Very brief introduction to R software that I have presented at UNISZA. No R codes and No Statistical Contents. Basically for those who just heard about R software for the first time
Basic introduction to "R", a free and open source statistical programming language designed to help users analyze data sets by creating scripts to increase automation. The program can also be used as a free substitute for Microsoft Excel.
Webinar : Introduction to R Programming and Machine LearningEdureka!
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
The topics covered in the presentation are:
1.What is R
2.Domains and Companies in which R is used
3.Characteristics of R
4.Get an Overview of Machine Learning
5.Understand the difference between supervised and unsupervised learning
6.Learn Clustering and K-means Clustering
7.Implement K-means Clustering in R
8.Google Trends in R
Very brief introduction to R software that I have presented at UNISZA. No R codes and No Statistical Contents. Basically for those who just heard about R software for the first time
Basic introduction to "R", a free and open source statistical programming language designed to help users analyze data sets by creating scripts to increase automation. The program can also be used as a free substitute for Microsoft Excel.
Webinar : Introduction to R Programming and Machine LearningEdureka!
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
The topics covered in the presentation are:
1.What is R
2.Domains and Companies in which R is used
3.Characteristics of R
4.Get an Overview of Machine Learning
5.Understand the difference between supervised and unsupervised learning
6.Learn Clustering and K-means Clustering
7.Implement K-means Clustering in R
8.Google Trends in R
This slide will provide an overview of current functionality, techniques, and tips for visualization and query of HDF and netCDF data in ArcGIS, as well as future plans. Hierarchical Data Format (HDF) and netCDF (network Common Data Form) are two widely used data formats for storing and manipulating scientific data. The NetCDF format also supports temporal data by using multidimensional arrays. The basic structure of data in this format and how to work with it will be covered in the context of standardized data structures and conventions. This slide will demonstrate the tools and techniques for ingesting HDF and netCDF data efficiently in ArcGIS, as well as some common workflows to employ the visualization capabilities of ArcGIS for effective animation and analysis of your data.
Incremental Export of Relational Database Contents into RDF GraphsNikolaos Konstantinou
In addition to tools offering RDF views over databases, a variety of tools exist that allow exporting database contents into RDF graphs; tools proven that in many cases demonstrate better performance than the former. However, in cases when database contents are exported into RDF, it is not always optimal or even necessary to dump the whole database contents every time. In this paper, the problem of incremental generation and storage of the resulting RDF graph is investigated. An implementation of the R2RML standard is used in order to express mappings that associate tuples from the source database to triples in the resulting RDF graph. Next, a methodology is proposed that enables incremental generation and storage of an RDF graph based on a source relational database, and it is evaluated through a set of performance measurements. Finally, a discussion is presented regarding the authors’ most important findings and conclusions.
This one-year research project, funded by NOAA Climate Program Office (CPO) Scientific Data Stewardship (SDS), provides a solution to migrate data to a single standards-based archive format. Specifically, we investigate on how to store NASA ECS data and metadata into HDF5 Archival Information Packages (AIP). To achieve this, the HDF4 to HDF5 conversion tool has been enhanced so that converted ECS data can be read through the NetCDF4/CDM interface. In addition, metadata tools will be developed that convert ECS collection and granule level metadata to NOAA's collection level and NARA's METS standard. The enhanced HDF4 to HDF5 conversion tool has been released in May 2008 and it includes new functionality as the converted ECS data can be read through the NetCDF4 interface. We have tested 33 typical HDF-EOS2 swath, grid and point products at the National Snow and Ice Data Center (NSIDC). We also demonstrate the initial effort of the work to develop METS compliant metadata from granule metadata held in NASA's Earth Observing System (EOS) Data and Information System (EOSDIS) Core System (ECS).
The tool takes HDF-EOS 5 data as input, and generates COARDS-compatible output - if the input file has enough metadata to be COARDS-compliant, the output file will be COARDS-compliant. The tool is written in portable C, and ought to run on any platform where the HDF-EOS and netCDF libraries are available.
This year, we have made two major enhancements to the converter:
It now automatically detects whether its input is HDF-EOS2 or HDF-EOS5 format, and handles either one. The previous tool worked with HDF-EOS5 only.
Its netCDF output attempts to conform to the new CF conventions (a superset of the COARDS conventions). This is primarily an improvement in its translation of Swath datasets, which CF handles much better than COARDS.
