This document provides an overview of exploratory data analysis techniques. It discusses what data is, common sources of data, and different data types and formats. Key steps in exploratory data analysis are introduced, including descriptive statistics, visualizations, and handling messy data. Common measures used to describe central tendency and spread of data are defined. The importance of visualization for exploring relationships and patterns in data is emphasized. Examples of different visualizations are provided.
Various statistical software's in data analysis.SelvaMani69
The document provides an overview of various statistical software used for data analysis. It discusses the history and emergence of statistical software, as well as common software packages for quantitative (e.g. SPSS, STATA, SAS) and qualitative (e.g. Atlas ti, HyperResearch) analysis. SPSS is described in more detail, including its point-and-click interface, ability to perform various analyses like regression and ANOVA, and examples of using it to code data, edit variable names, and create contingency tables. The document emphasizes that statistical software makes data analysis easier by automating calculations and reducing mathematical errors.
This document provides an overview of topics related to research and statistics, including research problems, variables, hypotheses, data collection, presentation, and analysis using SPSS. It discusses key concepts such as descriptive versus inferential statistics, point and interval estimates, and confidence intervals for means and proportions. The document serves as an introduction to research methodology and statistical analysis concepts.
This one-day workshop on data analysis using SPSS has two parts. Part 1 covers entering data into SPSS, including preparing datasets, transforming data, and running descriptive statistics. Part 2 provides an overview of statistical analysis techniques and how to choose the appropriate technique for decision making, giving examples. The document introduces SPSS and its four windows: the data editor, output viewer, syntax editor, and script window. It describes how to define variables, enter and manage data files, sort cases, compute new variables, and perform basic analyses like frequencies, descriptives, and linear regression. Proper use of statistical techniques depends on the research question, variable types and definitions, and assumptions.
Statistical software programs are used to analyze, organize, and present data. Some popular statistical software packages include SPSS, R, MATLAB, Microsoft Excel, SAS, GraphPad Prism, and Minitab. Another statistical software package is CoStat, which can analyze different data types and import data from various file formats. It uses procedures like ANOVA for data analysis. CoStat costs $140 for a license. Statistica is also a powerful statistical software that provides data analysis, management, mining and visualization tools. It has a customizable interface and supports programming for customization. Statistica allows analyzing large datasets without limits.
This document discusses various statistical software packages. It provides information on:
- Open source packages like R and SciPy which are free to use.
- Public domain packages such as CSPro and Epi Info which are developed by government organizations for use in fields like epidemiology.
- Freeware packages like WinBUGS and Winpepi that can be downloaded and used at no cost.
- Proprietary packages including SAS, SPSS, and MATLAB that usually require purchasing a license but provide comprehensive statistical functionality.
Commonly used statistical software in pharmacy include SAS, SPSS, GraphPad InStat, and GraphPad Prism. SPSS allows for a range of descriptive, bivariate
SPSS is a statistical software package used for data analysis in business research that was originally developed for social science applications. It allows users to import, organize, and analyze data using a variety of statistical procedures to generate reports and visualizations. SPSS has evolved over time from mainframe usage to its current version as a product of IBM after being acquired from SPSS Inc. in 2009.
A brief introduction for beginners. Topic included: background history of SPSS, some basics but effective data management techniques, frequency distribution, descriptive statistics, hypothesis testing rule, association test/ contingency table test. All these statistical topics are explained with easy hands on example with basic data-set. This slide also provide a short but effective understanding about p-value, which is very important for statistical decision making
SPSS (Statistical Package for the Social Sciences) is statistical software used for data management and analysis. It allows users to process questionnaires, report data in tables and graphs, and analyze data through various tests like means, chi-square, and regression. Originally called SPSS Inc., it is now owned by IBM and known as IBM SPSS Statistics. The document provides an introduction to SPSS and outlines how to define variables, enter data, select cases, run descriptive statistics like frequencies and crosstabs, and manipulate output files.
Various statistical software's in data analysis.SelvaMani69
The document provides an overview of various statistical software used for data analysis. It discusses the history and emergence of statistical software, as well as common software packages for quantitative (e.g. SPSS, STATA, SAS) and qualitative (e.g. Atlas ti, HyperResearch) analysis. SPSS is described in more detail, including its point-and-click interface, ability to perform various analyses like regression and ANOVA, and examples of using it to code data, edit variable names, and create contingency tables. The document emphasizes that statistical software makes data analysis easier by automating calculations and reducing mathematical errors.
This document provides an overview of topics related to research and statistics, including research problems, variables, hypotheses, data collection, presentation, and analysis using SPSS. It discusses key concepts such as descriptive versus inferential statistics, point and interval estimates, and confidence intervals for means and proportions. The document serves as an introduction to research methodology and statistical analysis concepts.
This one-day workshop on data analysis using SPSS has two parts. Part 1 covers entering data into SPSS, including preparing datasets, transforming data, and running descriptive statistics. Part 2 provides an overview of statistical analysis techniques and how to choose the appropriate technique for decision making, giving examples. The document introduces SPSS and its four windows: the data editor, output viewer, syntax editor, and script window. It describes how to define variables, enter and manage data files, sort cases, compute new variables, and perform basic analyses like frequencies, descriptives, and linear regression. Proper use of statistical techniques depends on the research question, variable types and definitions, and assumptions.
Statistical software programs are used to analyze, organize, and present data. Some popular statistical software packages include SPSS, R, MATLAB, Microsoft Excel, SAS, GraphPad Prism, and Minitab. Another statistical software package is CoStat, which can analyze different data types and import data from various file formats. It uses procedures like ANOVA for data analysis. CoStat costs $140 for a license. Statistica is also a powerful statistical software that provides data analysis, management, mining and visualization tools. It has a customizable interface and supports programming for customization. Statistica allows analyzing large datasets without limits.
This document discusses various statistical software packages. It provides information on:
- Open source packages like R and SciPy which are free to use.
