The document discusses data structures and their classification. It defines data structures as a systematic way to store and organize data for efficient use. Data structures can be primitive or non-primitive. Primitive structures are basic data types like integers while non-primitive structures are composed of primitive types, like linked lists. Data structures are also classified as linear or non-linear. Linear structures like arrays and linked lists arrange data in a sequence while non-linear structures like trees represent hierarchical relationships. Common linear structures discussed are stacks, queues, and linked lists and non-linear graphs and trees are also described.
Classification is a popular data mining technique that assigns items to target categories or classes. It builds models called classifiers to predict the class of records with unknown class labels. Some common applications of classification include fraud detection, target marketing, and medical diagnosis. Classification involves a learning step where a model is constructed by analyzing a training set with class labels, and a classification step where the model predicts labels for new data. Supervised learning uses labeled data to train machine learning algorithms to produce correct outcomes for new examples.
This document outlines the learning objectives and resources for a course on data mining and analytics. The course aims to:
1) Familiarize students with key concepts in data mining like association rule mining and classification algorithms.
2) Teach students to apply techniques like association rule mining, classification, cluster analysis, and outlier analysis.
3) Help students understand the importance of applying data mining concepts across different domains.
The primary textbook listed is "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber. Topics that will be covered include introduction to data mining, preprocessing, association rules, classification algorithms, cluster analysis, and applications.
This document discusses decision trees and entropy. It begins by providing examples of binary and numeric decision trees used for classification. It then describes characteristics of decision trees such as nodes, edges, and paths. Decision trees are used for classification by organizing attributes, values, and outcomes. The document explains how to build decision trees using a top-down approach and discusses splitting nodes based on attribute type. It introduces the concept of entropy from information theory and how it can measure the uncertainty in data for classification. Entropy is the minimum number of questions needed to identify an unknown value.
The document provides information about key concepts in relational databases including:
- Components of a relational database include tables made up of rows and columns that store related data.
- Database schemas define the structure and relationships of tables.
- Relationships between tables can be one-to-one, one-to-many, or many-to-many.
- Integrity rules like entity and referential integrity enforce data consistency within and between related tables.
This document discusses different types of data:
- Nominal categorical data has no units or meaningful ordering between categories like sex or blood type.
- Ordinal categorical data can be ordered but not measured, like the Glasgow Coma Scale.
- Discrete metric data comes from counting things with units like number of hospital visits.
- Continuous metric data results from measurement and has real number values and units like weight or blood pressure.
The type of analysis depends on whether the data is categorical or metric, nominal or ordered, discrete or continuous.
The document discusses data cleaning which aims to fill in missing values, smooth noisy data, identify outliers, and resolve inconsistencies in data. It describes various techniques for handling dirty data issues like missing values, noisy data, and inconsistencies. The goal of data cleaning is to produce high quality data that can be effectively mined to generate meaningful and useful results.
This document provides an overview of key concepts in data collection and analysis. It defines different types of data, such as primary and secondary data, and variables like independent, dependent, and mediating variables. It also discusses important steps in data processing like editing, coding, data entry, and cleaning. The document explains why computers are useful for data processing and analysis. It then covers specific techniques for coding, entering data into spreadsheets, and cleaning data. Finally, it discusses different types of data analysis including descriptive and inferential statistics, and choosing the appropriate analytical techniques based on factors like sampling design and variable types.
The document discusses data structures and their classification. It defines data structures as a systematic way to store and organize data for efficient use. Data structures can be primitive or non-primitive. Primitive structures are basic data types like integers while non-primitive structures are composed of primitive types, like linked lists. Data structures are also classified as linear or non-linear. Linear structures like arrays and linked lists arrange data in a sequence while non-linear structures like trees represent hierarchical relationships. Common linear structures discussed are stacks, queues, and linked lists and non-linear graphs and trees are also described.
