Goal: Provide an overview of data mining
Define data mining
Data mining vs. databases
Basic data mining tasks
Data mining development
Data mining issues
- The document provides an introduction to the concept of data mining, defining it as the process of fitting data to models to uncover hidden patterns.
- It discusses the differences between data mining and traditional database querying, and outlines some basic data mining tasks like classification, clustering, and association rule mining.
- The document also briefly touches on issues in data mining like privacy and changing data, as well as metrics used to evaluate data mining models and tasks.
Introduction to Data Mining - A Beginner's Guidegokulprasath06
We live in a world where vast amounts of data are collected daily. Analyzing such data is an important need. Data mining can meet this demand by providing tools to discover knowledge from data.
Checkout: http://bit.ly/2Mub6xP
Any Queries, Call us@ +91 9884412301 / 9600112302
This document provides an overview and introduction to the course CIS 674 Introduction to Data Mining. It defines data mining, outlines basic data mining tasks such as classification, clustering, and association rule mining. It also discusses the relationship between data mining and knowledge discovery in databases (KDD), and highlights some issues in data mining such as handling large datasets, high dimensionality, and interpretation of results.
This document provides an overview and introduction to the course CIS 674 Introduction to Data Mining. It defines data mining, outlines the basic data mining tasks of classification, regression, clustering, summarization and link analysis. It discusses the relationship between data mining and knowledge discovery in databases (KDD) and describes some common issues in data mining such as handling large datasets, high dimensionality, interpretation and visualization of results.
Data Patterns - A Native Open Source Data Profiling Tool for HPCC SystemsHPCC Systems
This document discusses using predictive analytics and HPCC Systems to make IoT data actionable for insurance companies. It begins by outlining the growth of IoT devices and some of the big questions they pose for insurers. The document then provides examples of how smart thermostat and water leak detection data could help with occupancy monitoring, prevention and claims. It also discusses how water leak claims have increased in Florida due to assignment of benefits to third parties. The document concludes by discussing how insurers can start unlocking insights from IoT data through technology, analytics and pilot programs that leverage HPCC Systems' pull architecture to integrate diverse data sources for predictive modeling.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
- The document provides an introduction to the concept of data mining, defining it as the process of fitting data to models to uncover hidden patterns.
- It discusses the differences between data mining and traditional database querying, and outlines some basic data mining tasks like classification, clustering, and association rule mining.
- The document also briefly touches on issues in data mining like privacy and changing data, as well as metrics used to evaluate data mining models and tasks.
Introduction to Data Mining - A Beginner's Guidegokulprasath06
We live in a world where vast amounts of data are collected daily. Analyzing such data is an important need. Data mining can meet this demand by providing tools to discover knowledge from data.
Checkout: http://bit.ly/2Mub6xP
Any Queries, Call us@ +91 9884412301 / 9600112302
This document provides an overview and introduction to the course CIS 674 Introduction to Data Mining. It defines data mining, outlines basic data mining tasks such as classification, clustering, and association rule mining. It also discusses the relationship between data mining and knowledge discovery in databases (KDD), and highlights some issues in data mining such as handling large datasets, high dimensionality, and interpretation of results.
This document provides an overview and introduction to the course CIS 674 Introduction to Data Mining. It defines data mining, outlines the basic data mining tasks of classification, regression, clustering, summarization and link analysis. It discusses the relationship between data mining and knowledge discovery in databases (KDD) and describes some common issues in data mining such as handling large datasets, high dimensionality, interpretation and visualization of results.
Data Patterns - A Native Open Source Data Profiling Tool for HPCC SystemsHPCC Systems
This document discusses using predictive analytics and HPCC Systems to make IoT data actionable for insurance companies. It begins by outlining the growth of IoT devices and some of the big questions they pose for insurers. The document then provides examples of how smart thermostat and water leak detection data could help with occupancy monitoring, prevention and claims. It also discusses how water leak claims have increased in Florida due to assignment of benefits to third parties. The document concludes by discussing how insurers can start unlocking insights from IoT data through technology, analytics and pilot programs that leverage HPCC Systems' pull architecture to integrate diverse data sources for predictive modeling.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsJason Riedy
Graph-structured data in network security, social networks, finance, and other applications not only are massive but also under continual evolution. The changes often are scattered across the graph, permitting novel parallel and incremental analysis algorithms. We discuss analysis algorithms for streaming graph data to maintain both local and global metrics with low latency and high efficiency.
