open data sharing has benefited fields like epidemiology and agriculture.
Rising tide of data update
Presentation to Federation Uni Australia, 24 Oct 2017, Keith Russell, ANDS Partnership Projects Manager (ands.org.au)
from data to artificial intelligence, from laboratory to industry
This document discusses artificial intelligence (AI) research and commercialization in Indonesia. It notes that while Indonesia ranks 41st globally in AI publications, cooperation between Indonesian universities is still low. It highlights some AI research topics at Indonesian universities, including deep learning, neural networks, and natural language processing. The document also presents some university AI use cases, such as road scene segmentation using edge computing and deep learning for cervical cell classification. It argues that to further innovation, Indonesia needs more open data and support from industry and government to help move AI research at universities to commercialization through startups.
Big Data and Data Science: Opportunities for Biomedical Engineering
American Institute of Medical and Biological Engineeting (AIMBE) presentation to the Academic Council. Washington DC, April 8, 2018
Ggmuk conf talk-samanthaahern
This document discusses several topics related to data ethics and privacy laws:
1. It examines the ethical and legal considerations around data collection from the Internet of Things and big data, as well as individuals' privacy rights.
2. It provides an overview of the General Data Protection Regulation (GDPR) which takes effect in 2018 and restricts personal data transfers outside the EU.
3. It notes recommendations for higher education institutions around implementing learning analytics systems, including obtaining student consent and having clear data policies to address privacy, security, and use of data.
Kalpana Shankar: Public-Private Partnerships and the Case of Geneaology: Thre...
Professor Kalpana Shankar discusses public-private partnerships (PPPs) in data organizations and uses genealogy as a case study. PPPs between commercial entities and public organizations can help make data archives more sustainable through new revenue streams but also risks privatizing open data. In genealogy, while companies digitize historical records, it can alienate records from institutions and raise issues of transparency. Long-term, PPPs could lead to open data becoming proprietary through restrictive metadata or expectations of users shifting to prioritize commercial partners over public interests.
Submit Your Research Articles - International Journal of Information Sciences...
The International Journal of Information Science & Techniques (IJIST ) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
What is Data Science
Looking for an objective, complete, inclusive, accurate and succinct definition of this emerging field
UK data management environment and support
The document discusses a leaders conference on UK data management environments and support. It provides information on the current UK research data management policy environment, systems used, and challenges. It introduces Jisc's proposed Research Data Shared Service as a sector-wide approach to address these issues by providing a single, integrated solution for research data management across the UK. Key benefits identified include optimizing costs, growing the value of research data, and increasing compliance with funder requirements for data preservation and sharing. The development history and features of the proposed shared service are outlined.
Submit Your Research Articles - International Journal of Information Sciences...
The International Journal of Information Science & Techniques (IJIST ) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
Open data for innovation, smart and sustainable prof muliaro
1) The document discusses how open data can support smart and sustainable development through enabling innovation, creative economies, and ICT applications in areas like disaster management and smart learning.
2) It provides examples of how open data principles and policies can be developed, highlighting the importance of context, content, and impact.
3) JKUAT's open research data policy and open data platform are presented as case studies of enabling open data sharing and its benefits.
Ibm csoc response_future _ai 20160907 v5
The document is IBM's response to the White House Office of Science and Technology Policy's request for input on artificial intelligence. It addresses 11 topics, providing an overview of key issues like the use of AI for public good, the social and economic implications of AI, important research gaps, and the need for multi-disciplinary collaboration and open data sets to advance the field safely. It recommends focusing on skills and having a responsible dialogue to develop AI for societal benefit.
Algorithms are biased because we are. Are we willing to change?
Algorithms are just as biased as we are. But how do we identify these biases and how do we change our ways to make our technology better and more impactful for everyone?
Presentation given at the Intel Dev Fest 2019
Building Real Time, Open-Source Tools for Wikipedia
Building Real Time, Open-Source Tools for Wikipedia
with Rob Kenedi
Presented at FITC Toronto 2015
More info at www.fitc.ca
OVERVIEW
Wikipedia is one of the most frequently visited websites in the world. The vast online encyclopedia, editable by anyone, has become the go-to source for general information on any subject. Building user-friendly apps that people can actually use on top of Wikipedia’s massive dataset involves overcoming a number of challenges, but it can also be a lot of fun. Join Rob Kenedi, Entrepreneur In Residence at The Working Group (TWG) as he shares lessons learned in TWG’s Lab building WikiWash, a free tool for journalists that helps them uncover spin and bias in Wikipedia.