Classification is a popular data mining technique that assigns items to target categories or classes. It builds models called classifiers to predict the class of records with unknown class labels. Some common applications of classification include fraud detection, target marketing, and medical diagnosis. Classification involves a learning step where a model is constructed by analyzing a training set with class labels, and a classification step where the model predicts labels for new data. Supervised learning uses labeled data to train machine learning algorithms to produce correct outcomes for new examples.
This document outlines the learning objectives and resources for a course on data mining and analytics. The course aims to:
1) Familiarize students with key concepts in data mining like association rule mining and classification algorithms.
2) Teach students to apply techniques like association rule mining, classification, cluster analysis, and outlier analysis.
3) Help students understand the importance of applying data mining concepts across different domains.
The primary textbook listed is "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber. Topics that will be covered include introduction to data mining, preprocessing, association rules, classification algorithms, cluster analysis, and applications.
This document discusses decision trees and entropy. It begins by providing examples of binary and numeric decision trees used for classification. It then describes characteristics of decision trees such as nodes, edges, and paths. Decision trees are used for classification by organizing attributes, values, and outcomes. The document explains how to build decision trees using a top-down approach and discusses splitting nodes based on attribute type. It introduces the concept of entropy from information theory and how it can measure the uncertainty in data for classification. Entropy is the minimum number of questions needed to identify an unknown value.
The document provides information about key concepts in relational databases including:
- Components of a relational database include tables made up of rows and columns that store related data.
- Database schemas define the structure and relationships of tables.
- Relationships between tables can be one-to-one, one-to-many, or many-to-many.
- Integrity rules like entity and referential integrity enforce data consistency within and between related tables.
This document discusses different types of data:
- Nominal categorical data has no units or meaningful ordering between categories like sex or blood type.
- Ordinal categorical data can be ordered but not measured, like the Glasgow Coma Scale.
- Discrete metric data comes from counting things with units like number of hospital visits.
- Continuous metric data results from measurement and has real number values and units like weight or blood pressure.
The type of analysis depends on whether the data is categorical or metric, nominal or ordered, discrete or continuous.
The document discusses data cleaning which aims to fill in missing values, smooth noisy data, identify outliers, and resolve inconsistencies in data. It describes various techniques for handling dirty data issues like missing values, noisy data, and inconsistencies. The goal of data cleaning is to produce high quality data that can be effectively mined to generate meaningful and useful results.
This document provides an overview of key concepts in data collection and analysis. It defines different types of data, such as primary and secondary data, and variables like independent, dependent, and mediating variables. It also discusses important steps in data processing like editing, coding, data entry, and cleaning. The document explains why computers are useful for data processing and analysis. It then covers specific techniques for coding, entering data into spreadsheets, and cleaning data. Finally, it discusses different types of data analysis including descriptive and inferential statistics, and choosing the appropriate analytical techniques based on factors like sampling design and variable types.
This presentation demonstrated the fundamental of SPSS for beginner to learn what is SPSS and how to create variables and define their definition.
Thank you for your interest.
Please contact for more detail.
Logical data independence refers to the ability to change the logical schema without affecting external schemas or application programs. Physical data independence means changing the physical schema without changing the logical schema.
The Entity-Relationship model represents data structures graphically using three main elements - entity sets which are collections of similar entities, attributes which are properties of entities, and relationships which connect two or more entity sets.
This document discusses data processing and analysis. It defines key terms like data, information, variables, and cases. It explains that data processing involves collecting, organizing, and analyzing raw data to produce useful information. The main steps in data processing are editing, coding, classification, data entry, validation, and tabulation. Types of data processing include manual, electronic data processing (EDP), real-time processing, and batch processing. The data processing cycle involves input, processing, and output stages to convert data into accurate and useful information.
The conference presentation of the article:
L. Akritidis, P. Bozanis, "Effective Unsupervised Matching of Product Titles with k-Combinations and Permutations", In Proceedings of the 14th IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA), pp. 1-10, 2018.
which was presented in Thessaloniki, Greece in 2018.