- Public domain packages such as CSPro and Epi Info which are developed by government organizations for use in fields like epidemiology.
- Freeware packages like WinBUGS and Winpepi that can be downloaded and used at no cost.
- Proprietary packages including SAS, SPSS, and MATLAB that usually require purchasing a license but provide comprehensive statistical functionality.
Commonly used statistical software in pharmacy include SAS, SPSS, GraphPad InStat, and GraphPad Prism. SPSS allows for a range of descriptive, bivariate
SPSS is a statistical software package used for data analysis in business research that was originally developed for social science applications. It allows users to import, organize, and analyze data using a variety of statistical procedures to generate reports and visualizations. SPSS has evolved over time from mainframe usage to its current version as a product of IBM after being acquired from SPSS Inc. in 2009.
A brief introduction for beginners. Topic included: background history of SPSS, some basics but effective data management techniques, frequency distribution, descriptive statistics, hypothesis testing rule, association test/ contingency table test. All these statistical topics are explained with easy hands on example with basic data-set. This slide also provide a short but effective understanding about p-value, which is very important for statistical decision making
SPSS (Statistical Package for the Social Sciences) is statistical software used for data management and analysis. It allows users to process questionnaires, report data in tables and graphs, and analyze data through various tests like means, chi-square, and regression. Originally called SPSS Inc., it is now owned by IBM and known as IBM SPSS Statistics. The document provides an introduction to SPSS and outlines how to define variables, enter data, select cases, run descriptive statistics like frequencies and crosstabs, and manipulate output files.
SPSS statistics - how to use SPSS for research, analysis, and surveys. Includes instructions and examples of how to: define a data file and variables, correlation analysis, multiple response sets, creating and editing charts, and much, much more!
This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses opening and closing SPSS, the structure and windows of SPSS including the Data View and Variable View windows for entering data. It defines key concepts in SPSS like variables, different types of variables (nominal, ordinal, interval, ratio), and the process of defining variables in the Variable View window by specifying name, type, width, labels, values etc. before entering data. Examples are given around designing an experiment with independent and dependent variables and dealing with extraneous variables.
This document provides an introduction to using SPSS (Statistical Package for the Social Sciences) for data analysis. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and sorting data. Several basic analysis techniques are introduced, such as frequencies, descriptives, and linear regression. Examples are provided for how to conduct these analyses and interpret the outputs.
SPSS is a popular statistical analysis software that is known for its ease of use. It has strong graphical capabilities and supports a variety of statistical analyses. However, it lacks some more advanced statistical procedures and has limited data management tools. While suitable for many tasks, some users may outgrow it over time and require more specialized software like SAS or Stata for complex or cutting-edge analyses. Overall, SPSS is best suited for users performing basic to intermediate statistical analysis and reporting.
Introduction To Spss - Opening Data File and Descriptive AnalysisDr Ali Yusob Md Zain
This document provides instructions for opening an SPSS data file and performing descriptive analysis. It outlines the steps to open a data file from the File menu, select the file, and press Open. It then describes how to perform frequencies analysis by selecting variables, clicking Continue, and OK to output a frequencies table showing counts and percentages.
Data analysis lecture for IST 400/600 "Science Data Management" at the Syracuse University School of Information Studies. This lecture covers some basic high-level concepts involved in data analysis, with mild emphasis on scientific research contexts.
In this presentation various fundamental data analysis using Statistical Tool SPSS was elaborated with special reference to physical education and sports
This presentation introduces SPSS as a statistical analysis software tool. SPSS can be used for tasks like processing questionnaires, reporting data in tables and graphs, and analyzing data through various tests like means, chi-square, and regression. It is commonly used in fields like telecommunications, banking, finance, insurance, healthcare, manufacturing, retail, education, and market research. Key features of SPSS include its ease of use, robust data management and editing tools, in-depth statistical capabilities, and reporting features. The presentation demonstrates how to get data into SPSS, enter and edit variables and data, and describes the basic steps of data analysis using various statistical procedures available in SPSS.
Statistics is the study of collecting, organizing, analyzing, and interpreting data. It was introduced in 1791 in English by Sir John Sinclair when he published the first volume of a statistical account of Scotland. SPSS is a widely used software package for statistical analysis and data management. It allows users to easily enter and manage data, conduct statistical analyses, and display results in graphs and tables.
This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses the history and ownership of SPSS, its use as a statistical analysis program, and an overview of its basic functions. Key features covered include opening and managing data files, descriptive statistics like frequencies and charts, data cleaning techniques for handling missing values, and methods for data manipulation such as recoding variables and creating new computed variables. The goal is to provide readers with foundational knowledge on using SPSS for statistical analysis in the social sciences.
Statistics is the discipline dealing with the collection, manipulation, analysis, and interpretation of data to draw conclusions and make decisions. Statistical software packages are essential tools that allow statisticians to efficiently analyze large datasets using computers for simulation, data storage, calculations, analysis, and presentation of results. Some common statistical software packages include Excel, which supports basic statistical functions, and more specialized packages like Costat, Minitab, SAS, SPSS, and R, which provide advanced statistical analysis capabilities.
SPSS is statistical software used for statistical analysis, data manipulation, and generating tables and graphs summarizing data. It performs basic descriptive statistics as well as advanced statistical techniques like regression, ANOVA, and factor analysis. SPSS allows importing and exporting data from different sources and connecting to online databases. It is robust, but training is typically required to fully utilize its features. SPSS is widely used in research and development, quality control, compliance and validation, clinical trials, and data mining in the pharmaceutical industry.
This document provides an overview of SPSS (Statistical Package for the Social Sciences) from the perspective of Yacar-Yacara Consults, a strategy, research, and data analytics consulting firm. It discusses the meaning of statistical data analysis, the reasons for performing statistical analysis, and the types of data analysis including descriptive, associative, and inferential. It also covers topics like choosing a statistical software package, features of SPSS, preparing a codebook to define variables, and coding responses for data entry in SPSS.