OBJECTIVE
Learn how to build useful products using Wikipedia’s dataset.
TARGET AUDIENCE
Software developers, data journalists, product managers
ASSUMED AUDIENCE KNOWLEDGE
What Wikipedia is, and how web applications work
FIVE THINGS AUDIENCE MEMBERS WILL LEARN
How we built an open-source tool for journalists using Wikipedia
How to manage the massive amounts of data in Wikipedia
How to turn a non-technical pitch presentation into a working product that the client loves
How TWG labs treats its projects, products and prototypes and what happens to them once they launch
How WikiWash can be used to expose bias and spin on Wikipedia
Tim Berners-Lee's 5-Star Open Data Scheme
For our September 19th lecture... Tim Berners-Lee's 5-Star Open Data scheme.
Who is Tim Berners-Lee? What is the 5-Star Open Data scheme? We will be giving an introduction to open data and how to apply the 5-Star Open Data scheme to an open data program.
Academic IT support for Data Science
Paper presentation at EUNIS 2016 conference, Thessaloniki, Greece. Globally, over 500 universities now offer data science courses at undergraduate or postgraduate level and, in research-intensive universities, these courses are typically underpinned by academic research in statistics, machine learning and computer science departments and, increasingly, in multidisciplinary data science institutes. Much has been written about the academic challenges of data science from the perspective of its core academic disciplines and from its application domains, ranging from sciences and engineering through to arts and humanities. However, relatively little has been written about the institutional information technology (IT) support challenges entailed by this rapid growth in data science. This paper sets out some of these IT challenges and examines competing support strategies, service design and financial models through the lens of academic IT support services.
Deep Dive : Spark Data Frames, SQL and Catalyst Optimizer
RDD recap
Spark SQL library
Architecture of Spark SQL
Comparison with Pig and Hive Pipeline
DataFrames
Definition of a DataFrames API
DataFrames Operations
DataFrames features
Data cleansing
Diagram for logical plan container
Plan Optimization & Execution
Catalyst Analyzer
Catalyst Optimizer
Generating Physical Plan
Code Generation
Extensions
Actian Vector Whitepaper
Actian Vector is a high performance analytics database that exploits modern CPU features like SIMD instructions to process large volumes of data much faster than traditional databases. It uses CPU caches rather than RAM for execution memory and avoids overhead by processing vectors of data at once. Actian Vector also leverages industry best practices like columnar storage and compression to optimize input/output and further improve performance for data warehouse workloads. Its unique ability to fully utilize CPU features allows it to run workloads on a single server that would require multiple servers on other databases.
Actian Analytics Platform - Hadoop SQL Edition
The Actian Analytics Platform™ Accelerate your transition to Big Data 2.0. Transform big data into business value with the Actian Analytics Platform.
- Create transformational value along with many innovative companies
- Deliver next generation analytics with our comprehensive platform
- Accelerate your on-ramp for big data with disruptive price performance
The Actian Analytics Platform-Hadoop SQL Edition accelerates Hadoop and makes it enterprise-grade by providing high-performance data enrichment, visual design and SQL analytics on Hadoop without the need for MapReduce skills. The platform edition uniquely enhances and extends Hadoop natively for both discovery and time-sensitive analytics using next generation capabilities:
- Parallelized Pipelining – run high performance analytics natively on Hadoop 10X+ faster
- Data Science Workbench – visually prep and analyze Hadoop data without coding
- Ultrafast SQL on Hadoop – deliver industrialized access to Hadoop data to business users
With Actian, you can run Hadoop at the speed and scale needed to help your organization transform big data into business value.
Analytics at the Speed of Thought: Actian Express Overview
Deliver faster insight – reduce query response times to seconds
Analyze more data faster – explore billions of rows of data in seconds
More concurrent users – enable more concurrent BI users to explore more data
Jump start your analytics investments and accelerate analytics ROI
The document discusses Actian's Clear Path Program which aims to help companies accelerate their customer analytics ROI through the use of pre-built analytics blueprints. The program provides blueprints, workflows, and workshops to help customers address challenges in areas like customer profiling, segmentation, lifetime value, campaign optimization, and churn analysis. It also describes Actian's Analytics Center of Excellence which develops the blueprints by applying data science expertise to real-world problems. Example use cases are provided that demonstrate how the blueprints can help optimize campaigns, ad placement, and increase revenues.