Overview Of the HDF5 Lite and High Level interfaces.
Source: http://hdfeos.org/workshops/ws07/presentations/McGrath3/McGrath_HDF5_High_Level_and_Lite_Libraries_Intro.ppt
Presented by Matthias Arnold at the Annual Conference of the Visual Resources Association, March 12-15, 2014 in Milwaukee, Wisconsin.
Session #8: VRA Core 4 Unbound: Expanding Core capabilities through embedded metadata, APIs, and editors
ORGANIZER: Trish Rose-Sandler, Center for Biodiversity Informatics, Missouri Botanical Garden
MODERATOR: Greg Reser, University of California, San Diego
PRESENTERS:
Matthias Arnold, University of Heidelberg
Greg Reser, University of California, San Diego
Trish Rose-Sandler, Center for Biodiversity Informatics, Missouri Botanical Garden
Since the publication of the VRA Core 4.0 (Core 4) data standard in 2007 many institutions have developed tools that extend its capabilities either to support a local need or to enable the interaction of Core 4 data with data encoded in other standards. The proliferation of these tools within the last few years illustrate how the Core 4 has moved from a US-based standard developed for a specific audience to having a much more international uptake and even adoption within communities not originally envisioned e.g. biodiversity.
The speakers will talk about tools they have developed that help demonstrate how Core 4 can be incorporated within embedded metadata standards; how it can be used in conjunction with scientific data standards; and how a Core 4 editor can easily convert, store, and exchange data in XML.
This slide is about hdf4 to hdf5 conversion mapping and tool.
Source: http://hdfeos.org/workshops/ws07/presentations/McGrath1/McGrath_HDF4_and_HDF5.ppt
Integrating Ontario’s Provincially Tracked Species Data Using FMESafe Software
The Natural Heritage Information Centre (NHIC) compiles, maintains and distributes information on natural species, plant communities and spaces of conservation concern in Ontario. This information is stored in a spatial database used for tracking this information. The data in these databases is received from internal and external partners and undergoes a vetting process which includes spatial corrections, generalization, etc. and is then posted to the Land Information Ontario (LIO) warehouse. Information from LIO is extracted to fulfill information requests to clients all over the province. The information in LIO (FGDB) and the NHIC data repositories (Biotics – ORACLE, Central Holdings – Access) must be reconciled on a regular basis.
The NHIC is undertaking a project to migrate its internal MS Access format repository to a newly developed Central Repository based on a geodatabase. As part of this development and migration process, new routines must be developed for data integration, data export and data reconciliation with other databases, as well as extraction for information requests. Additionally, NHIC business requires that data be subject to both automated QC checks and manual review by qualified biological staff. NHIC staff had begun the process of creating a central repository and some data management processes using FME, however, due to staffing constraints the Provincial Geomatics Service Centre (PGSC) was contracted to assist with the work.
This project led to the development of a centralized authoritative source for provincially tracked species information in Ontario. This represents over 700,000 records that span across 6 related tables. NHIC staff are now able to extract, create and edit data using FME workspaces to ensure a quality product to serve ministry clients, stakeholders and staff.
This slide will provide an overview of current functionality, techniques, and tips for visualization and query of HDF and netCDF data in ArcGIS, as well as future plans. Hierarchical Data Format (HDF) and netCDF (network Common Data Form) are two widely used data formats for storing and manipulating scientific data. The NetCDF format also supports temporal data by using multidimensional arrays. The basic structure of data in this format and how to work with it will be covered in the context of standardized data structures and conventions. This slide will demonstrate the tools and techniques for ingesting HDF and netCDF data efficiently in ArcGIS, as well as some common workflows to employ the visualization capabilities of ArcGIS for effective animation and analysis of your data.
Incremental Export of Relational Database Contents into RDF GraphsNikolaos Konstantinou
In addition to tools offering RDF views over databases, a variety of tools exist that allow exporting database contents into RDF graphs; tools proven that in many cases demonstrate better performance than the former. However, in cases when database contents are exported into RDF, it is not always optimal or even necessary to dump the whole database contents every time. In this paper, the problem of incremental generation and storage of the resulting RDF graph is investigated. An implementation of the R2RML standard is used in order to express mappings that associate tuples from the source database to triples in the resulting RDF graph. Next, a methodology is proposed that enables incremental generation and storage of an RDF graph based on a source relational database, and it is evaluated through a set of performance measurements. Finally, a discussion is presented regarding the authors’ most important findings and conclusions.