Final spss hands on training (descriptive analysis) may 24th 2013Tin Myo Han
This document provides guidance on conducting valid data analysis in SPSS. It discusses:
1) The importance of solid study planning and design;
2) Ensuring a valid data set through proper data collection, entry, cleaning and manipulation;
3) Selecting appropriate statistical procedures that match the data types and meet statistical assumptions;
4) Correctly interpreting and summarizing results in the context of the research question.
SPSS is statistical analysis software. It can be used to perform a wide range of analyses from basic descriptive statistics to complex analyses like regression. The document discusses SPSS including its interface, how to define and enter data, and common analysis procedures. Key windows in the SPSS interface include the data editor, output navigator, and syntax window. Variables must be strongly defined by type before entering data. SPSS can then be used to analyze the data.
This document provides an overview of key tasks in data preprocessing for knowledge discovery and data mining. It discusses why preprocessing is important, as real-world data is often dirty, noisy, incomplete and inconsistent. The major tasks covered are data cleaning, integration and transformation, reduction, discretization and concept hierarchy generation. Data cleaning involves techniques for handling missing data, noisy data and inconsistent data. Data reduction aims to reduce data volume while preserving analytical results. Methods discussed include binning, clustering, dimensionality reduction and sampling. Discretization converts continuous attributes to discrete intervals.
Dr. Pragyan Paramita Parija's presentation outlines statistics and biostatistics concepts and discusses various statistical software tools used in public health. It introduces statistics and defines biostatistics as applying statistical tools to biological data from medicine and public health. The presentation describes steps for research, applications of statistical software in public health, advantages of using computer software, and commonly used software like Excel, Epi Info, SPSS, SAS, and R. It provides an overview of each software including costs, pros, and cons to help users select the appropriate software.
The document discusses key concepts in quantitative research methods and data analytics covered in a university course. It outlines the course content, which includes topics like data visualization, the normal distribution, and hypothesis testing. It then details the course assessments, which include a mid-term assignment and final coursework report worth 30% and 70% respectively. The final report involves selecting a topic, collecting and analyzing data using R Studio, and reporting the results in a 2000 word paper with sections on introduction, data, results, and conclusion.
The document provides an overview of a 3-day data analytics training program held in Jakarta, Indonesia from April 24-26, 2019. It discusses topics that will be covered including big data overview, data for business analysis, data analytics concepts, and data analytics tools. The training is led by Dr. Ir. John Sihotang and is aimed at management trainees of the company Sucofindo.
Data science combines fields like statistics, programming, and domain expertise to extract meaningful insights from data. It involves preparing, analyzing, and modeling data to discover useful information. Exploratory data analysis is the process of investigating data to understand its characteristics and check assumptions before modeling. There are four types of EDA: univariate non-graphical, univariate graphical, multivariate non-graphical, and multivariate graphical. Python and R are popular tools used for EDA due to their data analysis and visualization capabilities.
SPSS statistics - how to use SPSS for research, analysis, and surveys. Includes instructions and examples of how to: define a data file and variables, correlation analysis, multiple response sets, creating and editing charts, and much, much more!
This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses opening and closing SPSS, the structure and windows of SPSS including the Data View and Variable View windows for entering data. It defines key concepts in SPSS like variables, different types of variables (nominal, ordinal, interval, ratio), and the process of defining variables in the Variable View window by specifying name, type, width, labels, values etc. before entering data. Examples are given around designing an experiment with independent and dependent variables and dealing with extraneous variables.
This document provides an introduction to using SPSS (Statistical Package for the Social Sciences) for data analysis. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and sorting data. Several basic analysis techniques are introduced, such as frequencies, descriptives, and linear regression. Examples are provided for how to conduct these analyses and interpret the outputs.
SPSS is a popular statistical analysis software that is known for its ease of use. It has strong graphical capabilities and supports a variety of statistical analyses. However, it lacks some more advanced statistical procedures and has limited data management tools. While suitable for many tasks, some users may outgrow it over time and require more specialized software like SAS or Stata for complex or cutting-edge analyses. Overall, SPSS is best suited for users performing basic to intermediate statistical analysis and reporting.
Introduction To Spss - Opening Data File and Descriptive AnalysisDr Ali Yusob Md Zain
This document provides instructions for opening an SPSS data file and performing descriptive analysis. It outlines the steps to open a data file from the File menu, select the file, and press Open. It then describes how to perform frequencies analysis by selecting variables, clicking Continue, and OK to output a frequencies table showing counts and percentages.
Data analysis lecture for IST 400/600 "Science Data Management" at the Syracuse University School of Information Studies. This lecture covers some basic high-level concepts involved in data analysis, with mild emphasis on scientific research contexts.
In this presentation various fundamental data analysis using Statistical Tool SPSS was elaborated with special reference to physical education and sports
This presentation introduces SPSS as a statistical analysis software tool. SPSS can be used for tasks like processing questionnaires, reporting data in tables and graphs, and analyzing data through various tests like means, chi-square, and regression. It is commonly used in fields like telecommunications, banking, finance, insurance, healthcare, manufacturing, retail, education, and market research. Key features of SPSS include its ease of use, robust data management and editing tools, in-depth statistical capabilities, and reporting features. The presentation demonstrates how to get data into SPSS, enter and edit variables and data, and describes the basic steps of data analysis using various statistical procedures available in SPSS.
Statistics is the study of collecting, organizing, analyzing, and interpreting data. It was introduced in 1791 in English by Sir John Sinclair when he published the first volume of a statistical account of Scotland. SPSS is a widely used software package for statistical analysis and data management. It allows users to easily enter and manage data, conduct statistical analyses, and display results in graphs and tables.