Graph Analysis Trends and Opportunities -- CMG Performance and Capacity 2014Jason Riedy
High-performance graph analysis is unlocking knowledge in problems like anomaly detection in computer security, community structure in social networks, and many other data integration areas. While graphs provide a convenient abstraction, real-world problems' sparsity and lack of locality challenge current systems. This talk will cover current trends ranging from massive scales to low-power, low-latency systems and summarize opportunities and directions for graphs and computing systems.
The document provides an introduction to data analytics using R given by Wei Zhong from NUS. It begins with an overview of Wei Zhong and his background in computational biology. The agenda is then outlined, covering key concepts in data analytics like logistic regression, decision trees, random forests, and evaluation metrics. An overview of data analytics is presented, distinguishing descriptive, predictive, and prescriptive analytics. Statistical learning techniques like linear models, tree-based models, and clustering are introduced. Key concepts like cross-validation that will be used in the hands-on session are then defined.
Data mining involves analyzing large datasets to extract hidden patterns and predictive information. It is used to discover useful information from large data repositories like data warehouses. The document discusses data mining concepts like data extraction, data warehousing, the data mining process, applications, and issues. Major trends in data mining include datafication of enterprises, use of Hadoop for large datasets, and in-database analytics for performance.
The document describes data workflows and data integration systems. It defines a data integration system as IS=<O,S,M> where O is a global schema, S is a set of data sources, and M are mappings between them. It discusses different views of data workflows including ETL processes, Linked Data workflows, and the data science process. Key steps in data workflows include extraction, integration, cleansing, enrichment, etc. Tools to support different steps are also listed. The document introduces global-as-view (GAV) and local-as-view (LAV) approaches to specifying the mappings M between the global and local schemas using conjunctive rules.
Talk at ISIM 2017 in Durham, UK on applying database techniques to querying model results in the geosciences, with a broader position about the interaction between data science and simulation as modes of scientific inquiry.
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
This lecture presented at Remote Sensing, Uncertainty Quantification and a Theory of Data Systems Workshop - Cahill Center, California Institute of Technology
This document provides an introduction to machine learning. It discusses machine learning background, including the differences between artificial intelligence, machine learning, and deep learning. It also covers machine learning algorithms, applications, and how machine learning works. Example machine learning techniques discussed include classification using k-nearest neighbors, naive Bayes, and decision trees, as well as clustering with k-means.
This document provides an overview of Continuum Analytics and Python for data science. It discusses how Continuum created two organizations, Anaconda and NumFOCUS, to support open source Python data science software. It then describes Continuum's Anaconda distribution, which brings together 200+ open source packages like NumPy, SciPy, Pandas, Scikit-learn, and Jupyter that are used for data science workflows involving data loading, analysis, modeling, and visualization. The document outlines how Continuum helps accelerate adoption of data science through Anaconda and provides examples of industries using Python for data science.
201412 Predictive Analytics Foundation course extractJefferson Lynch
This document provides an overview of predictive analytics techniques including:
- Measuring relationships between variables using correlation for numeric data.
- The data mining process of building descriptive and predictive models with or without a target variable.
- Common data mining techniques including decision trees, regression, clustering, and affinity analysis that can be applied to individual-level data.
Strong Baselines for Neural Semi-supervised Learning under Domain ShiftSebastian Ruder
Oral presentation given at ACL 2018 about our paper Strong Baselines for Neural Semi-supervised Learning under Domain Shift (http://aclweb.org/anthology/P18-1096).
Machine Learning part 3 - Introduction to data science Frank Kienle
Lecture: Introduction to Data Science
given 2017 at Technical University of Kaiserslautern, Germany
Topic: part 3 machine learning, link to data science practice
If you are curious what is ML all about, this is a gentle introduction to Machine Learning and Deep Learning. This includes questions such as why ML/Data Analytics/Deep Learning ? Intuitive Understanding o how they work and some models in detail. At last I share some useful resources to get started.