Data Science as a Career and Intro to RAnshik Bansal
This document discusses data science as a career option and provides an overview of the roles of data analyst, data scientist, and data engineer. It notes that data analysts solve problems using existing tools and manage data quality, while data scientists are responsible for undirected research and strategic planning. Data engineers compile and install database systems. The document also outlines the typical salaries for each role and discusses the growing demand for data science skills. It provides recommendations for learning tools and resources to pursue a career in data science.
Data analysis involves editing, coding, sorting, and entering data to discover useful information. The process includes gathering data from various sources, reviewing it, and analyzing it to form conclusions. Specifically, editing ensures quality by reviewing collected data, coding categorizes data for computer analysis, sorting arranges data in a meaningful order for comprehension, and data entry inputs collected information into a computer. Finally, a master chart compiles all essential data in one format.
The document discusses the entity-relationship (ER) model, which is a top-down approach for conceptual database design. The ER model represents real-world objects as entities and relationships between entities. An ER diagram visually shows entities, attributes, and relationships. The model has advantages such as mapping well to the relational model and being easy to understand. It allows communicating the database design to users and serving as a design plan for developers.
This document discusses different methods of item analysis, including classical analysis and latent trait models like Rasch and Item Response Theory (IRT). Classical analysis uses statistics like difficulty, discrimination, and reliability to evaluate test questions based on how a particular group of students performed. Latent trait models aim to measure underlying abilities and provide sample-free measurement of questions. Rasch and IRT models use item characteristic curves and estimate parameters like difficulty, discrimination, and guessing to characterize questions independently of particular test administrations. The document provides an overview of the assumptions, statistics, and software used for different item analysis methods.
The document discusses the relational model and its key concepts including tables, rows, columns, domains, relations, and Codd's rules for what constitutes a relational database management system. It explains that a relational database consists of multiple tables where each row represents a relationship between column values. The document also covers relational integrity constraints, database languages, and Codd's 12 rules for RDBMS including logical data independence and comprehensive relational sublanguages.
Unsupervised Main Entity Extraction from News Articles using Latent VariablesJinho Choi
This document presents a methodology for semi-unsupervised main entity extraction from news articles using latent variables. It trains a semi-supervised model using only semantic and lexical information from raw text to automatically extract main entities from articles. The extracted entities are evaluated based on word sequence matches between the entities and news article titles, with the evaluation metric for this task needing improvement.
This document provides an introduction to statistics as it relates to language study. It discusses why statistics is needed in linguistic research, gives examples of quantitative data used in language studies, and defines key statistical terms like population, sampling, descriptive statistics, and inferential statistics. It also provides tasks for the reader to apply the concepts by giving examples of different statistical variables and sampling methods from linguistic research.
This presentation covers the intricacies of the Item Response Theory. I made this presentation to explain the concepts of IRT to my lab research group at the University of Minnesota. I have taken the contents from various sources so apologies for the poor design of the presentation.
Linear search is a simple algorithm for finding an element within a list. It involves traversing the list sequentially from the beginning, comparing each element to the target value until a match is found or the end of the list is reached. The steps are to read the search element, compare it to the first list element, check for a match, and if no match then compare to the next element until the end of the list. It has an complexity of O(n) but is practical for small lists or a single search of an unordered list.
SPSS is statistical software used by researchers to perform statistical analysis. It was first released in 1968 as the Statistical Package for the Social Sciences. SPSS is now owned by IBM and allows users to manage and analyze data, perform statistical tests, and produce graphs and reports. Researchers use SPSS to clean, code, and enter data, choose appropriate statistical tests to analyze the data, and interpret the results.
XLMiner provides several data utilities for sampling, handling missing data, and transforming categorical variables. It offers simple random sampling, stratified sampling in proportionate and specified sizes, and the ability to detect and handle missing data through deletion, mean/median/mode imputation, or user-specified values. Categorical variables can be transformed into dummy variables or numeric category scores. These utilities aid in data preparation and transformation for analysis.