This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses the history and ownership of SPSS, its use as a statistical analysis program, and an overview of its basic functions. Key features covered include opening and managing data files, descriptive statistics like frequencies and charts, data cleaning techniques for handling missing values, and methods for data manipulation such as recoding variables and creating new computed variables. The goal is to provide readers with foundational knowledge on using SPSS for statistical analysis in the social sciences.
Statistics is the discipline dealing with the collection, manipulation, analysis, and interpretation of data to draw conclusions and make decisions. Statistical software packages are essential tools that allow statisticians to efficiently analyze large datasets using computers for simulation, data storage, calculations, analysis, and presentation of results. Some common statistical software packages include Excel, which supports basic statistical functions, and more specialized packages like Costat, Minitab, SAS, SPSS, and R, which provide advanced statistical analysis capabilities.
SPSS is statistical software used for statistical analysis, data manipulation, and generating tables and graphs summarizing data. It performs basic descriptive statistics as well as advanced statistical techniques like regression, ANOVA, and factor analysis. SPSS allows importing and exporting data from different sources and connecting to online databases. It is robust, but training is typically required to fully utilize its features. SPSS is widely used in research and development, quality control, compliance and validation, clinical trials, and data mining in the pharmaceutical industry.
This document provides an overview of SPSS (Statistical Package for the Social Sciences) from the perspective of Yacar-Yacara Consults, a strategy, research, and data analytics consulting firm. It discusses the meaning of statistical data analysis, the reasons for performing statistical analysis, and the types of data analysis including descriptive, associative, and inferential. It also covers topics like choosing a statistical software package, features of SPSS, preparing a codebook to define variables, and coding responses for data entry in SPSS.
Final spss hands on training (descriptive analysis) may 24th 2013Tin Myo Han
This document provides guidance on conducting valid data analysis in SPSS. It discusses:
1) The importance of solid study planning and design;
2) Ensuring a valid data set through proper data collection, entry, cleaning and manipulation;
3) Selecting appropriate statistical procedures that match the data types and meet statistical assumptions;
4) Correctly interpreting and summarizing results in the context of the research question.
SPSS is statistical analysis software. It can be used to perform a wide range of analyses from basic descriptive statistics to complex analyses like regression. The document discusses SPSS including its interface, how to define and enter data, and common analysis procedures. Key windows in the SPSS interface include the data editor, output navigator, and syntax window. Variables must be strongly defined by type before entering data. SPSS can then be used to analyze the data.
This document provides an overview of key tasks in data preprocessing for knowledge discovery and data mining. It discusses why preprocessing is important, as real-world data is often dirty, noisy, incomplete and inconsistent. The major tasks covered are data cleaning, integration and transformation, reduction, discretization and concept hierarchy generation. Data cleaning involves techniques for handling missing data, noisy data and inconsistent data. Data reduction aims to reduce data volume while preserving analytical results. Methods discussed include binning, clustering, dimensionality reduction and sampling. Discretization converts continuous attributes to discrete intervals.
Dr. Pragyan Paramita Parija's presentation outlines statistics and biostatistics concepts and discusses various statistical software tools used in public health. It introduces statistics and defines biostatistics as applying statistical tools to biological data from medicine and public health. The presentation describes steps for research, applications of statistical software in public health, advantages of using computer software, and commonly used software like Excel, Epi Info, SPSS, SAS, and R. It provides an overview of each software including costs, pros, and cons to help users select the appropriate software.
The document discusses key concepts in quantitative research methods and data analytics covered in a university course. It outlines the course content, which includes topics like data visualization, the normal distribution, and hypothesis testing. It then details the course assessments, which include a mid-term assignment and final coursework report worth 30% and 70% respectively. The final report involves selecting a topic, collecting and analyzing data using R Studio, and reporting the results in a 2000 word paper with sections on introduction, data, results, and conclusion.
The document provides an overview of a 3-day data analytics training program held in Jakarta, Indonesia from April 24-26, 2019. It discusses topics that will be covered including big data overview, data for business analysis, data analytics concepts, and data analytics tools. The training is led by Dr. Ir. John Sihotang and is aimed at management trainees of the company Sucofindo.
Data science combines fields like statistics, programming, and domain expertise to extract meaningful insights from data. It involves preparing, analyzing, and modeling data to discover useful information. Exploratory data analysis is the process of investigating data to understand its characteristics and check assumptions before modeling. There are four types of EDA: univariate non-graphical, univariate graphical, multivariate non-graphical, and multivariate graphical. Python and R are popular tools used for EDA due to their data analysis and visualization capabilities.
This document provides an overview of a data science curriculum for grade 9 students. It covers 4 chapters:
1. Introduction to data - Students will learn about data, information, the DIKW model, how data influences lives, data footprints, and data loss/recovery.
2. Arranging and collecting data - Students will learn about data collection, variables, data sources, big data, questioning data, and univariate/multivariate data.
3. Data visualizations - Students will learn the importance of visualization and how to plot data using histograms, shapes, and single/multivariate plots.
4. Ethics in data science - Students will learn ethical guidelines for data analysis, the need for governance,
The document discusses different types of data sets that can be analyzed in data science. It describes record, graph and network, ordered, spatial, image and multimedia data. It then discusses key concepts related to data sets including data objects, attributes, attribute types (nominal, binary, numeric), and characteristics of data sets like dimensionality and sparsity. The document also lists some repositories for finding publicly available data sets and outlines strategies for getting data like being provided data, downloading data, or scraping data from the web. Finally, it introduces the topic of data visualization.
Exploratory Data Analysis (EDA) is used to analyze datasets and summarize their main characteristics visually. EDA involves data sourcing, cleaning, univariate analysis with visualization to understand single variables, bivariate analysis with visualization to understand relationships between two variables, and deriving new metrics from existing data. EDA is an important first step for understanding data and gaining confidence before building machine learning models. It helps detect errors, anomalies, and map data structures to inform question asking and data manipulation for answering questions.