This document provides an introduction to machine learning. It discusses what machine learning is, using examples like credit default prediction using logistic regression. The key reasons for the popularity of machine learning currently are the availability of large amounts of cheap data, the algorithmic economy of machine learning tools and platforms, and cloud-based machine learning solutions that make these techniques accessible. Various machine learning concepts are also introduced, such as feature vectors, supervised vs unsupervised learning, and terminology.
The document discusses a lecture on red-black trees, which are self-balancing binary search trees. It provides an overview of red-black trees, including that they ensure the tree is balanced so that operations take O(log n) time. The lecture covers red-black trees and their implementation in Java. Next week will include a midterm exam on the material covered so far.
An online semantic enhanced dirichlet model for short textJay Kumarr
The document presents a new online semantic-enhanced Dirichlet model (OSDM) for clustering short text streams. OSDM addresses challenges with existing approaches like semantic ambiguity, concept drift over time, and batch vs online processing. It maintains active topics online using a non-parametric probabilistic graphical model that incorporates semantic information through term co-occurrence and performs automatic topic detection. Experimental results on news, tweet and Reuters datasets show OSDM outperforms other models in clustering performance over data streams and is robust to parameter changes.
This document provides an outline and overview of key concepts related to data mining. It begins with an introduction to data mining and related tasks such as classification, clustering, and association rule mining. It then discusses concepts that are related to data mining, such as databases, information retrieval, and dimensional modeling. Finally, it outlines common data mining techniques including statistics, similarity measures, decision trees, and neural networks. The overall goal is to introduce the main components and approaches used in data mining.
This document provides an outline for a lecture on exploratory data analysis and hypothesis testing. The lecture will cover topics like descriptive versus inferential statistics, examples of business questions that can be answered through data analysis, and different types of charts for data visualization, including scatter plots, histograms, and box plots. It will also discuss concepts like probability distributions, the need for models to simplify real-world data, and how hypothesis testing works through setting a null hypothesis and calculating p-values. The class will include an exercise in exploratory data analysis and hypothesis testing in Python and a review of student projects.
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. Organizations can then use these insights to strengthen customer relationships, improve service delivery and drive new opportunities. To help guide the processes and activities within a given domain, data scientists and engineers need a foundational methodology that provides a framework for how to proceed with whichever methods or tools they will use to obtain answers and deliver results. In this presentation, we will share data science tips for data engineers.
Join the Data Science Experience: http://ibm.co/data-science
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsJason Riedy
Graph-structured data in network security, social networks, finance, and other applications not only are massive but also under continual evolution. The changes often are scattered across the graph, permitting novel parallel and incremental analysis algorithms. We discuss analysis algorithms for streaming graph data to maintain both local and global metrics with low latency and high efficiency.
Graph Analysis Trends and Opportunities -- CMG Performance and Capacity 2014Jason Riedy
High-performance graph analysis is unlocking knowledge in problems like anomaly detection in computer security, community structure in social networks, and many other data integration areas. While graphs provide a convenient abstraction, real-world problems' sparsity and lack of locality challenge current systems. This talk will cover current trends ranging from massive scales to low-power, low-latency systems and summarize opportunities and directions for graphs and computing systems.
The document provides an introduction to data analytics using R given by Wei Zhong from NUS. It begins with an overview of Wei Zhong and his background in computational biology. The agenda is then outlined, covering key concepts in data analytics like logistic regression, decision trees, random forests, and evaluation metrics. An overview of data analytics is presented, distinguishing descriptive, predictive, and prescriptive analytics. Statistical learning techniques like linear models, tree-based models, and clustering are introduced. Key concepts like cross-validation that will be used in the hands-on session are then defined.
Data mining involves analyzing large datasets to extract hidden patterns and predictive information. It is used to discover useful information from large data repositories like data warehouses. The document discusses data mining concepts like data extraction, data warehousing, the data mining process, applications, and issues. Major trends in data mining include datafication of enterprises, use of Hadoop for large datasets, and in-database analytics for performance.
The document describes data workflows and data integration systems. It defines a data integration system as IS=<O,S,M> where O is a global schema, S is a set of data sources, and M are mappings between them. It discusses different views of data workflows including ETL processes, Linked Data workflows, and the data science process. Key steps in data workflows include extraction, integration, cleansing, enrichment, etc. Tools to support different steps are also listed. The document introduces global-as-view (GAV) and local-as-view (LAV) approaches to specifying the mappings M between the global and local schemas using conjunctive rules.