This document discusses an attribute grammar for type checking assignment statements. It defines two key non-terminal attributes: actual_type, which represents the type of an expression or variable, and expected_type, which represents the expected type for an expression. The example checks that the type of the left side of an assignment matches the type of the right side based on type rules for variables and expressions.
Steering Model Selection with Visual Diagnostics: Women in Analytics 2019Rebecca Bilbro
Machine learning is ultimately a search for the best combination of features, algorithm, and hyperparameters that result in the best performing model. Oftentimes, this leads us to stay in our algorithmic comfort zones, or to resort to automated processes such as grid searches and random walks. Whether we stick to what we know or try many combinations, we are sometimes left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this tutorial, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more informed, and more effective.
This document provides an overview of using SPSS for data analysis in research. It discusses the major steps, including entering data, descriptive statistics, and inferential statistics. For entering data, it describes setting up variables, data types, labels, and missing values. Descriptive statistics are used to summarize data through measures of central tendency (mean, median, mode) and excentricity (range, standard deviation). Inferential statistics help determine if differences found are due to chance or reflect true effects in the population through hypothesis testing. The null hypothesis is that samples are from the same population.
1) An abstract data type (ADT) defines the operations that can be performed on a certain type of data but does not define how those operations are implemented.
2) Common examples of ADTs include lists, stacks, queues, and trees. Each ADT has multiple possible concrete data type implementations.
3) To define an ADT, one identifies the essential data fields and operations without specifying how they are stored or computed. This provides an abstract model of the problem domain.
This document provides an overview of key concepts in statistics. It discusses that statistics involves collecting, organizing, analyzing and interpreting data. It also defines important statistical terms like population, sample, parameter, statistic, qualitative and quantitative data, independent and dependent variables, discrete and continuous variables, and different levels of measurement for variables. The different levels of measurement are nominal, ordinal, interval and ratio. Descriptive statistics are used to summarize and describe data, while inferential statistics allow making inferences about populations from samples.
biostatistical data and their types.pptxAdeshthombre
This document discusses the important role of biostatisticians in research. It notes that biostatisticians play an essential role in evaluating the rigor and reproducibility of research methods through quantitative review of protocols. Additionally, it states that effective data management is crucial for producing reliable results, and that biostatisticians collaborate closely with researchers throughout the entire process from study design to analysis and presentation of findings.
This presentation demonstrated the fundamental of SPSS for beginner to learn what is SPSS and how to create variables and define their definition.
Thank you for your interest.
Please contact for more detail.
Logical data independence refers to the ability to change the logical schema without affecting external schemas or application programs. Physical data independence means changing the physical schema without changing the logical schema.
The Entity-Relationship model represents data structures graphically using three main elements - entity sets which are collections of similar entities, attributes which are properties of entities, and relationships which connect two or more entity sets.
This document discusses data processing and analysis. It defines key terms like data, information, variables, and cases. It explains that data processing involves collecting, organizing, and analyzing raw data to produce useful information. The main steps in data processing are editing, coding, classification, data entry, validation, and tabulation. Types of data processing include manual, electronic data processing (EDP), real-time processing, and batch processing. The data processing cycle involves input, processing, and output stages to convert data into accurate and useful information.
The conference presentation of the article:
L. Akritidis, P. Bozanis, "Effective Unsupervised Matching of Product Titles with k-Combinations and Permutations", In Proceedings of the 14th IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA), pp. 1-10, 2018.
which was presented in Thessaloniki, Greece in 2018.
Data Science as a Career and Intro to RAnshik Bansal
This document discusses data science as a career option and provides an overview of the roles of data analyst, data scientist, and data engineer. It notes that data analysts solve problems using existing tools and manage data quality, while data scientists are responsible for undirected research and strategic planning. Data engineers compile and install database systems. The document also outlines the typical salaries for each role and discusses the growing demand for data science skills. It provides recommendations for learning tools and resources to pursue a career in data science.