Data Processing & Explain each term in details.pptxPratikshaSurve4
Data processing involves converting raw data into useful information through various steps. It includes collecting data through surveys or experiments, cleaning and organizing the data, analyzing it using statistical tools or software, interpreting the results, and presenting findings visually through tables, charts and graphs. The goal is to gain insights and knowledge from the data that can help inform decisions. Common data analysis types are descriptive, inferential, exploratory, diagnostic and predictive analysis. Data analysis is important for businesses as it allows for better customer targeting, more accurate decision making, reduced costs, and improved problem solving.
This document provides an overview of exploratory data analysis (EDA). It discusses the key stages of EDA including data requirements, collection, processing, cleaning, exploration, modeling, products, and communication. The stages involve examining available data to discover patterns and relationships. EDA is the first step in data mining projects to understand data without assumptions. The document also outlines the problem definition, data preparation, analysis, and result development and representation steps of EDA. Finally, it discusses different types of data like numeric, categorical, and the importance of understanding data types for analysis.
This document provides a summary of a 4-part training program on using PASW Statistics 17 (SPSS 17) software to perform descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. The agenda covers topics like frequency analysis, correlations, t-tests, ANOVA, importing/exporting data, and more. The goal is to help users answer research questions and test hypotheses using techniques in PASW Statistics.
Exploratory data analysis (EDA) involves analyzing datasets to discover patterns, trends, and relationships. EDA techniques include graphical methods like histograms, box plots, and scatter plots as well as calculating summary statistics. The goal of EDA is to better understand the data structure and relationships between variables through visual and numerical techniques without beginning with a specific hypothesis. EDA is used to generate hypotheses for further confirmatory analysis and to identify outliers, anomalies, and other unusual data characteristics. Lattice graphics and other plotting functions in R can be useful tools for EDA to visualize univariate and bivariate relationships in data.
The document provides an introduction to statistical concepts, explaining that statistics is used to extract useful information from data to help with decision making. It discusses different types of data, variables, methods of data collection and quality, as well as statistical analysis techniques including descriptive statistics, inferential statistics, frequency distributions, graphs and charts. The goal of statistics is to summarize and analyze data to draw conclusions and make informed business decisions.
Data science uses techniques like machine learning and AI to extract meaningful insights from large, complex datasets. It relies on applied mathematics, statistics, and programming to analyze big data. Common data science tools include SAS for statistical analysis, Apache Spark for large-scale processing, BigML for machine learning modeling, Excel for visualization and basic analytics, and programming libraries like TensorFlow, Scikit-learn, and NLTK. These tools help data scientists extract knowledge and make predictions from huge amounts of data.
Epson has developed a toolkit to help users analyze data and make decisions. The toolkit outlines a 5-step process: 1) define the problem and data collection plan, 2) collect and clean the data, 3) interpret the data, 4) develop recommendations, and 5) monitor improvements. It also provides guidance on descriptive statistics, data relationships, grouping data, and identifying trends to analyze problems. The overall goal is to help users turn data into actionable insights and impactful decisions.
This document discusses preparing data for analysis. It covers the need for data exploration including validation, sanitization, and treatment of missing values and outliers. The main steps in statistical data analysis are also presented. Specific techniques discussed include calculating frequency counts and descriptive statistics to understand the distribution and characteristics of variables in a loan data set with 250,000 observations. SAS procedures like Proc Freq, Proc Univariate, and Proc Means are demonstrated for exploring the data.
This document discusses data analysis and presentation. It covers qualitative and quantitative analysis methods, scales of measurement, tools to support analysis, and theoretical frameworks like grounded theory and activity theory. The purpose of data analysis is to obtain useful information by describing, summarizing, comparing, and identifying relationships in data. Presentation of findings should be supported by evidence and make use of graphical representations, rigorous notations, stories, or summaries.
This document provides an introduction to statistics for data science. It discusses why statistics are important for processing and analyzing data to find meaningful trends and insights. Descriptive statistics are used to summarize data through measures like mean, median, and mode for central tendency, and range, variance, and standard deviation for variability. Inferential statistics make inferences about populations based on samples through hypothesis testing and other techniques like t-tests and regression. The document outlines the basic terminology, types, and steps of statistical analysis for data science.
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.
Introducition to Data scinece compiled by huwekineheshete
This document provides an overview of data science and its key components. It discusses that data science uses scientific methods and algorithms to extract knowledge from structured, semi-structured, and unstructured data sources. It also notes that data science involves organizing data, packaging it through visualization and statistics, and delivering insights. The document further outlines the data science lifecycle and workflow, covering understanding the problem, exploring and preprocessing data, developing models, and evaluating results.
This document discusses data analysis and presentation. It covers qualitative and quantitative analysis methods, scales of measurement, tools to support analysis, and theoretical frameworks like grounded theory and activity theory. Graphs, stories, and summaries are presented as ways to communicate findings, which should be supported by the data and not overstate the evidence.
4 work readiness and reflection on wr and polVuTran231
This document discusses key work readiness skills such as diligence, collaboration, adaptability, communication, time management, problem solving, and their definitions. It provides examples of each skill and how they are important for minimizing training, being promoted, adapting to new technologies, and interacting with diverse colleagues. Project-oriented learning helps build these skills by requiring collaboration, time management, and problem solving for complex, real-world simulations. Teachers should incorporate learner-centered activities to further develop students' work readiness.
This document discusses the key characteristics of project-oriented learning (POL). It identifies that POL involves organizing learning around an issue or question that builds on students' knowledge and interests. POL uses authentic, real-world contexts for learning. It also emphasizes learner responsibility, with students accessing and managing their own information to design processes for reaching solutions and adapting their thinking. Finally, assessment in POL focuses on final projects and performances, and it encourages collaborative work between students to complete projects.
