The document discusses the importance of data quality, proper use of statistics, and correct interpretation of results in statistical analysis. It provides a 3 step approach: 1) Ensuring high quality data by addressing issues like missing values and outliers. 2) Appropriate use of statistical techniques after defining the variables and objectives clearly. Considering issues like correlation, normality, and model assumptions. 3) Careful interpretation of results while preserving the multidimensional nature of phenomena and considering partial correlations between variables. It emphasizes the need for collaboration between data miners, statisticians and domain experts for successful knowledge discovery.
Multiple regression analysis , its methods among which multiple regression analysis one of the popular method. also discuss the applications and purposes
Multiple regression analysis , its methods among which multiple regression analysis one of the popular method. also discuss the applications and purposes
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Data Preparation with the help of Analytics MethodologyRupak Roy
Get involved with the steps of data preparation and data assessment using widely used methodologies for machine learning data science modeling.
Let me know if anything is required, ping me at google #bobrupakroy
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
Types of analytics & the structures of dataRupak Roy
Get to know more about Prescriptive, Predictive analytics like market basket analytics plus the data structure and variables to apply the analytics.
for more info you can ping me at google #bobrupakroy
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Data Preparation with the help of Analytics MethodologyRupak Roy
Get involved with the steps of data preparation and data assessment using widely used methodologies for machine learning data science modeling.
Let me know if anything is required, ping me at google #bobrupakroy
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
Types of analytics & the structures of dataRupak Roy
Get to know more about Prescriptive, Predictive analytics like market basket analytics plus the data structure and variables to apply the analytics.
for more info you can ping me at google #bobrupakroy
Machine Learning Approaches and its Challengesijcnes
Real world data sets considerably is not in a proper manner. They may lead to have incomplete or missing values. Identifying a missed attributes is a challenging task. To impute the missing data, data preprocessing has to be done. Data preprocessing is a data mining process to cleanse the data. Handling missing data is a crucial part in any data mining techniques. Major industries and many real time applications hardly worried about their data. Because loss of data leads the company growth goes down. For example, health care industry has many datas about the patient details. To diagnose the particular patient we need an exact data. If these exist missing attribute values means it is very difficult to retain the datas. Considering the drawback of missing values in the data mining process, many techniques and algorithms were implemented and many of them not so efficient. This paper tends to elaborate the various techniques and machine learning approaches in handling missing attribute values and made a comparative analysis to identify the efficient method.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
Data science is likely to become even more important as the volume and complexity of data continues to increase. With advancements in machine learning and artificial intelligence, data scientists will have access to more sophisticated tools and algorithms to analyze and extract insights from data. Data science will continue to play a crucial role in fields such as healthcare, finance, and technology, helping organizations make better decisions and drive innovation. Additionally, there will be a greater emphasis on data privacy and ethical considerations as the use of data becomes more prevalent.
#Data science is a field that involves using statistical and computational methods to analyze and extract insights from data. It plays a crucial role in various industries, from business and healthcare to finance and technology.
This brief work is aimed in the direction of basics of data sciences and model building with focus on implementation on fairly sizable dataset. It focuses on cleaning the data, visualization, EDA, feature scaling, feature normalization, k-nearest neighbor, logistic regression, random forests, cross validation without delving too deep into any of them but giving a start to a new learner.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Data Science - Part III - EDA & Model SelectionDerek Kane
This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
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!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
2. Our work Someone said tortura i dati abbastanza a lungo e confesseranno qualsiasi cosa ( Anonimo )
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19. Conclusions Knowledge discovery rests on the three balanced legs of computer science, statistics and client knowledge. It will not stand either on one leg or on two legs, or even on three unbalanced legs. Successful knowledge discovery needs a substantial to collaboration from all three. DATA MINING = STATISTICS + CLIENT DOMAIN
20. Conclusions There is the opportunity for an immensely rewarding synergy between data miners and statisticians. However, most data miners tend to be ignorant of statistics and client's domain; statisticians tend to be ignorant of data mining and client's domain; and clients tend to be ignorant of data mining and statistics.
