Learn how to manipulate data frames using the dplyr package by Hadley Wickham. This session will cover select, filter, summarize, tally, group_by, and mutate. Based on the data carpentry ecology lessons
Analysing biomedical data (ers october 2017)Paul Agapow
Presented at European Respiratory Society, Berlin, October 2017. High level talk to mix of clinicians and scientists on the difficulties of biomedical analysis, including practical, statistical and data issues.
Learn how to manipulate data frames using the dplyr package by Hadley Wickham. This session will cover select, filter, summarize, tally, group_by, and mutate. Based on the data carpentry ecology lessons
Analysing biomedical data (ers october 2017)Paul Agapow
Presented at European Respiratory Society, Berlin, October 2017. High level talk to mix of clinicians and scientists on the difficulties of biomedical analysis, including practical, statistical and data issues.
What are Data structures in Python? | List, Dictionary, Tuple Explained | Edu...Edureka!
YouTube Link: https://youtu.be/m9n2f9lhtrw
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on 'Data Structures in Python' will help you understand the various data structures that Python has built into itself such as the list, dictionary, tuple and more. Further, we will also understand stacks, queues, trees and how they are implemented in Python using classes and functions. The video is divided into the following parts:
What are Data Structures?
Why are Data Structures needed?
Types of Data Structures in Python
Built-In Data Structures
Lists
Dictionary
Tuple
Sets
User-Defined Data Structure
Array
Stack
Queue
Linked List
Tree
Graph
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Clinical modelling with openEHR ArchetypesKoray Atalag
This is the prezo I used in CellML workshop in Waiheke Island, Auckland, New Zealand on 14 April 2015. The aim was to introduce information modelling with openEHR and how to achieve semantic interoperability by using shared ontologies and clinical terminology.
*RDBMS ( Relational Database Management System)
*Network model
*Hierarchical Data Model
*Object-Oriented Model
*Attribute Types
*Relation Instance
*Relations are Unordered
*Database
*E-R Diagram for the Banking Enterprise
*Determining Keys from E-R Sets
Linkages to EHRs and Related Standards. What can we learn from the Parallel U...Koray Atalag
This is the prezo I used during the CellML workshop in Waiheke Island, Auckland, New Zealand on 13 April 2015. The aim was to introduce information modelling methods and tools for the purpose of inspiring computational modelling work in the area of semantics and interoperability.
Pam Luecke presents "Assignments that Build Skills" during the annual 2012 Reynolds Business Journalism Seminars, hosted by the Donald W. Reynolds National Center for Business Journalism.
For more information about free training for business journalists, please visit businessjournalism.org.
How to process the quantitative data collected through field survey with structured interview schedule with the help of MsS=s? This paper answers this question.
Archetype-based data transformation with LinkEHRDavid Moner Cano
How can we convert data to standard data (EN ISO 13606, openEHR, HL7 CDA...) using archetypes? LinkEHR is a tool that helps in achieving this objective.
This presentation was made at the "Arctic Conference on Dual-Model based Clinical Decision Support and Knowledge Management", that took place the 27th and 28th of May, 2014 in Tromsø, Norway.
Presentations focused on materials and documentation that should be saved in order to prepare data file from a survey for secondary use. Some hints were given on how to label items, code missing values, organize folder structure etc. Additionally to clean dataset, documentation on data level, following internationally accepted DDI specification, could be prepared using Colectica for Excel or Nesstar Publisher.
Event was one of Foster Cessda training events for doctoral students.
Related link: https://www.fosteropenscience.eu/project/index.php?option=com_content&view=category&layout=blog&id=23&Itemid=104
Abstract: https://www.fosteropenscience.eu/event/research-data-management-and-open-data-0
In this you know about
Types of Data Structures / Data structures types in C++
1.Primitive and non-primitive data structure
2.Linear and non-linear data structure
3.Static and dynamic data structure
4.Persistent and ephemeral data structure
5.Sequential and direct access data structure
What are Data structures in Python? | List, Dictionary, Tuple Explained | Edu...Edureka!
YouTube Link: https://youtu.be/m9n2f9lhtrw
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on 'Data Structures in Python' will help you understand the various data structures that Python has built into itself such as the list, dictionary, tuple and more. Further, we will also understand stacks, queues, trees and how they are implemented in Python using classes and functions. The video is divided into the following parts:
What are Data Structures?
Why are Data Structures needed?
Types of Data Structures in Python
Built-In Data Structures
Lists
Dictionary
Tuple
Sets
User-Defined Data Structure
Array
Stack
Queue
Linked List
Tree
Graph
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Clinical modelling with openEHR ArchetypesKoray Atalag
This is the prezo I used in CellML workshop in Waiheke Island, Auckland, New Zealand on 14 April 2015. The aim was to introduce information modelling with openEHR and how to achieve semantic interoperability by using shared ontologies and clinical terminology.