The document discusses blended learning and using learning stations to evaluate online resources. It describes having 4 learning stations that focus on different topics: 1) online courses and content, 2) copyright and fair use, 3) blended learning resources, and 4) a blended learning readiness tool. Students would rotate between each station in groups for 30 minutes to explore the topics. The document provides questions to discuss at each station and describes the basics of setting up learning stations, including managing resources, content with multiple components, and simultaneous non-sequential learning.
The document describes the jigsaw technique, a cooperative learning strategy where students are organized into expert groups to learn about different topics and then regroup into jigsaw groups to teach their topic to their peers. The process involves students first learning in expert groups, then regrouping to teach their topic in a jigsaw group, and finally returning to their original expert group to synthesize what was learned. The jigsaw is presented as a learner-centered instructional method that develops teamwork and collaborative learning skills by having students teach each other.
This document discusses individual vs collaborative learning. It presents two tasks - one where participants work alone on squares and another where they work in teams of 3 on rectangles. It finds that collaborative work leads to higher success rates and better performance than individual work. The key elements of collaborative learning are positive interdependence, individual accountability, social skills, face-to-face interaction, and group processing. The document provides guidance for incorporating collaborative activities and assessing their effectiveness in the classroom.
This document provides an overview of Independent Components Analysis (ICA). It discusses how ICA can be used to separate mixed signals, like separating different speakers' voices from recordings of a conversation. The document introduces the ICA problem and assumptions, discusses ambiguities in the ICA solution, and presents the Bell-Sejnowski algorithm for performing ICA using maximum likelihood estimation.
PCA is an algorithm that identifies the subspace where data approximately lies in a way that directly computes the principal eigenvectors of the data's covariance matrix. This identifies directions that maximize the variance of data projections. PCA can be used for dimensionality reduction by projecting high-dimensional data onto the top k principal components/eigenvectors to obtain a lower-dimensional representation. It has applications in data compression, visualization, preprocessing data for machine learning, and filtering noise.
This document summarizes part of a lecture on factor analysis from Andrew Ng's CS229 course. It begins by reviewing maximum likelihood estimation of Gaussian distributions and its issues when the number of data points n is smaller than the dimension d. It then introduces the factor analysis model, which models data x as coming from a latent lower-dimensional variable z through x = μ + Λz + ε, where ε is Gaussian noise. The EM algorithm is derived for estimating the parameters of this model.
The document provides an overview of the EM algorithm and Jensen's inequality. It can be summarized as follows:
1) The EM algorithm is an approach for maximum likelihood estimation involving latent variables. It alternates between estimating the latent variables (E-step) and maximizing the likelihood based on those estimates (M-step).
2) Jensen's inequality states that for a convex function f, the expected value of f(X) is greater than or equal to f of the expected value of X.
3) The EM algorithm derives a lower bound on the log-likelihood using Jensen's inequality, with the latent variable distribution Q chosen to make the bound tight. It then alternates between tightening the bound
The document discusses the Expectation-Maximization (EM) algorithm for estimating the parameters of a mixture of Gaussian distributions from unlabeled training data. The EM algorithm iteratively estimates the parameters by taking turns performing an E-step, where it calculates the probability of each point belonging to each Gaussian component, and an M-step, where it directly maximizes the likelihood using these membership probabilities. The EM algorithm provides a way to estimate the parameters of the mixture model even when the true component memberships of each point are unknown.
The k-means clustering algorithm aims to group data points into k clusters based on their distances from initial cluster centroid points. It works by alternating between assigning each point to its nearest centroid, and updating the centroid locations to be the mean of their assigned points. This process monotonically decreases the distortion score measuring distances from points to centroids, and is guaranteed to converge, though possibly to local optima rather than the global minimum. Running it multiple times can help avoid bad initial results.
This document summarizes lecture notes on reinforcement learning and control from CS229. It introduces reinforcement learning as a framework where algorithms are provided only a reward function rather than explicit labels. It then defines Markov decision processes (MDPs) using states, actions, transition probabilities, rewards, and policies. It describes two algorithms - value iteration and policy iteration - for solving MDPs if the model is known, and discusses learning a model from data when transition probabilities and rewards are unknown. Finally, it notes MDPs can also have continuous rather than finite states.
This summary provides an overview of the key points from the CS229 lecture notes document:
1. The document introduces neural networks and discusses representing simple neural networks as "stacks" of individual neuron units. It uses a housing price prediction example to illustrate this concept.
2. More complex neural networks can have multiple input features that are connected to hidden units, which may learn intermediate representations to predict the output.
3. Vectorization techniques are discussed to efficiently compute the outputs of all neurons in a layer simultaneously, without using slow for loops. Matrix operations allow representing the computations in a way that can leverage optimized linear algebra software.
This document summarizes lecture notes on learning theory from CS229. It discusses the bias-variance tradeoff when fitting models to data. Simply models have high bias but low variance, while complex models can have high variance but low bias. The optimal model balances these factors. It then provides preliminaries on learning theory, defining key concepts like training error, generalization error, and hypothesis classes. For a finite hypothesis class, it shows that training error is a reliable estimate of generalization error, and this implies an upper bound on the generalization error of the model selected by empirical risk minimization.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
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ACEP Magazine edition 4th launched on 05.06.2024Rahul
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
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objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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4. The Data Science Process
Recall the data science process.
• Ask questions
• Data Collection
• Data Exploration
• Data Modeling
• Data Analysis
• Visualization and Presentation of Results
Today we will begin introducing the data collection and data
exploration steps.
3
6. What are data?
“A datum is a single measurement of something on a scale that is
understandable to both the recorder and the reader. Data are
multiple such measurements.”
Claim: everything is (can be) data!
5
7. Where do data come from?
• Internal sources: already collected by or is part of the overall
data collection of you organization.
For example: business-centric data that is available in the
organization data base to record day to day operations;
scientific or experimental data
• Existing External Sources: available in ready to read format
from an outside source for free or for a fee.