More Related Content
What's hot
To share or not to share? machine generated data for science
This document discusses the sharing of machine-generated data for scientific research. It notes that data is being generated everywhere through devices like connected cars, smart home devices, and sensors in farming and power grids. However, companies are often reluctant to share this data without incentives. The document proposes some solutions, including regulation to allow text and data mining of commercial data for public research, as well as encouraging data collaboratives and defining "public interest data" that can be shared. Overall it argues that machine-generated data has huge potential for research but challenges around incentives, risks, and standardization must be addressed.
Rising tide of data update 20171024
This document discusses the rising tide of data in science and the opportunities and challenges it presents. It outlines how scientific breakthroughs are increasingly powered by advanced computing capabilities applied to massive datasets. Open sharing of research data allows for errors to be identified and theories to be supported, rejected or refined, improving reproducibility. However, a survey found that many scientists believe there is a reproducibility crisis. Maximizing the value of data requires making it FAIR (findable, accessible, interoperable, and reusable). Examples provided demonstrate how open data sharing has benefited fields like epidemiology and agriculture.
Rising tide of data update
Presentation to Federation Uni Australia, 24 Oct 2017, Keith Russell, ANDS Partnership Projects Manager (ands.org.au)
from data to artificial intelligence, from laboratory to industry
This document discusses artificial intelligence (AI) research and commercialization in Indonesia. It notes that while Indonesia ranks 41st globally in AI publications, cooperation between Indonesian universities is still low. It highlights some AI research topics at Indonesian universities, including deep learning, neural networks, and natural language processing. The document also presents some university AI use cases, such as road scene segmentation using edge computing and deep learning for cervical cell classification. It argues that to further innovation, Indonesia needs more open data and support from industry and government to help move AI research at universities to commercialization through startups.
Big Data and Data Science: Opportunities for Biomedical Engineering
American Institute of Medical and Biological Engineeting (AIMBE) presentation to the Academic Council. Washington DC, April 8, 2018
Ggmuk conf talk-samanthaahern
This document discusses several topics related to data ethics and privacy laws:
1. It examines the ethical and legal considerations around data collection from the Internet of Things and big data, as well as individuals' privacy rights.
2. It provides an overview of the General Data Protection Regulation (GDPR) which takes effect in 2018 and restricts personal data transfers outside the EU.
3. It notes recommendations for higher education institutions around implementing learning analytics systems, including obtaining student consent and having clear data policies to address privacy, security, and use of data.
Kalpana Shankar: Public-Private Partnerships and the Case of Geneaology: Thre...
Professor Kalpana Shankar discusses public-private partnerships (PPPs) in data organizations and uses genealogy as a case study. PPPs between commercial entities and public organizations can help make data archives more sustainable through new revenue streams but also risks privatizing open data. In genealogy, while companies digitize historical records, it can alienate records from institutions and raise issues of transparency. Long-term, PPPs could lead to open data becoming proprietary through restrictive metadata or expectations of users shifting to prioritize commercial partners over public interests.
Submit Your Research Articles - International Journal of Information Sciences...
The International Journal of Information Science & Techniques (IJIST ) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
What is Data Science
Looking for an objective, complete, inclusive, accurate and succinct definition of this emerging field
UK data management environment and support
The document discusses a leaders conference on UK data management environments and support. It provides information on the current UK research data management policy environment, systems used, and challenges. It introduces Jisc's proposed Research Data Shared Service as a sector-wide approach to address these issues by providing a single, integrated solution for research data management across the UK. Key benefits identified include optimizing costs, growing the value of research data, and increasing compliance with funder requirements for data preservation and sharing. The development history and features of the proposed shared service are outlined.
Submit Your Research Articles - International Journal of Information Sciences...
The International Journal of Information Science & Techniques (IJIST ) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
Open data for innovation, smart and sustainable prof muliaro
1) The document discusses how open data can support smart and sustainable development through enabling innovation, creative economies, and ICT applications in areas like disaster management and smart learning.
2) It provides examples of how open data principles and policies can be developed, highlighting the importance of context, content, and impact.
3) JKUAT's open research data policy and open data platform are presented as case studies of enabling open data sharing and its benefits.
Ibm csoc response_future _ai 20160907 v5
The document is IBM's response to the White House Office of Science and Technology Policy's request for input on artificial intelligence. It addresses 11 topics, providing an overview of key issues like the use of AI for public good, the social and economic implications of AI, important research gaps, and the need for multi-disciplinary collaboration and open data sets to advance the field safely. It recommends focusing on skills and having a responsible dialogue to develop AI for societal benefit.
Algorithms are biased because we are. Are we willing to change?
Algorithms are just as biased as we are. But how do we identify these biases and how do we change our ways to make our technology better and more impactful for everyone?