*RDBMS ( Relational Database Management System)
*Network model
*Hierarchical Data Model
*Object-Oriented Model
*Attribute Types
*Relation Instance
*Relations are Unordered
*Database
*E-R Diagram for the Banking Enterprise
*Determining Keys from E-R Sets
Linkages to EHRs and Related Standards. What can we learn from the Parallel U...Koray Atalag
This is the prezo I used during the CellML workshop in Waiheke Island, Auckland, New Zealand on 13 April 2015. The aim was to introduce information modelling methods and tools for the purpose of inspiring computational modelling work in the area of semantics and interoperability.
Pam Luecke presents "Assignments that Build Skills" during the annual 2012 Reynolds Business Journalism Seminars, hosted by the Donald W. Reynolds National Center for Business Journalism.
For more information about free training for business journalists, please visit businessjournalism.org.
How to process the quantitative data collected through field survey with structured interview schedule with the help of MsS=s? This paper answers this question.
Archetype-based data transformation with LinkEHRDavid Moner Cano
How can we convert data to standard data (EN ISO 13606, openEHR, HL7 CDA...) using archetypes? LinkEHR is a tool that helps in achieving this objective.
This presentation was made at the "Arctic Conference on Dual-Model based Clinical Decision Support and Knowledge Management", that took place the 27th and 28th of May, 2014 in Tromsø, Norway.
Presentations focused on materials and documentation that should be saved in order to prepare data file from a survey for secondary use. Some hints were given on how to label items, code missing values, organize folder structure etc. Additionally to clean dataset, documentation on data level, following internationally accepted DDI specification, could be prepared using Colectica for Excel or Nesstar Publisher.
Event was one of Foster Cessda training events for doctoral students.
Related link: https://www.fosteropenscience.eu/project/index.php?option=com_content&view=category&layout=blog&id=23&Itemid=104
Abstract: https://www.fosteropenscience.eu/event/research-data-management-and-open-data-0
In this you know about
Types of Data Structures / Data structures types in C++
1.Primitive and non-primitive data structure
2.Linear and non-linear data structure
3.Static and dynamic data structure
4.Persistent and ephemeral data structure
5.Sequential and direct access data structure
SBI Magnum Balanced Fund: An Open-ended Balanced Scheme - Sep 16SBI Mutual Fund
SBI Magnum Balanced Fund invests in a mix of equity and debt investments. It provides a good investment opportunity to investors who do not wish to be completely exposed to equity markets, but are looking for relatively higher returns than those provided by debt funds. The scheme invests in a diversified portfolio of equities of high growth companies and balances the risk through investing the rest in a relatively safe portfolio of debt.To know more about this mutual fund check SBI Mutual Fund page
https://www.sbimf.com/Products/HybridSchemes/Magnum_Balanced_Fund.aspx
SBI Dynamic Asset Allocation Fund: An Open-ended Dynamic Asset Allocation Sch...SBI Mutual Fund
SBI Dynamic Asset Allocation Fund is an open-ended dynamic asset allocation scheme which aims to provide investors an opportunity to invest in a portfolio of a mix of equity and equity-related securities and fixed-income instruments which will be managed dynamically so as to provide investors with long-term capital appreciation.To know more about this mutual fund check SBI Mutual Fund page
https://www.sbimf.com/Products/HybridSchemes.aspx
SBI Dynamic Asset Allocation Fund: An Open-ended Dynamic Asset Allocation Sch...SBI Mutual Fund
SBI Dynamic Asset Allocation Fund is an open-ended dynamic asset allocation scheme which aims to provide investors an opportunity to invest in a portfolio of a mix of equity and equity-related securities and fixed-income instruments which will be managed dynamically so as to provide investors with long-term capital appreciation.To know more about this mutual fund check SBI Mutual Fund page
https://www.sbimf.com/Products/HybridSchemes.aspx
Ask these basic questions: How much capital does your growth require? What will be your cost of capital over the growth of your venture? Can you afford to grow at all costs? When will growth not be the basis for valuation? What if factors beyond your control impact growth?
Beyond EXPLAIN: Query Optimization From Theory To CodeYuto Hayamizu
EXPLAIN is too much explained. Let's go "beyond EXPLAIN".
This talk will take you to an optimizer backstage tour: from theoretical background of state-of-the-art query optimization to close look at current implementation of PostgreSQL.
ChatGPT
Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It involves applying various techniques and methods to extract insights from data sets, often with the goal of uncovering patterns, trends, relationships, or making predictions.
Here's an overview of the key steps and techniques involved in data analysis:
Data Collection: The first step in data analysis is gathering relevant data from various sources. This can include structured data from databases, spreadsheets, or surveys, as well as unstructured data such as text documents, social media posts, or sensor readings.
Data Cleaning and Preprocessing: Once the data is collected, it often needs to be cleaned and preprocessed to ensure its quality and suitability for analysis. This involves handling missing values, removing duplicates, addressing inconsistencies, and transforming data into a suitable format for analysis.
Exploratory Data Analysis (EDA): EDA involves examining and understanding the data through summary statistics, visualizations, and statistical techniques. It helps identify patterns, distributions, outliers, and potential relationships between variables. EDA also helps in formulating hypotheses and guiding further analysis.