For example: public government databases, stock market
data, Yelp reviews
• External Sources Requiring Collection Efforts: available
from external source but acquisition requires special
processing.
For example: data appearing only in print form, or data on
websites
6
8. Where do data come from?
How to get data generated, published or hosted online:
• API (Application Programming Interface): using a
prebuilt set of functions developed by a company to access
their services. Often pay to use. For example: Google Map
API, Facebook API, Twitter API
• RSS (Rich Site Summary): summarizes frequently updated
online content in standard format. Free to read if the site has
one. For example: news-related sites, blogs
• Web scraping: using software, scripts or by-hand extracting
data from what is displayed on a page or what is contained in
the HTML file.
7
9. Web Scraping
• Why do it? Older government or smaller news sites might not
have APIs for accessing data, or publish RSS feeds or have
databases for download. You dont want to pay to use the API
or the database.
• How do you do it? See HW1
• Should you do it?
• You just want to explore: Are you violating their terms of
service? Privacy concerns for website and their clients?
• You want to publish your analysis or product: Do they have an
API or fee that you are bypassing? Are they willing to share
this data? Are you violating their terms of service? Are there
privacy concerns?
8
10. Types of Data
What kind of values are in your data (data types)? Simple or
atomic:
• Numeric: integers, floats
• Boolean: binary or true false values
• Strings: sequence of symbols
9
11. Types of Data
What kind of values are in your data (data types)? Compound,
composed of a bunch of atomic types:
• Date and time: compound value with a specific structure
• Lists: a list is a sequence of values
• Dictionaries: A dictionary is a collection of key-value pairs, a
pair of values x : y where x is usually a string called key
representing the “name” of the value, and y is a value of any
type.
Example: Student record
• First: Kevin
• Last: Rader
• Classes: [CS109A, STAT121A, AC209A, STAT139]
10
12. How is your data represented and stored (data format)?
• Tabular Data: a dataset that is a two-dimensional table,
where each row typically represents a single data record, and
each column represents one type of measurement (csv, tsp,
xlsx etc.).
• Structured Data: each data record is presented in a form of
a, possibly complex and multi-tiered, dictionary (json, xml
etc.)
• Semistructured Data: not all records are represented by the
same set of keys or some data records are not represented
using the key-value pair structure.
11
13. How is your data represented and stored (data format)?
• Textual Data
• Temporal Data
• Geolocation Data
12
14. Tabular Data
In tabular data, we expect each record or observation to represent
a set of measurements of a single object or event. We’ve seen this
already in Lecture 0:
Each type of measurement is called a variable or an attribute of
the data (e.g. seq id, status and duration are variables or
attributes). The number of attributes is called the dimension.
We expect each table to contain a set of records or observations
of the same kind of object or event (e.g. our table above contains
observations of rides/checkouts).
13
15. Types of Data
We’ll see later that its important to distinguish between classes of
variables or attributes based on the type of values they can take on.
• Quantitative variable: is numerical and can be either:
• discrete - a finite number of values are possible in any
bounded interval.
For example: “Number of siblings” is a discrete variable
• continuous - an infinite number of values are possible in any
bounded interval
For example: “Height” is a continuous variable
• Categorical variable: no inherent order among the values For
example: “What kind of pet you have” is a categorical variable
14
16. Common Issues
Common issues with data:
• Missing values: how do we fill in?
• Wrong values: how can we detect and correct?
• Messy format
• Not usable: the data cannot answer the question posed
15
17. Handling Messy Data
The following is a table accounting for the number of produce
deliveries over a weekend.
What are the variables in this dataset?
What object or event are we measuring?
Friday Saturday Sunday
Morning 15 158 10
Afternoon 2 90 20
Evening 55 12 45
What’s the issue? How do we fix it?
16
18. Handling Messy Data
Were measuring individual deliveries; the variables are Time, Day,
Number of Produce.
Friday Saturday Sunday
Morning 15 158 10
Afternoon 2 90 20
Evening 55 12 45
Problem: each column header represents a single value rather than
a variable. Row headers are “hiding” the Day variable. The values
of the variable,“Number of Produce”, is not recorded in a single
column.
17
19. Handling Messy Data
We need to reorganize the information to make explicit the event
were observing and the variables associated to this event.
ID Time Day Number
1 Morning Friday 15
2 Morning Saturday 158
3 Morning Sunday 10
4 Afternoon Friday 2
5 Afternoon Saturday 9
6 Afternoon Sunday 20
7 Evening Friday 55
8 Evening Saturday 12
9 Evening Sunday 45
18
20. More Messiness
What object or event are we measuring?
What are the variables in this dataset?
How do we fix?
19
22. Common causes of messiness are:
• Column headers are values, not variable names
• Variables are stored in both rows and columns
• Multiple variables are stored in one column
• Multiple types of experimental units stored in same table
In general, we want each file to correspond to a dataset, each
column to represent a single variable and each row to represent a
single observation.
We want to tabularize the data. This makes Python happy.
21
24. Basics of Sampling
Population versus sample:
• A population is the entire set of objects or events under
study. Population can be hypothetical “all students” or all
students in this class.
• A sample is a “representative” subset of the objects or events
under study. Needed because its impossible or intractable to
obtain or compute with population data.
Biases in samples:
• Selection bias: some subjects or records are more likely to be
selected
• Volunteer/nonresponse bias: subjects or records who are
not easily available are not represented
Examples? 22
25. Sample mean
The mean of a set of n observations of a variable is denoted ¯x and
is defined as:
¯x =
x1 + x2 + ... + xn
n
=
1
n
n
i=1
xi
The mean describes what a “typical” sample value looks like, or
where is the “center” of the distribution of the data.
Key theme: there is always uncertainty involved when calculating a
sample mean to estimate a population mean.