Talk at ISIM 2017 in Durham, UK on applying database techniques to querying model results in the geosciences, with a broader position about the interaction between data science and simulation as modes of scientific inquiry.
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
This lecture presented at Remote Sensing, Uncertainty Quantification and a Theory of Data Systems Workshop - Cahill Center, California Institute of Technology
This document provides an introduction to machine learning. It discusses machine learning background, including the differences between artificial intelligence, machine learning, and deep learning. It also covers machine learning algorithms, applications, and how machine learning works. Example machine learning techniques discussed include classification using k-nearest neighbors, naive Bayes, and decision trees, as well as clustering with k-means.
This document provides an overview of Continuum Analytics and Python for data science. It discusses how Continuum created two organizations, Anaconda and NumFOCUS, to support open source Python data science software. It then describes Continuum's Anaconda distribution, which brings together 200+ open source packages like NumPy, SciPy, Pandas, Scikit-learn, and Jupyter that are used for data science workflows involving data loading, analysis, modeling, and visualization. The document outlines how Continuum helps accelerate adoption of data science through Anaconda and provides examples of industries using Python for data science.
201412 Predictive Analytics Foundation course extractJefferson Lynch
This document provides an overview of predictive analytics techniques including:
- Measuring relationships between variables using correlation for numeric data.
- The data mining process of building descriptive and predictive models with or without a target variable.
- Common data mining techniques including decision trees, regression, clustering, and affinity analysis that can be applied to individual-level data.
Strong Baselines for Neural Semi-supervised Learning under Domain ShiftSebastian Ruder
Oral presentation given at ACL 2018 about our paper Strong Baselines for Neural Semi-supervised Learning under Domain Shift (http://aclweb.org/anthology/P18-1096).
Machine Learning part 3 - Introduction to data science Frank Kienle
Lecture: Introduction to Data Science
given 2017 at Technical University of Kaiserslautern, Germany
Topic: part 3 machine learning, link to data science practice
If you are curious what is ML all about, this is a gentle introduction to Machine Learning and Deep Learning. This includes questions such as why ML/Data Analytics/Deep Learning ? Intuitive Understanding o how they work and some models in detail. At last I share some useful resources to get started.
This document provides an introduction to machine learning. It discusses what machine learning is, using examples like credit default prediction using logistic regression. The key reasons for the popularity of machine learning currently are the availability of large amounts of cheap data, the algorithmic economy of machine learning tools and platforms, and cloud-based machine learning solutions that make these techniques accessible. Various machine learning concepts are also introduced, such as feature vectors, supervised vs unsupervised learning, and terminology.
The document discusses a lecture on red-black trees, which are self-balancing binary search trees. It provides an overview of red-black trees, including that they ensure the tree is balanced so that operations take O(log n) time. The lecture covers red-black trees and their implementation in Java. Next week will include a midterm exam on the material covered so far.
An online semantic enhanced dirichlet model for short textJay Kumarr
The document presents a new online semantic-enhanced Dirichlet model (OSDM) for clustering short text streams. OSDM addresses challenges with existing approaches like semantic ambiguity, concept drift over time, and batch vs online processing. It maintains active topics online using a non-parametric probabilistic graphical model that incorporates semantic information through term co-occurrence and performs automatic topic detection. Experimental results on news, tweet and Reuters datasets show OSDM outperforms other models in clustering performance over data streams and is robust to parameter changes.
This document provides an outline and overview of key concepts related to data mining. It begins with an introduction to data mining and related tasks such as classification, clustering, and association rule mining. It then discusses concepts that are related to data mining, such as databases, information retrieval, and dimensional modeling. Finally, it outlines common data mining techniques including statistics, similarity measures, decision trees, and neural networks. The overall goal is to introduce the main components and approaches used in data mining.
This document provides an outline for a lecture on exploratory data analysis and hypothesis testing. The lecture will cover topics like descriptive versus inferential statistics, examples of business questions that can be answered through data analysis, and different types of charts for data visualization, including scatter plots, histograms, and box plots. It will also discuss concepts like probability distributions, the need for models to simplify real-world data, and how hypothesis testing works through setting a null hypothesis and calculating p-values. The class will include an exercise in exploratory data analysis and hypothesis testing in Python and a review of student projects.