This one-year research project, funded by NOAA Climate Program Office (CPO) Scientific Data Stewardship (SDS), provides a solution to migrate data to a single standards-based archive format. Specifically, we investigate on how to store NASA ECS data and metadata into HDF5 Archival Information Packages (AIP). To achieve this, the HDF4 to HDF5 conversion tool has been enhanced so that converted ECS data can be read through the NetCDF4/CDM interface. In addition, metadata tools will be developed that convert ECS collection and granule level metadata to NOAA's collection level and NARA's METS standard. The enhanced HDF4 to HDF5 conversion tool has been released in May 2008 and it includes new functionality as the converted ECS data can be read through the NetCDF4 interface. We have tested 33 typical HDF-EOS2 swath, grid and point products at the National Snow and Ice Data Center (NSIDC). We also demonstrate the initial effort of the work to develop METS compliant metadata from granule metadata held in NASA's Earth Observing System (EOS) Data and Information System (EOSDIS) Core System (ECS).
The tool takes HDF-EOS 5 data as input, and generates COARDS-compatible output - if the input file has enough metadata to be COARDS-compliant, the output file will be COARDS-compliant. The tool is written in portable C, and ought to run on any platform where the HDF-EOS and netCDF libraries are available.
This year, we have made two major enhancements to the converter:
It now automatically detects whether its input is HDF-EOS2 or HDF-EOS5 format, and handles either one. The previous tool worked with HDF-EOS5 only.
Its netCDF output attempts to conform to the new CF conventions (a superset of the COARDS conventions). This is primarily an improvement in its translation of Swath datasets, which CF handles much better than COARDS.
Overview Of the HDF5 Lite and High Level interfaces.
Source: http://hdfeos.org/workshops/ws07/presentations/McGrath3/McGrath_HDF5_High_Level_and_Lite_Libraries_Intro.ppt
Presented by Matthias Arnold at the Annual Conference of the Visual Resources Association, March 12-15, 2014 in Milwaukee, Wisconsin.
Session #8: VRA Core 4 Unbound: Expanding Core capabilities through embedded metadata, APIs, and editors
ORGANIZER: Trish Rose-Sandler, Center for Biodiversity Informatics, Missouri Botanical Garden
MODERATOR: Greg Reser, University of California, San Diego
PRESENTERS:
Matthias Arnold, University of Heidelberg
Greg Reser, University of California, San Diego
Trish Rose-Sandler, Center for Biodiversity Informatics, Missouri Botanical Garden
Since the publication of the VRA Core 4.0 (Core 4) data standard in 2007 many institutions have developed tools that extend its capabilities either to support a local need or to enable the interaction of Core 4 data with data encoded in other standards. The proliferation of these tools within the last few years illustrate how the Core 4 has moved from a US-based standard developed for a specific audience to having a much more international uptake and even adoption within communities not originally envisioned e.g. biodiversity.
The speakers will talk about tools they have developed that help demonstrate how Core 4 can be incorporated within embedded metadata standards; how it can be used in conjunction with scientific data standards; and how a Core 4 editor can easily convert, store, and exchange data in XML.
This slide is about hdf4 to hdf5 conversion mapping and tool.
Source: http://hdfeos.org/workshops/ws07/presentations/McGrath1/McGrath_HDF4_and_HDF5.ppt
Integrating Ontario’s Provincially Tracked Species Data Using FMESafe Software
The Natural Heritage Information Centre (NHIC) compiles, maintains and distributes information on natural species, plant communities and spaces of conservation concern in Ontario. This information is stored in a spatial database used for tracking this information. The data in these databases is received from internal and external partners and undergoes a vetting process which includes spatial corrections, generalization, etc. and is then posted to the Land Information Ontario (LIO) warehouse. Information from LIO is extracted to fulfill information requests to clients all over the province. The information in LIO (FGDB) and the NHIC data repositories (Biotics – ORACLE, Central Holdings – Access) must be reconciled on a regular basis.
The NHIC is undertaking a project to migrate its internal MS Access format repository to a newly developed Central Repository based on a geodatabase. As part of this development and migration process, new routines must be developed for data integration, data export and data reconciliation with other databases, as well as extraction for information requests. Additionally, NHIC business requires that data be subject to both automated QC checks and manual review by qualified biological staff. NHIC staff had begun the process of creating a central repository and some data management processes using FME, however, due to staffing constraints the Provincial Geomatics Service Centre (PGSC) was contracted to assist with the work.