Presentation given at the Intel Dev Fest 2019
Building Real Time, Open-Source Tools for Wikipedia
Building Real Time, Open-Source Tools for Wikipedia
with Rob Kenedi
Presented at FITC Toronto 2015
More info at www.fitc.ca
OVERVIEW
Wikipedia is one of the most frequently visited websites in the world. The vast online encyclopedia, editable by anyone, has become the go-to source for general information on any subject. Building user-friendly apps that people can actually use on top of Wikipedia’s massive dataset involves overcoming a number of challenges, but it can also be a lot of fun. Join Rob Kenedi, Entrepreneur In Residence at The Working Group (TWG) as he shares lessons learned in TWG’s Lab building WikiWash, a free tool for journalists that helps them uncover spin and bias in Wikipedia.
OBJECTIVE
Learn how to build useful products using Wikipedia’s dataset.
TARGET AUDIENCE
Software developers, data journalists, product managers
ASSUMED AUDIENCE KNOWLEDGE
What Wikipedia is, and how web applications work
FIVE THINGS AUDIENCE MEMBERS WILL LEARN
How we built an open-source tool for journalists using Wikipedia
How to manage the massive amounts of data in Wikipedia
How to turn a non-technical pitch presentation into a working product that the client loves
How TWG labs treats its projects, products and prototypes and what happens to them once they launch
How WikiWash can be used to expose bias and spin on Wikipedia
Tim Berners-Lee's 5-Star Open Data Scheme
For our September 19th lecture... Tim Berners-Lee's 5-Star Open Data scheme.
Who is Tim Berners-Lee? What is the 5-Star Open Data scheme? We will be giving an introduction to open data and how to apply the 5-Star Open Data scheme to an open data program.
Academic IT support for Data Science
Paper presentation at EUNIS 2016 conference, Thessaloniki, Greece. Globally, over 500 universities now offer data science courses at undergraduate or postgraduate level and, in research-intensive universities, these courses are typically underpinned by academic research in statistics, machine learning and computer science departments and, increasingly, in multidisciplinary data science institutes. Much has been written about the academic challenges of data science from the perspective of its core academic disciplines and from its application domains, ranging from sciences and engineering through to arts and humanities. However, relatively little has been written about the institutional information technology (IT) support challenges entailed by this rapid growth in data science. This paper sets out some of these IT challenges and examines competing support strategies, service design and financial models through the lens of academic IT support services.
What's hot (17)
To share or not to share? machine generated data for science
To share or not to share? machine generated data for science
from data to artificial intelligence, from laboratory to industry
from data to artificial intelligence, from laboratory to industry
Big Data and Data Science: Opportunities for Biomedical Engineering
Big Data and Data Science: Opportunities for Biomedical Engineering
Kalpana Shankar: Public-Private Partnerships and the Case of Geneaology: Thre...
Kalpana Shankar: Public-Private Partnerships and the Case of Geneaology: Thre...
Submit Your Research Articles - International Journal of Information Sciences...
Submit Your Research Articles - International Journal of Information Sciences...
Submit Your Research Articles - International Journal of Information Sciences...
Submit Your Research Articles - International Journal of Information Sciences...
Open data for innovation, smart and sustainable prof muliaro
Open data for innovation, smart and sustainable prof muliaro
Algorithms are biased because we are. Are we willing to change?
Algorithms are biased because we are. Are we willing to change?
Building Real Time, Open-Source Tools for Wikipedia
Building Real Time, Open-Source Tools for Wikipedia
Viewers also liked
Deep Dive : Spark Data Frames, SQL and Catalyst Optimizer
RDD recap
Spark SQL library
Architecture of Spark SQL
Comparison with Pig and Hive Pipeline
DataFrames
Definition of a DataFrames API
DataFrames Operations
DataFrames features
Data cleansing
Diagram for logical plan container
Plan Optimization & Execution
Catalyst Analyzer
Catalyst Optimizer
Generating Physical Plan
Code Generation
Extensions
Actian Vector Whitepaper
Actian Vector is a high performance analytics database that exploits modern CPU features like SIMD instructions to process large volumes of data much faster than traditional databases. It uses CPU caches rather than RAM for execution memory and avoids overhead by processing vectors of data at once. Actian Vector also leverages industry best practices like columnar storage and compression to optimize input/output and further improve performance for data warehouse workloads. Its unique ability to fully utilize CPU features allows it to run workloads on a single server that would require multiple servers on other databases.