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. Organizations can then use these insights to strengthen customer relationships, improve service delivery and drive new opportunities. To help guide the processes and activities within a given domain, data scientists and engineers need a foundational methodology that provides a framework for how to proceed with whichever methods or tools they will use to obtain answers and deliver results. In this presentation, we will share data science tips for data engineers.
Join the Data Science Experience: http://ibm.co/data-science
How can I become a data scientist? What are the most valuable skills to learn for a data scientist now? Could I learn how to be a data scientist by going through online tutorials? What does a data scientist do?
These are only some of the questions that are being discussed online, on blogs, on forums and on knowledge-sharing platforms like Quora.
Let me share the Beginner's Guide to Data Science which will be really helpful to you.
Also Checkout: http://bit.ly/2Mub6xP
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 document discusses WEKA, an open-source data mining and machine learning tool. It summarizes how WEKA was used to analyze a bike sharing dataset from Washington D.C. to predict bike usage. Different WEKA techniques were explored, including classification algorithms like J48 and Naive Bayes. J48 performed best by visualizing decision trees. Clustering was also attempted but seasonal patterns were only partially distinguished. Overall, the dataset seemed better suited to classification than clustering for predicting bike usage.
1) Data analytics involves treating available digital data as a "gold mine" to obtain tangible outputs that can improve business efficiency when applied. Machine learning uses algorithms to correlate parameters in data and improve relationships.
2) The document provides an overview of getting started in data science, covering business objectives, statistical analysis, programming tools like R and Python, and problem-solving approaches like supervised and unsupervised learning.
3) It describes the iterative "rule of seven" process for data science projects, including collecting/preparing data, exploring/analyzing it, transforming features, applying models, evaluating performance, and visualizing results.
This document provides an introduction to advanced data analytics using R. It outlines the key steps in an analytics process: [1] understanding the domain; [2] obtaining and cleaning data; [3] reducing, transforming, and visualizing the data; [4] choosing analytical approaches; and [5] communicating results. As a first example, it analyzes a public dataset on ice cream consumption using R commands to summarize, visualize with histograms and boxplots, and explore relationships between variables like income, temperature, and consumption over time. The document demonstrates how to interpret these analyses and leverage additional tools in R to further understand the data.
Entity matching and entity resolution are becoming more important disciplines in data management over time, based on increasing number of data sources that should be addressed in economy that is undergoing digital transformation process, growing data volumes and increasing requirements related to data privacy. Data matching process is also called record linkage, entity matching or entity resolution in some published works. For long time research about the process was focused on matching entities from same dataset (i.e. deduplication) or from two datasets. Different algorithms used for matching different types of attributes were described in the literature, developed and implemented in data matching and data cleansing platforms. Entity resolution is element of larger entity integration process that include data acquisition, data profiling, data cleansing, schema alignment, data matching and data merge (fusion).
We can use motivating example of global pharmaceutical company with offices in more than 60 countries worldwide that migrated customer data from various legacy systems in different countries to new common CRM system in the cloud. Migration was phased by regions and countries, with new sources and data incrementally added and merged with data already migrated in previous phases. Entity integration in such case require deep understanding of data architectures, data content and each step of the process. Even with such deep understanding, design and implementation of the solution require many iterations in development process that consume human resources, time and financial resources. Reducing the number of iterations by automating and optimizing steps in the process can save vast amount of resources. There is a lot of available literature addressing any of the steps in the process, proposing different options for improvement of results or processing optimization, but the whole process still require a lot of human work and subject matter specific knowledge and many iterations to produce results that will have high F-measure (both high precision and recall). Most of the algorithms used in the various steps of the process are Human in the loop (HITL) algorithms that require human interaction. Human is always part of the simulation and consequently influences the outcome.
This paper is a part of the work in progress aimed to define conceptual framework that will try to automate and optimize some steps of entity integration process and try to reduce requirements for human influence in the process. In this paper focus will be on conceptual process definition, recommended data architecture and use of existing open source solutions for entity integration process automation and optimization.
Overview of a Machine Learning 11 week course I developed and trained software engineers at Dell on their way to become Data Scientists. Class is outline of Predictive Analytics methods using Python. I taught this class 8 separate occasions over 3 years.