Data Modeling and Statistical Analysis: In this step, various statistical techniques and models are applied to the data to gain deeper insights. This can include descriptive statistics, inferential statistics, hypothesis testing, regression analysis, time series analysis, clustering, classification, and more. The choice of techniques depends on the nature of the data and the research questions being addressed.
Data Visualization: Data visualization plays a crucial role in data analysis. It involves creating meaningful and visually appealing representations of data through charts, graphs, plots, and interactive dashboards. Visualizations help in communicating insights effectively and spotting trends or patterns that may be difficult to identify in raw data.
Interpretation and Conclusion: Once the analysis is performed, the findings need to be interpreted in the context of the problem or research objectives. Conclusions are drawn based on the results, and recommendations or insights are provided to stakeholders or decision-makers.
Reporting and Communication: The final step is to present the results and findings of the data analysis in a clear and concise manner. This can be in the form of reports, presentations, or interactive visualizations. Effective communication of the analysis results is crucial for stakeholders to understand and make informed decisions based on the insights gained.
Data analysis is widely used in various fields, including business, finance, marketing, healthcare, social sciences, and more. It plays a crucial role in extracting value from data, supporting evidence-based decision-making, and driving actionable insig
This presentation is intended to give the viewer a working knowledge of the practical applications of SAS in terms of Banking Analytics. Specifically, Enterprise Guide and Enterprise Miner have been discussed in detail.
A Scalable Approach for Efficiently Generating Structured Dataset Topic ProfilesBesnik Fetahu
The increasing adoption of Linked Data principles has led
to an abundance of datasets on the Web. However, take-up and reuse is hindered by the lack of descriptive information about the nature of the data, such as their topic coverage, dynamics or evolution. To address this issue, we propose an approach for creating linked dataset profiles. A profile consists of structured dataset metadata describing topics and their relevance. Profiles are generated through the configuration of techniques for resource sampling from datasets, topic extraction from reference datasets and their ranking based on graphical models. To enable a good trade-off between scalability and accuracy of generated profiles, appropriate parameters are determined experimentally. Our evaluation considers topic profiles for all accessible datasets from the Linked Open Data cloud. The results show that our approach generates accurate profiles even with comparably small sample sizes (10%) and outperforms established topic modelling approaches.
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...Rodney Joyce
Number 2 in the Data Science for Dummies series - We'll predict Titanic survival with Databricks, python and MLSpark.
These are the slides only (excuse the Powerpoint animation issues) - check out the actual tech talk on YouTube: https://rodneyjoyce.home.blog/2019/05/03/data-science-for-dummies-machine-learning-with-databricks-python-sparkml-tech-talk-1-of-7/)
If you have not used Databricks before check out the first talk - Databricks for Dummies.
Here's the rest of the series: https://rodneyjoyce.home.blog/tag/data-science-for-dummies/
1) Data Science overview with Databricks
2) Titanic survival prediction with Azure Machine Learning Studio + Kaggle
3) Data Engineering with Titanic dataset + Databricks + Python
4) Titanic with Databricks + Spark ML
5) Titanic with Databricks + Azure Machine Learning Service
6) Titanic with Databricks + MLS + AutoML
7) Titanic with Databricks + MLFlow
8) Titanic with .NET Core + ML.NET
9) Deployment, DevOps/MLOps and Productionisation
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
3. Introduction
• Web data extraction has been an important part for many
web data analysis applications.
• Many web sites contain large sets of pages generated using
a common template or layout.
– EX ： Amazon 、 Ebay 、 Google, etc.
• The key to automatic extraction for these template web pages
depend on whether we can deduce the template automatically.
– There is no need to annotate the web pages for extraction targets.
4. Introduction (Cont.)
• According to the kind of extraction targets, the web data
extraction tasks can be classified into three categories ：
– Record-level ： the target is usually constrained to record-wide
information
• DEPTA
• IEPAD
– Page-level ： the target aims at page-wide information.
• RoadRunner
• EXALG
• FivaTech
– Site-level ： populate database from pages of a Web site.
5. Introduction (Cont.)
• We take FivaTech System as our research, and study it’s
problem to improve the performance.
– It is unsupervised.
– It is both page-level and record-level.
– It has much higher precision than EXALG.
– It is comparable with other record-level extraction systems
like ViPER and MSE.
7. • Assume the similarity between b1 and b2 is 1.0 ， and the
similarity between tr1~tr4 and tr5~tr6 is 0.6
• The FivaMatchingScore is (1.0+0.6+0.6+0.6+0.6)/5 = 0.68
8. The problem of FivaMatchingScore
• Case 1. Table structure.
• Case 2. Child trees containing set type data.
• Case 3. Asymmetry.
11. Case 2. Child trees containing set type
data
• Assume tr5 and tr6 containing set type data, and the similarity
between tr1~tr4 and tr5~tr6 is 0.3.
• The FivaMatchingScore is 1.0/5 = 0.2.