Data analysis involves editing, coding, sorting, and entering data to discover useful information. The process includes gathering data from various sources, reviewing it, and analyzing it to form conclusions. Specifically, editing ensures quality by reviewing collected data, coding categorizes data for computer analysis, sorting arranges data in a meaningful order for comprehension, and data entry inputs collected information into a computer. Finally, a master chart compiles all essential data in one format.
The document discusses the entity-relationship (ER) model, which is a top-down approach for conceptual database design. The ER model represents real-world objects as entities and relationships between entities. An ER diagram visually shows entities, attributes, and relationships. The model has advantages such as mapping well to the relational model and being easy to understand. It allows communicating the database design to users and serving as a design plan for developers.
This document discusses different methods of item analysis, including classical analysis and latent trait models like Rasch and Item Response Theory (IRT). Classical analysis uses statistics like difficulty, discrimination, and reliability to evaluate test questions based on how a particular group of students performed. Latent trait models aim to measure underlying abilities and provide sample-free measurement of questions. Rasch and IRT models use item characteristic curves and estimate parameters like difficulty, discrimination, and guessing to characterize questions independently of particular test administrations. The document provides an overview of the assumptions, statistics, and software used for different item analysis methods.
The document discusses the relational model and its key concepts including tables, rows, columns, domains, relations, and Codd's rules for what constitutes a relational database management system. It explains that a relational database consists of multiple tables where each row represents a relationship between column values. The document also covers relational integrity constraints, database languages, and Codd's 12 rules for RDBMS including logical data independence and comprehensive relational sublanguages.
Unsupervised Main Entity Extraction from News Articles using Latent VariablesJinho Choi
This document presents a methodology for semi-unsupervised main entity extraction from news articles using latent variables. It trains a semi-supervised model using only semantic and lexical information from raw text to automatically extract main entities from articles. The extracted entities are evaluated based on word sequence matches between the entities and news article titles, with the evaluation metric for this task needing improvement.
This document provides an introduction to statistics as it relates to language study. It discusses why statistics is needed in linguistic research, gives examples of quantitative data used in language studies, and defines key statistical terms like population, sampling, descriptive statistics, and inferential statistics. It also provides tasks for the reader to apply the concepts by giving examples of different statistical variables and sampling methods from linguistic research.
This presentation covers the intricacies of the Item Response Theory. I made this presentation to explain the concepts of IRT to my lab research group at the University of Minnesota. I have taken the contents from various sources so apologies for the poor design of the presentation.
Linear search is a simple algorithm for finding an element within a list. It involves traversing the list sequentially from the beginning, comparing each element to the target value until a match is found or the end of the list is reached. The steps are to read the search element, compare it to the first list element, check for a match, and if no match then compare to the next element until the end of the list. It has an complexity of O(n) but is practical for small lists or a single search of an unordered list.
SPSS is statistical software used by researchers to perform statistical analysis. It was first released in 1968 as the Statistical Package for the Social Sciences. SPSS is now owned by IBM and allows users to manage and analyze data, perform statistical tests, and produce graphs and reports. Researchers use SPSS to clean, code, and enter data, choose appropriate statistical tests to analyze the data, and interpret the results.
XLMiner provides several data utilities for sampling, handling missing data, and transforming categorical variables. It offers simple random sampling, stratified sampling in proportionate and specified sizes, and the ability to detect and handle missing data through deletion, mean/median/mode imputation, or user-specified values. Categorical variables can be transformed into dummy variables or numeric category scores. These utilities aid in data preparation and transformation for analysis.
This document discusses an attribute grammar for type checking assignment statements. It defines two key non-terminal attributes: actual_type, which represents the type of an expression or variable, and expected_type, which represents the expected type for an expression. The example checks that the type of the left side of an assignment matches the type of the right side based on type rules for variables and expressions.