Recently, in the fields Business Intelligence and Data Management, everybody is talking about data science, machine learning, predictive analytics and many other “clever” terms with promises to turn your data into gold. In this slides, we present the big picture of data science and machine learning. First, we define the context for data mining from BI perspective, and try to clarify various buzzwords in this field. Then we give an overview of the machine learning paradigms. After that, we are going to discuss - at a high level - the various data mining tasks, techniques and applications. Next, we will have a quick tour through the Knowledge Discovery Process. Screenshots from demos will be shown, and finally we conclude with some takeaway points.
Chapter 4 Classification in data sience .pdfAschalewAyele2
This document discusses data mining tasks related to predictive modeling and classification. It defines predictive modeling as using historical data to predict unknown future values, with a focus on accuracy. Classification is described as predicting categorical class labels based on a training set. Several classification algorithms are mentioned, including K-nearest neighbors, decision trees, neural networks, Bayesian networks, and support vector machines. The document also discusses evaluating classification performance using metrics like accuracy, precision, recall, and a confusion matrix.
OpenLSH - a framework for locality sensitive hashingJ Singh
The document discusses limitations of the k-means clustering algorithm and proposes alternatives like locality-sensitive hashing (LSH) for clustering large document collections. LSH hashes documents into "buckets" based on similarity so that similar documents are hashed to the same buckets, allowing efficient retrieval of nearest neighbors. The document demonstrates LSH using minhashing, which represents documents as sets of "shingles" or fragments, and hashes the minimum value found. It also describes an open-source implementation of LSH called OpenLSH that works with large-scale databases like Cassandra.
The document discusses various topics related to active learning and optimal experimental design. It begins with motivations for active learning such as collecting higher quality data being more useful than simply more data, and data collection often being expensive. It provides examples of applying active learning techniques to problems like classification with gene expression data, collaborative filtering for movie recommendations, sequencing genomes, and improving cell culture conditions. It then covers topics like uncertainty sampling, query by committee, and information-based loss functions for active learning. For optimal experimental design, it discusses techniques like A-optimal, D-optimal, and E-optimal design and how they can be applied to problems with linear models. It also covers extensions to non-linear models using techniques like sequential experimental design
This document summarizes a presentation given by Alexander Fok on big data. The presentation covered the scope of Check Point's big data forum, some of Check Point's big data projects, an overview of NoSQL databases, and challenges related to big data. It discussed topics like copying large files, world data volumes, the three V's of big data, NoSQL database types, and barriers to adopting NoSQL solutions.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Risk Management and Reliable Forecasting using Un-reliable Data (magennis) - ...Troy Magennis
To meet expectations and optimize flow, managing risk is an important part of Kanban. Anticipating and adapting to things that "go wrong" and the uncertainty they cause is topic of this session. We look at techniques for quantifying what risks should be considered important to deal with.
Although discouraging, forecasting size, effort, staff and cost is sometimes necessary. Of course we have to do as little of this as possible, but when we do, we have to do it well with the data we have available. Forecasting is made difficult by un-reliable information as inputs to our process – the amount of work is uncertain, the historical data we are basing our forecasts on is biased and tainted, the situation seems hopeless. But it isn't. Good decisions can be made on imperfect data, and this session discusses how. This session shows immediately usable and simple techniques to capture, analyze, cleanse and assess data, and then use that data for reliable forecasting.
Second and hopefully draft of LKCE 2014 talk.
Prof. Nikhat Fatma Mumtaz Husain Shaikh gave a guest lecture on business intelligence and analytics. She began by defining business intelligence and how analytics builds on it by using data to understand business performance and answer higher-value questions. She then discussed the three levels of analytics - descriptive, predictive, and prescriptive - and gave examples of the business payoffs that can result from building analytic models in each area. The rest of the lecture covered how to build analytic models using tools like Excel, Power BI, data mining software, simulation, and optimization. She recommended textbooks and online courses for learning more and provided examples of free tools to get started with analytics.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
13. Knowledge Discovery Process
– Data mining: the core
of knowledge discovery
process.
Data Cleaning
Data Integration
Databases
Preprocessed
Data
Task-relevant Data
Data transformations
Selection
Data Mining
Knowledge Interpretation