Actian Analytics Platform - Hadoop SQL Edition
The Actian Analytics Platform™ Accelerate your transition to Big Data 2.0. Transform big data into business value with the Actian Analytics Platform.
- Create transformational value along with many innovative companies
- Deliver next generation analytics with our comprehensive platform
- Accelerate your on-ramp for big data with disruptive price performance
The Actian Analytics Platform-Hadoop SQL Edition accelerates Hadoop and makes it enterprise-grade by providing high-performance data enrichment, visual design and SQL analytics on Hadoop without the need for MapReduce skills. The platform edition uniquely enhances and extends Hadoop natively for both discovery and time-sensitive analytics using next generation capabilities:
- Parallelized Pipelining – run high performance analytics natively on Hadoop 10X+ faster
- Data Science Workbench – visually prep and analyze Hadoop data without coding
- Ultrafast SQL on Hadoop – deliver industrialized access to Hadoop data to business users
With Actian, you can run Hadoop at the speed and scale needed to help your organization transform big data into business value.
Analytics at the Speed of Thought: Actian Express Overview
Deliver faster insight – reduce query response times to seconds
Analyze more data faster – explore billions of rows of data in seconds
More concurrent users – enable more concurrent BI users to explore more data
Jump start your analytics investments and accelerate analytics ROI
The document discusses Actian's Clear Path Program which aims to help companies accelerate their customer analytics ROI through the use of pre-built analytics blueprints. The program provides blueprints, workflows, and workshops to help customers address challenges in areas like customer profiling, segmentation, lifetime value, campaign optimization, and churn analysis. It also describes Actian's Analytics Center of Excellence which develops the blueprints by applying data science expertise to real-world problems. Example use cases are provided that demonstrate how the blueprints can help optimize campaigns, ad placement, and increase revenues.
Platform for Data Scientists
This document discusses a platform for data scientists that aims to automate routine jobs, maximize resource utilization, and allow data scientists to focus more on business solutions. The platform provides capabilities for data capture, analysis, modeling, and output of analytics. It seeks to reduce the time taken to turn data into insights from months to weeks. Key elements of the platform include tools for exploratory data analysis, advanced modeling, distributed architecture, bespoke algorithms, and packaged analytics solutions.
Turning Your Data Lake into Measurable Business Value
1. The document discusses turning big data into business value by adding ingredients to Hadoop, including a visual framework, KNIME operators and R analytics, a high-performance data processing engine, a vector processing engine, an extreme performance analytics engine, and blueprints to accelerate application development.
2. It promotes an upcoming webinar on June 17th to announce the "seventh ingredient" being added to Hadoop by Actian to create value from big data.
3. The presentation was given by Jim Walker of Hortonworks and John Santaferraro of Actian.
Telco analytics at scale
This document discusses Telco analytics at scale using distributed stream processing. It describes using technologies like Apache Spark Streaming, Kafka, and Hadoop (HDFS, Hive, HBase) to ingest and process large volumes of streaming data from various sources in real-time or near real-time. Example use cases discussed include fraud detection, real-time rating, security information and event management. It also covers strategies for distributed in-memory caching and rule processing to enable low latency analytics at high throughput scales needed for telco data and applications.
Interactive Data Analysis in Spark Streaming
This document discusses strategies for building interactive streaming applications in Spark Streaming. It describes using Zookeeper as a dynamic configuration source to allow modifying a Spark Streaming application's behavior at runtime. The key points are:
- Zookeeper can be used to track configuration changes and trigger Spark Streaming context restarts through its watch mechanism and Curator library.
- This allows building interactive applications that can adapt to configuration updates without needing to restart the whole streaming job.
- Examples are provided of using Curator caches like node and path caches to monitor Zookeeper for changes and restart Spark Streaming contexts in response.
Building scalable rest service using Akka HTTP
Akka HTTP is a toolkit for building scalable REST services in Scala. It provides a high-level API built on top of Akka actors and Akka streams for writing asynchronous, non-blocking and resilient microservices. The document discusses Akka HTTP's architecture, routing DSL, directives, testing, additional features like file uploads and websockets. It also compares Akka HTTP to other Scala frameworks and outlines pros and cons of using Akka HTTP for building REST APIs.
Digital Workspace
BearingPoint has developed a comprehensive approach to deal with large workspace outfit projects, managed as real “transformation” projects involving major changes around work modes, workspaces and IT/digital equipment.