Steering Model Selection with Visual Diagnostics: Women in Analytics 2019Rebecca Bilbro
Machine learning is ultimately a search for the best combination of features, algorithm, and hyperparameters that result in the best performing model. Oftentimes, this leads us to stay in our algorithmic comfort zones, or to resort to automated processes such as grid searches and random walks. Whether we stick to what we know or try many combinations, we are sometimes left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this tutorial, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more informed, and more effective.
This document provides an overview of using SPSS for data analysis in research. It discusses the major steps, including entering data, descriptive statistics, and inferential statistics. For entering data, it describes setting up variables, data types, labels, and missing values. Descriptive statistics are used to summarize data through measures of central tendency (mean, median, mode) and excentricity (range, standard deviation). Inferential statistics help determine if differences found are due to chance or reflect true effects in the population through hypothesis testing. The null hypothesis is that samples are from the same population.
1) An abstract data type (ADT) defines the operations that can be performed on a certain type of data but does not define how those operations are implemented.
2) Common examples of ADTs include lists, stacks, queues, and trees. Each ADT has multiple possible concrete data type implementations.
3) To define an ADT, one identifies the essential data fields and operations without specifying how they are stored or computed. This provides an abstract model of the problem domain.
This document provides an overview of key concepts in statistics. It discusses that statistics involves collecting, organizing, analyzing and interpreting data. It also defines important statistical terms like population, sample, parameter, statistic, qualitative and quantitative data, independent and dependent variables, discrete and continuous variables, and different levels of measurement for variables. The different levels of measurement are nominal, ordinal, interval and ratio. Descriptive statistics are used to summarize and describe data, while inferential statistics allow making inferences about populations from samples.
biostatistical data and their types.pptxAdeshthombre
This document discusses the important role of biostatisticians in research. It notes that biostatisticians play an essential role in evaluating the rigor and reproducibility of research methods through quantitative review of protocols. Additionally, it states that effective data management is crucial for producing reliable results, and that biostatisticians collaborate closely with researchers throughout the entire process from study design to analysis and presentation of findings.
This document provides an overview of data analysis and graphical representation. It discusses data analytics, statistics, quantitative and qualitative data, different types of graphical representations including line graphs, bar graphs and histograms. It also covers sampling design, types of sampling including probability and non-probability sampling, and measures of central tendency such as mean, median and mode.
In educational research, Research errors may be grouped under some headings:
1. Sampling errors
2. Measurement errors
3. Statistical errors
4. Interpretation errors
along with suggestions to reduce them
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding, categorizing, comparing and interpreting collected data to find meanings and implications. The researcher's perspective influences the analysis. It also describes techniques for qualitative data analysis like becoming familiar with the data, providing in-depth descriptions, and categorizing data into themes. Ensuring credibility involves considering factors like the researcher's observations and biases. The document also contrasts qualitative data analysis with quantitative analysis.
This document discusses different types of data and measurement scales used in statistics. It defines nominal, ordinal, interval, and ratio data, providing examples of each. It also discusses blind and double-blind experimental designs, explaining how randomization and blinding help reduce bias. Key terms discussed include mode, bimodal, random sampling, randomization, and simple random sampling.
The document discusses data analysis and interpretation. It describes the different scales of measurement used in data analysis including nominal, ordinal, interval, and ratio scales. It also discusses various methods used for interpreting qualitative and quantitative data, such as using statistical techniques like mean and standard deviation for quantitative data. Finally, it covers different visualization techniques used in data interpretation like bar graphs, pie charts, tables, and line graphs.
This document discusses various topics related to data preparation and analysis, including editing, coding, data entry, validity of data, qualitative vs quantitative analysis, bivariate and multivariate statistical techniques like multiple regression, factor analysis, discriminant analysis, cluster analysis, multidimensional scaling, and the application of statistical software like SAS, SPSS, and R. It provides an overview of key concepts and steps involved in preparing data for analysis and using different statistical methods to analyze the relationships between variables.