This project led to the development of a centralized authoritative source for provincially tracked species information in Ontario. This represents over 700,000 records that span across 6 related tables. NHIC staff are now able to extract, create and edit data using FME workspaces to ensure a quality product to serve ministry clients, stakeholders and staff.
Introduction to Data Analysis with R and the R programming language. More information can be found at https://www.spiraltrain.nl/course-data-analysis-with-r/?lang=en
Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)
This is an open source programming language as well as the software atmosphere, used for the statical computers and the graphics, which supported by the R foundation. The environment of the R software was earlier written in the ‘C’, Fortain as well as the R, which is now easily available in the GNU
Scenario of E library in the 21st century by
Dr. Gururaj S. Hadagali
Assistant Professor
Dept. of Library and Information Science
Karnatak University, Dharwad
.Introduction
.Pre -Requisites to form a contract
.What contract means ?
.Who are competent to contract
.Free consent
.Classification of contracts
.Conclusion
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
3. History of R programming
• R is a programming language and free software
environment for statistical computing and
graphics.
• R was created by Ross Ihaka and Robert
Gentleman at the University of Auckland, New
Zealand, and further developed by the R
Development Core Team.
• R is named after the first names of the first two R
authors.
• The project was conceived in 1992, with an initial
version released in 1995 and a stable beta version
(v1.0) on 29 February 2000
4. Programming features
• R is an interpreted language; users typically
access it through a command-line interpreter
• R's data structures include vectors, matrices,
arrays, data frames and lists.
• R supports procedural programming with
functions and for some functions, object-oriented
programming with generic functions.
• Although used mainly by statisticians requiring an
environment for statistical computation and
software development, R can also operate as a
general calculation toolbox – with performance
benchmarks comparable to MATLAB.
5. Statistical features
• R and its libraries implement a wide variety of
statistical and graphical techniques, including linear
and nonlinear modeling, classical statistical tests, time-
series analysis, classification, clustering, and others.
• R is easily extensible through functions and extensions,
and the R community is noted for its active
contributions in terms of packages.
• Many of R's standard functions are written in R itself,
which makes it easy for users to follow the algorithmic
choices made.
• Another strength of R is static graphics, which can
produce publication-quality graphs, including
mathematical symbols. Dynamic and interactive
graphics are available through additional packages.
6. Packages
• The capabilities of R are extended through user-created
packages, which allow specialized statistical
techniques, graphical devices, import/export
capabilities, reporting tools.
• The R packaging system is also used by researchers to
create and organize research data, code and report
files in a systematic way for sharing and public
archiving.
• A core set of packages is included with the installation
of R, with more than 15,000 additional packages
available at the Comprehensive R Archive Network
(CRAN), Bioconductor, Omegahat, GitHub, and other
repositories.
7. C R A N
Comprehensive R Archive Network
• CRAN is a network of ftp and web servers
around the world that store identical, up-to-
date, versions of code and documentation
for R.
• Please use the CRAN mirror nearest to you to
minimize network load.
CRAN mirror?
8.
9. ANACONDA
• Anaconda is the birthplace of Python data
science.
• Anaconda is a free and open-source distribution
of the Python and R programming languages for
scientific computing.
• This aims to simplify package management and
deployment.
• The distribution includes data-science packages
suitable for Windows, Linux, and macOS.
10. Why should adopt R?
• R can be integrated with other programming
languages like C, C++, Python, etc.
• R has more than 10,000 packages in its
repository.
• R has community support of developers world-
wide.
• Easy interface for data treatment &
visualization.
11. Companies using
‘R’eal time
• Google:
– calculate ROI on advertising campaigns
– Economic forecasting
– Big-data statistical modeling
• Facebook:
– User behavior analysis related to status update
and profile pictures.
– Exploratory data analysis, Big-data visualization.
12. Companies using ‘R’eal time
• Twitter:
– Use for semantic clustering & data visualization
– Anomaly & breakout detection for improving their customer
experience.
• John Deere:
– Use to forecasting crop yields.
– Optimizing the build order on production line.
• ANZ Bank:
– Use for Credit Risk analysis.
– Fit models for mortgage loss.