Apache Spark Introduction and Resilient Distributed Dataset basics and deep dive
We will give a detailed introduction to Apache Spark and why and how Spark can change the analytics world. Apache Spark's memory abstraction is RDD (Resilient Distributed DataSet). One of the key reason why Apache Spark is so different is because of the introduction of RDD. You cannot do anything in Apache Spark without knowing about RDDs. We will give a high level introduction to RDD and in the second half we will have a deep dive into RDDs.
Greenplum Analytics Workbench - What Can a Private Hadoop Cloud Do For You?
This session discusses the rational behind the Greenplum Analytics Workbench initiative - it's goals, present status today and the roadmap for this first of a kind initiative. Enterprises learn about how a Hadoop Cloud can help unlock revenue opportunities from the data within the cluster.
Viewers also liked (13)
Deep Dive : Spark Data Frames, SQL and Catalyst Optimizer
Deep Dive : Spark Data Frames, SQL and Catalyst Optimizer
Analytics at the Speed of Thought: Actian Express Overview
Analytics at the Speed of Thought: Actian Express Overview
Jump start your analytics investments and accelerate analytics ROI
Jump start your analytics investments and accelerate analytics ROI
Turning Your Data Lake into Measurable Business Value
Turning Your Data Lake into Measurable Business Value
Apache Spark Introduction and Resilient Distributed Dataset basics and deep dive
Apache Spark Introduction and Resilient Distributed Dataset basics and deep dive
Greenplum Analytics Workbench - What Can a Private Hadoop Cloud Do For You?
Greenplum Analytics Workbench - What Can a Private Hadoop Cloud Do For You?
Similar to Data Science with Spark by Saeed Aghabozorgi
L3 Big Data and Application.pptx
This document provides an introduction to big data and data science from Amity Institute of Information Technology. It defines big data and data science, highlighting that big data is a subset of data science. The key differences between big data and data science are described. Examples of applications of big data in various domains like social media, healthcare, finance, ecommerce and education are outlined. Finally, the skills required to become a data scientist or big data specialist are summarized.
Data Scientists: Your Must-Have Business Investment
This document summarizes a presentation on data science and the role of data scientists. It discusses how data science has evolved from earlier fields like statistics and data mining. It also profiles common skills of data scientists like data integration, programming, analytics, and communication. Additionally, the presentation outlines how data science differs from traditional business intelligence by focusing more on prediction and interacting with large, unstructured datasets in real-time. The document promotes data science as a key business investment and announces an upcoming summer webinar series on related topics.
Data science courses part 4
The document is about a data science course offered by DataMites in Hyderabad, India. It provides an overview of the course's methodology, which uses conceptual learning and illustration of analytical solutions to business problems. The course offers lifetime access to a research lab for experimental projects. It also defines key concepts in data science like big data and the data science life cycle.
Data Visualization
The document discusses a panel on data visualization. It introduces the panelists from various organizations and their roles. Common themes of visual storytelling, easier comprehension of data through visualization, and spotting trends are discussed. John Teresko is quoted about how data visualization can help turn data into useful information. Examples of data visualization projects from different industries are briefly described.
The Analytics and Data Science Landscape
Presentation at the Analytics in Modern Taxation Meeting on Nov. 16, 2020, virtual https://www.mitre.org/acmta2020-conference-information
a-beginner-guide-to-an-incredible-technology-data-science.pdf
Data science is the sheer skill to gain competence in making sense of all the data pools that are being generated by organizations worldwide. From being the most promising and the hottest jobs in the world in 2023 global rankings by the World Economic Forum, you are sure to gain as a certified data scientist in the years to follow as well.
ppt1.pptx
This document proposes a theme on big data analytics research. It motivates the importance of big data due to the exponential growth of digital data and limitations of traditional databases. The power of big data analytics is discussed through its wide applications in health, policymaking, smart cities, education and robotics. The objectives are outlined as large-scale machine learning, distributed computing, theory development, and multi-disciplinary analytics. Hong Kong is well positioned for this research due to its institutions, industries and potential collaborators. A multi-university and interdisciplinary approach is advocated to tackle big data challenges and transform society through new technologies, applications, insights and knowledge.
KDD 2019 IADSS Workshop - Research Updates from Usama Fayyad & Hamit Hamutcu
The document discusses the need for standards in defining data science roles and skills. It notes there is currently wide variation in how data science roles are defined by different companies and academic programs. This leads to inefficiencies in recruitment and skills assessment. The Initiative for Analytics and Data Science Standards (IADSS) aims to address this by defining standard roles, skills, and career paths for data science professionals based on research involving industry experts and academics. The goal is to support the growth of the analytics industry by providing clarity around data science qualifications and expectations.