This document discusses key concepts in statistics including:
- Descriptive statistics involves collecting, organizing and presenting data to describe a situation. Inferential statistics involves making inferences about populations based on samples.
- There are different types of variables (qualitative, quantitative) and levels of measurement (nominal, ordinal, interval, ratio).
- Common data collection methods include surveys conducted by telephone, mail, or in-person interviews. Random sampling and stratified sampling are techniques for selecting samples from populations.
The document provides an overview of data analysis methods and concepts for graduate fellows. It covers:
1) The objectives of translating research questions into an analysis plan, identifying appropriate data analysis methods and software, and conducting exploratory analysis.
2) Key concepts in data analysis including response and explanatory variables, multi-level data structures, and exploratory versus confirmatory analysis.
3) Guidance on specific exploratory analysis methods and examples of confirmatory analysis options using different statistical models depending on variable types.
This document provides an overview of key concepts in data management and statistics. It defines statistics as the study of collecting, organizing, and interpreting data to make inferences about populations. The main branches are descriptive statistics, which summarizes data, and inferential statistics, which generalizes from samples to populations. It also defines key terms like population, sample, parameter, statistic, variable, data, levels of measurement, and measures of central tendency and dispersion. Measures of central tendency like mean, median, and mode are used to describe the center of data, while measures of dispersion like range and standard deviation describe how spread out data are.
This document provides an introduction to business statistics. It defines statistics as the science of collecting, organizing, summarizing, presenting, analyzing, and drawing conclusions from data. The document outlines the key components of statistics including descriptive statistics, which summarizes data, and inferential statistics, which makes generalizations about a population based on a sample. It also discusses different types of data, data sources, and the scope and importance of statistics in business decision making.
This is the best reference book for the subject of 'Statistics Math' that is useful for the students of BBA.
It has covered the course contents in a proper understanding way.
This document provides an introduction to business statistics. It defines statistics as the science of collecting, organizing, summarizing, presenting, analyzing, and drawing conclusions from data. The document outlines the key components of statistics including descriptive statistics, which summarizes data, and inferential statistics, which makes generalizations about a population based on a sample. It also discusses different types of data, data sources, and the scope and importance of statistics in business decision making.
Statistical inference is a process of making conclusions about a population based on a sample of data. It involves using statistical methods to draw inferences about the population parameters based on sample data. There are two main types of statistical inference: estimation and hypothesis testing. Estimation involves using sample data to estimate population parameter values like the mean or standard deviation, while hypothesis testing involves specifying and testing hypotheses about population parameters.
#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 document provides an introduction to descriptive statistics and measures of condensation. It defines key concepts including data, variables, descriptive versus inferential statistics, and different types of data such as nominal, ordinal, discrete, and continuous. It also discusses frequency distribution and different ways of presenting data through tables, charts and graphs. The goal of descriptive statistics and measures of condensation is to summarize and organize large datasets in a concise and meaningful way.
Data science notes for ASDS calicut 2.pptxswapnaraghav
Data science involves both statistics and practical hacking skills. It is the engineering of data - applying tools and theoretical understanding to data in a practical way. Statistical modeling is the process of using mathematical models to analyze and understand data in order to make general predictions. There are several statistical modeling techniques including linear regression, classification, resampling, non-linear models, tree-based methods, and neural networks. Unsupervised learning identifies patterns in data without pre-existing categories by techniques like clustering. Time series forecasting predicts future values based on patterns in historical time series data.
Data Analysis in Research: Descriptive Statistics & NormalityIkbal Ahmed
This document discusses different types of data and data analysis techniques used in research. It defines data as any set of characters gathered for analysis. Research data can take many forms including documents, laboratory notes, questionnaires, and digital outputs. There are two main types of data: quantitative data which can be measured numerically, and qualitative data involving words and symbols. Common quantitative analysis techniques described are descriptive statistics to summarize variables and inferential statistics to understand relationships. Qualitative analysis techniques include content analysis, narrative analysis and grounded theory.
