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Find out about: Cross Industry Standard Process for Data Mining
Knowledge Management Presentation.
MBA semester 2
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
Introduction
Domain Expert
Goal identification and Data Understanding
Data Cleaning
Missing values
Noisy Data
Inconsistent Data
Data Integration
Data Transformation
Data Reduction
Feature Selection
Sampling Discretization
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
Introduction
Domain Expert
Goal identification and Data Understanding
Data Cleaning
Missing values
Noisy Data
Inconsistent Data
Data Integration
Data Transformation
Data Reduction
Feature Selection
Sampling Discretization
This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
Aron Web solution is one of the leading IT company in India, Mohali. We provide various IT services. We also provide industrial training at fair cost. The main programming languages are PHP, HTML and JS. JQuery is also applied in the projects. We use various CMS as Drupal, Wordpress,Joomla, Magento for projects depends on requirements or preferences of the customer. We also provide SEO and Business Catalyst.
This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
Aron Web solution is one of the leading IT company in India, Mohali. We provide various IT services. We also provide industrial training at fair cost. The main programming languages are PHP, HTML and JS. JQuery is also applied in the projects. We use various CMS as Drupal, Wordpress,Joomla, Magento for projects depends on requirements or preferences of the customer. We also provide SEO and Business Catalyst.
Fashion central international june issue 2016Fashioncentral
It Covering All major Fashion activities in the world.All International fashion Shows and Cat walks.Fashion mag also covering Celebrity Gossips,beauty tips,Lifestyle,Travel and Men's corner.
Cursos de Formación Continuada Acreditados CFC.
Dirigidos a médicos, enfermeros, técnicos superiores y técnicos en cuidados auxiliares de enfermería.
Cursos transversales: Cursos de Calidad, Seguridad de Pacientes y Gestión Clínica.
Cursos especializados.
Formación eLearning, Presencial y mixta.
Todos estos cursos están acreditados por la Comisión de Formación Continuada de las Profesiones Sanitarias y son baremables en oposiciones, concursos y carrera profesional.
The content of the document, "Implementing Data Mesh: Six Ways That Can Improve the Odds of Your Success," is a whitepaper authored by Ranganath Ramakrishna from LTIMindtree. The whitepaper introduces the concept of Data Mesh, a socio-technical paradigm that aims to help organizations fully leverage the value of their analytical data.
Data pipelines are the heart and soul of data science. Are you a beginner looking to understand data pipelines? A glimpse into what they are and how they work.
get more idea about data science & how its works
http://techwaala.in/whats-data-science/about
#data
#datascience
#dataengneering
#machinelearning
#datascience
#bigdata
#dataanalyst
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
A simulated decision trees algorithm (sdt)Mona Nasr
The customer's information contained in
databases has increased dramatically in the last few years.
Data mining is a good approach to deal with this volume of
information to enhance the process of customer services.
One of the most important and powerful techniques of data
mining is decision trees algorithm. It appropriate for large
and sophisticated business area but it's complicated, high
cost and not easy to use by not specialists in the field. To
overcome this problem SDT is proposed which is a simple,
powerful and low-cost proposed methodology to simulate the
decision trees algorithm for different business scopes and
nature. SDT methodology consists of three phases. The first
phase is the data preparation which prepare data for
computing calculations, the second phase is SDT algorithm
which represents a simulation of decision trees algorithm to
find the most important rules that distinguish specific type of
customers, the third phase is to visualize results and rules for
better understanding and clarifying the results. In this paper
SDT methodology is tested by a dataset consists of 1000
instants for German Credit Data belongs to one of German
bank customers. SDT selects the most important rules and
paths that reaches the selected ratio and tested cluster of
customers successfully with interesting remarks and finding.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
2. Company: Teradata (Pune)
Address: Tower 12, Level 5, Cyber City, Magarpatta Inner Circle,
Magarpatta City, Hadapsar, Pune, Maharashtra 411028
Teradata Corporation (NYSE: TDC) is the world's largest company focused
on raising intelligence through data warehousing and enterprise analytics.
It is the global leader in data warehousing and enterprise analytics.
Teradata Professional Services enables Teradata customers to use their
enterprise data warehouse for decision making and to support business
operations providing active enterprise intelligence to frontline workers
throughout the enterprise.
3. Cross Industry Standard Process for Data
Mining (CRISP-DM)
A data mining process model that describes commonly used approaches
that data mining experts use to tackle problems.
Was the leading methodology used by industry data miners
Was called the "de facto standard for developing data mining and
knowledge discovery projects.” by miners in a recent survey
CRISP-DM breaks the process of data mining into six major phases
4. Phrase One: Business Understanding
This initial phase focuses on understanding the project objectives and requirements from a
business perspective, and then converting this knowledge into a data mining problem
definition, and a preliminary plan designed to achieve the objectives.
Phrase Two: Data Understanding
The data understanding phase starts with an initial data collection and proceeds with
activities in order to get familiar with the data, to identify data quality problems, to discover
first insights into the data, or to detect interesting subsets to form hypotheses for hidden
information.
Phrase Three: Data Preparation
The data preparation phase covers all activities to construct the final dataset (data that will
be fed into the modeling tool(s)) from the initial raw data.
5. Phrase Four: Modeling
In this phase, various modeling techniques are selected and applied, and their parameters
are calibrated to optimal values. Typically, there are several techniques for the same data
mining problem type.
Phrase Five: Evaluation
At this stage in the project, a model (or models) is built that appears to have high quality,
from a data analysis perspective.
Phrase Six: Deployment
The deployment phase can be as simple as generating a report or as complex as
implementing a repeatable data scoring (e.g. segment allocation) or data mining process
6. Teradata has adopted the Data Mining
procedure and is using this to it’s full capacity.
The company has been using this technique
since a very long time and through this, it gives
it’s customers the best possible results/reports.