Introduction to Data Science
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Big data
This document proposes a theme on big data analytics research. It notes that the world's data storage capacity doubles every 40 months and discusses how big data can provide value across many areas like health, policymaking, education and more. The proposal recommends that Hong Kong develop a state-of-the-art big data platform to make a difference in areas like smart cities and support aging populations. It outlines objectives like large-scale machine learning from big data and discusses how Hong Kong is well-positioned for this research with experts across universities and potential collaborators in industry. The expected outcomes include new methodologies, applications impacting society and industry, and educational programs to cultivate big data leaders.
Introduction to Data Science.pptx
This document provides an introduction to data science. It defines data science as a multi-disciplinary field that uses scientific methods and processes to extract knowledge and insights from structured and unstructured data. The document discusses the importance and impact of data science on organizations and society. It also outlines common applications of data science and the roles and skills required for a career in data science.
Data Science Intro.pptx
This document provides an introduction to data science. It defines data science as a multi-disciplinary field that uses scientific methods and processes to extract knowledge and insights from structured and unstructured data. The document discusses the importance and impact of data science on organizations and society. It also outlines common applications of data science and the roles and skills required for a career in data science.
Data science for everyone
This document provides an overview of data science. It defines data science as an interdisciplinary field that uses scientific methods and processes to extract knowledge and insights from data. It discusses what constitutes data and how "big data" refers to extremely large and growing collections of data that are challenging to store and process using traditional tools. The document outlines some examples of the scale of big data generated daily. It also reviews the growing demand for data scientists and the skills needed in data science, including statistics, machine learning, data visualization, and related fields. Finally, it provides some learning resources for becoming a data scientist.
The 10 best data science training institutes in india 2020
Through this edition, ‘The 10 Best Data Science Training Institutes in India 2020’, The Knowledge Review tries to portray some of Best Data Science Training Institutes in India out there in order to create a glimpse of the inspiration for others to follow and August on their trail or create a new one.
Insight white paper_2014
The Insight Data Science Fellows Program is a 6-week postdoctoral training fellowship that teaches scientists industry skills in data science. The program is held in Silicon Valley and New York City and bridges the gap between academia and careers in data science. Fellows learn tools and techniques from mentors at companies and work on projects to gain skills and interview at mentor companies for jobs in data science.
Data science lecture2_doaa_mohey
This document presents an overview of data science by Doaa Mohey Eldin. It introduces data science and its main methods, then discusses how data science is used across different industries to solve problems and meet user needs. Examples are given of applications of data science at companies like IBM, Google, Facebook, Netflix and more. The conclusion emphasizes that data science can interpret data and behavior, with essential applications including internet search, recommendations, recognition and gaming.
Two-Phase TDS Approach for Data Anonymization To Preserving Bigdata Privacy
While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. There have been a various different privacy preserving mechanisms developed for protecting privacy at different stages (e.g. data generation, data storage, data processing) of big data life cycle. The goal of this paper is to provide a complete overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms and also we illustrate the infrastructure of big data and state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. This paper focus on the anonymization process, which significantly improve the scalability and efficiency of TDS (top-down-specialization) for data anonymization over existing approaches. Also, we discuss the challenges and future research directions related to preserving privacy in big data.
Scalable Predictive Analysis and The Trend with Big Data & AI
This document discusses Jongwook Woo's work with Big Data AI at CalStateLA. It introduces Woo and his background, provides an overview of big data and how distributed systems enable scalable analysis of massive datasets. It also describes predictive analytics using machine learning and deep learning on big data, and how integrating GPUs into big data clusters can improve parallel processing for tasks like traffic analysis.
From Big Data to Smart Data
This document discusses moving from big data to smart data. It summarizes three key points:
1) Big data focuses too much on volume and speed without ensuring useful insights. Smart data prioritizes understanding data quality and relationships to provide more value.
2) Organizations should first enrich data by adding metadata, interlinking related pieces, and providing a common layer before pursuing large volumes of raw data.
3) The document describes two success stories where Ontotext utilized semantic technologies and interlinked data sources to provide insightful analytics and answers to complex questions for clients in job market intelligence and asset recovery.
How to use e-analytics. When big is not so big
How can learning analytics can be used to support active learning biomedical education? The talk describes three concepts to explore to bring students to the center of a learning experience and how technology can support that shift.