This document provides an overview of using Python for web development. It discusses Python's features and popularity as a programming language. It also covers several popular web frameworks like Django, Flask, and Pyramid that can be used to build web applications in Python. Examples are given showing how to get started with simple web applications using Flask and Django. Finally, references are provided for further reading on Python basics, web frameworks, and language comparisons.
The document discusses big data and provides an overview of key topics including:
- The rapid growth of data being created and how over 90% was created in just the past 2 years;
- What big data is and how it refers to our ability to analyze the increasing volumes of data;
- Some applications of big data like understanding customers, optimizing processes, and improving health and security;
- The differences between data mining which involves more human interaction and machine learning which allows systems to learn without being programmed;
- Programming languages used for big data analysis like those demonstrated in a Jupyter notebook.
This document discusses information literacy and its importance in the workplace and information society. It provides definitions for key terms like information overload, knowledge economy, and information literacy. It discusses information literacy standards and contexts. It then discusses how employees at the company PlantMiner seek and evaluate information from sources like Google, LinkedIn, suppliers, and newsletters to help their roles in sales, business development, marketing, finance, and development.
Unit test & Continuous deployment is a presentation that covers unit testing, continuous deployment, and taking questions. It discusses what unit tests are and how they should isolate components, check single assumptions, and be automated. Continuous deployment is also mentioned regarding building and deploying code. The presentation concludes by taking questions.
Machine learning workshop, session 4.
- Generalization in Machine Learning
- Overfitting and Underfitting
- Algorithms by Similarity
- Real Application
- People to follow
Machine learning workshop, session 3.
- Data sets
- Machine Learning Algorithms
- Algorithms by Learning Style
- Algorithms by Similarity
- People to follow
The document discusses Docker Swarm, a Docker container orchestration tool. It provides an overview of key Swarm features like cluster management, service discovery, load balancing, rolling updates and high availability. It also discusses how to deploy applications using Swarm, including accessing GPUs, the deployment workflow, and using Swarm on ARM architectures. The conclusion states that the best orchestration tool depends on one's use case and preferences as each has advantages and disadvantages.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
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.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
19. Types of data
Numerical data. These data have meaning as a measurement, such as a person’s height, weight, IQ, or
blood pressure; or they’re a count, such as the number of stock shares a person owns.
Discrete data represent items that can be counted; they take on possible values that can be
listed out. The list of possible values may be fixed (also called finite); or it may go from 0, 1, 2, on to infinity
(making it countably infinite).
Continuous data represent measurements; their possible values cannot be counted and can
only be described using intervals on the real number line.
Categorical data: Categorical data represent characteristics such as a person’s gender, marital status,
hometown, or the types of movies they like.
21. Dependent and independent variables
In mathematical modeling, statistical modeling and experimental sciences,
there are dependent and independent variables.
The models or experiments investigate how the former depend on the latter.
The dependent variables represent the output or outcome whose variation is
being studied.
The independent variables represent inputs or causes.
22. Linear regression
Statistics
In an experiment, the dependent variable is the event
expected to change when the independent variable is
manipulated.
In data mining tools (for multivariate statistics and machine
learning), the depending variable is assigned a role as target
variable (or in some tools as label attribute), while a
dependent variable may be assigned a role as regular
variable.
Known values for the target variable are provided for the
training data set and test data set, but should be predicted for
other data. The target variable is used in supervised learning
algorithms but not in non-supervised learning.
StatisticsIn an experiment, the dependent variable is the event expected to change when the independent variable is manipulated.[8]In data mining tools (for multivariate statistics and machine learning), the depending variable is assigned a role as target variable (or in some tools as label attribute), while a dependent variable may be assigned a role as regular variable.[9] Known values for the target variable are provided for the training data set and test data set, but should be predicted for other data. The target variable is used in supervised learning algorithms but not in non-supervised learning.