Similar to Data Science with Spark by Saeed Aghabozorgi (20)
Data Scientists: Your Must-Have Business Investment
Data Scientists: Your Must-Have Business Investment
a-beginner-guide-to-an-incredible-technology-data-science.pdf
a-beginner-guide-to-an-incredible-technology-data-science.pdf
KDD 2019 IADSS Workshop - Research Updates from Usama Fayyad & Hamit Hamutcu
KDD 2019 IADSS Workshop - Research Updates from Usama Fayyad & Hamit Hamutcu
The 10 best data science training institutes in india 2020
The 10 best data science training institutes in india 2020
Two-Phase TDS Approach for Data Anonymization To Preserving Bigdata Privacy
Two-Phase TDS Approach for Data Anonymization To Preserving Bigdata Privacy
Scalable Predictive Analysis and The Trend with Big Data & AI
Scalable Predictive Analysis and The Trend with Big Data & AI
Recently uploaded
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
How to Add Chatter in the odoo 17 ERP Module
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Build a Module in Odoo 17 Using the Scaffold Method
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...Nguyen Thanh Tu Collection
https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdaneReimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Physical pharmaceutics notes for B.pharm students
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Advanced Java[Extra Concepts, Not Difficult].docx
This is part 2 of my Java Learning Journey. This contains Hashing, ArrayList, LinkedList, Date and Time Classes, Calendar Class and more.
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Manage Your Lost Opportunities in Odoo 17 CRM
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection
https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdRecently uploaded (20)
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
How to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold Method
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
Data Science with Spark by Saeed Aghabozorgi
- 1. @bigdatau DATA SCIENCE WITH SPARK CHALLENGES,SKILLS ANDTOOLS @ B I G D A T A U # B D U M E E T U P Saeed Aghabozorgi, PhD Data Scientist Emerging Technologies IBM Analytics, Canada April 16th, 2016 India
- 2. @bigdatau WHAT IS DATA SCIENCE? WHOIS DATA SCIENTIST? © 2015 BigDataUniversity.com2 Data science is an interdisciplinary field to extract knowledge or insights from data (Big Data, structured or unstructured) which is a continuation of some of the data analysis such as Data Mining, etc.
- 3. @bigdatau WHYWE SHOULD LEARN DATA SCIENCE? § Data science for Social Good § Data Science in Industry § Data Journalism § Demand and Supply © 2015 BigDataUniversity.com3
- 4. @bigdatau DATA SCIENCE: DEMAND >> SUPPLY
- 6. @bigdatau
- 7. @bigdatau WHAT IS BIG DATA UNIVERSITY (BDU)? –A community initiative led by IBM –@yourpace, @yourplace online courses about data –Developed by industry experts –Free courses –Certificate of completion and badges BigDataUniversity.com
- 8. @bigdatau TYPICAL BDUCOURSE STRUCTURE § 5 – 5 – 5 rule of thumb: – 5 lessons – 5 videos per lesson – 5 minutes per video § Each lesson has a hands-on lab (VMWare, Docker, Cloud) § Each course has a multiple-choice test of 20 questions § Short courses (Typical course duration: 4 hours) 8© 2016 BigDataUniversity.com @bigdatau | #BDUmeetup
- 9. @bigdatau 9
- 10. @bigdatau 10 BigDataUniversity.com BigDataUniversity.com.br In Portuguese: BigDataUniversity.com.cn In Chinese: © 2016 BigDataUniversity.com @bigdatau | #BDUmeetup
- 15. @bigdatau DATA SCIENTIST WORKBENCH § DataScientistWorkbench.com © 2015 BigDataUniversity.com15
- 16. @bigdatau© 2015 BigDataUniversity.com16 Open Refine IPython Notebook R Studio ZeppelinMyData DATA SCIENTIST WORKBENCH
- 17. @bigdatau DATASCIENTISTWORKBENCH §DSWB: –Free tools –On cloud –Pre-installed Apache Spark. –Upload your data easily §Open Refine –Clean your data §Jupyter/Ipython Notebooks –Write your code, document it, and see the output... all in the same interface in the cloud. §Rstudio
- 18. @bigdatau SAMPLE TWEETS 18© 2016 BigDataUniversity.com @bigdatau | #BDUmeetup Learning #datascience with #Spark @BigDataU at IBM's Blr EGL Campus @apachespark_tc @IBMbigdata @IBMAnalytics #bdumeetup #IBMSparkMeetup
- 19. @bigdatau DATA SCIENTIST WORKBENCH § Demo § 3-min Tutorial for the Data Scientist Workbench § Data Scientist Workbench Tutorial | Big Data University http://bit.ly/dswb_tutorial Or http://bigdatauniversity.com/bdu-wp/bdu-course/data-scientist- workbench-tutorial/