23
26. Sample median
The median of a set of n number of observations in a sample,
ordered by value, of a variable is is defined by
Median =
x(n+1)/2 if n is odd
xn/2+x(n+1)/2
2 if n is even
Example (already in order):
Ages: 17, 19, 21, 22, 23, 23, 23, 38
Median = (22+23)/2 = 22.5
The median also describes what a typical observation looks like, or
where is the center of the distribution of the sample of
observations.
24
28. Mean vs. Median
The mean is sensitive to extreme values (outliers).
The above distribution is called right-skewed since the mean is
greater than the median.
26
29. Measures of Centrality: computation
How hard (in terms of algorithmic complexity) is it to calculate
• the mean
• the median
27
30. Measures of Centrality: Computation Time
How hard (in terms of algorithmic complexity) is it to calculate
• the mean: at most O(n)
• the median: at least O(n log n)
Note: Practicality of implementation should be considered!
28
31. Regarding Categorical Variables...
For categorical variables, neither mean or median make sense.
Why?
The mode might be a better way to find the most “representative”
value.
29
32. Measures of Spread: Range
The spread of a sample of observations measures how well the
mean or median describes the sample.
One way to measure spread of a sample of observations is via the
range.
Range = Maximum Value - Minimum Value
30
33. Measures of Spread: Variance
The (sample) variance, denoted s2, measures how much on
average the sample values deviate from the mean:
s2
=
1
n − 1
n
i=1
|xi − ¯x|2
Note: the term |xi − ¯x| measures the amount by which each xi
deviates from the mean ¯x. Squaring these deviations means that
s2 is sensitive to extreme values (outliers).
Note: s2 doesnt have the same units as the xi :(
What does a variance of 1,008 mean? Or 0.0001?
31
34. Measures of Spread: Standard Deviation
The (sample) standard deviation, denoted s, is the square root of
the variance
s =
√
s2 =
1
n − 1
n
i=1
|xi − ¯x|2
Note: s does have the same units as the xi . Phew!
32
35. Anscombe’s Data
The following four data sets comprise the Anscombes Quartet; all
four sets of data have identical simple summary statistics.
33
36. Anscombe’s Data 2
Summary statistics clearly don’t tell the story of how they differ.
But a picture can be worth a thousand words:
34
38. More Visualization Motivation
If I tell you that the average score for Homework 0 is: 7.64/15.
What does that suggest?
And what does the graph suggest?
35
39. More Visualization Motivation
Visualizations help us to analyze and explore the data. They help
to:
• Identify hidden patterns and trends
• Formulate/test hypotheses
• Communicate any modeling results
• Present information and ideas succinctly
• Provide evidence and support
• Influence and persuade
• Determine the next step in analysis/modeling
36
40. Principles of Visualizations
Some basic data visualization guidelines from Edward Tufte:
1. Maximize data to ink ratio: show the data
Good Better
37
41. Principles of Visualizations
Some basic data visualization guidelines from Edward Tufte:
1. Maximize data to ink ratio: show the data
2. Dont lie with scale: minimize size of effect in graph
size of effect in data (Lie Factor)
Bad Better
38
42. Principles of Visualizations
Some basic data visualization guidelines from Edward Tufte:
1. Maximize data to ink ratio: show the data
2. Dont lie with scale: minimize size of effect in graph
size of effect in data (Lie Factor)
3. Minimize chart-junk: show data variation, not design variation
Bad Better
39
43. Principles of Visualizations
Some basic data visualization guidelines from Edward Tufte:
1. Maximize data to ink ratio: show the data
2. Dont lie with scale: minimize size of effect in graph
size of effect in data (Lie Factor)
3. Minimize chart-junk: show data variation, not design variation
4. Clear, detailed and thorough labeling
More Tufte details can be found here:
http://www2.cs.uregina.ca/ rbm/cs100/notes/spreadsheets/tufte paper.html
40
44. Types of Visualizations
What do you want your visualization to show about your data?
• Distribution: how a variable or variables in the dataset
distribute over a range of possible values.
• Relationship: how the values of multiple variables in the
dataset relate
• Composition: how the dataset breaks down into subgroups
• Comparison: how trends in multiple variable or datasets
compare
41
45. Histograms to visualize distribution
A histogram is a way to visualize how 1-dimensional data is
distributed across certain values.
Note: Trends in histograms are sensitive to number of bins.
42
46. Scatter plots to visualize relationships
A scatter plot is a way to visualize how multi-dimensional data
are distributed across certain values.
A scatter plot is also a way to visualize the relationship between
two different attributes of multi-dimensional data.
43
47. Pie chart for a categorical variable
A pie chart is a way to visualize the static composition (aka,
distribution) of a variable (or single group).
44
48. Stacked area graph to show trend over time
A stacked area graph is a way to visualize the composition of a
group as it changes over time (or some other quantitative
variable). This shows the relationship of a categorical variable
(AgeGroup) to a quantitative variable (year).
45
49. Multiple histograms
Plotting multiple histograms and/or distribution curves on the
same axes is a way to visualize how different variables compare (or
how a variable differs over specific groups)
46
50. Boxplots
A boxplot is a simplified visualization to compare a quantitative
variable across groups. It highlights the range, quartiles, median
and any outliers present in a data set.
47
51. [Not] Anything is possible!
Often your dataset seem too complex to visualize:
• Data is too high dimensional (how do you plot 100 variables
on the same set of axes?)
• Some variables are categorical (how do you plot values like
Cat or No?)
48
52. More dimensions not always better
When the data is high dimensional, a scatter plot of all data
attributes can be impossible or unhelpful.
49
54. Adding a dimension
For 3D data, color coding a categorical attribute can be effective.
The above visualizes a set of Iris measurements. The variables are:
petal length, sepal length, Iris type (setosa, versicolor, virginica).
51
55. 3D can work
For 3D data, a quantitative attribute can be encoded by size in a
bubble chart.
The above visualizes a set of consumer products. The variables
are: revenue, consumer rating, product type and product cost.
52
57. An example
Use some simple visualizations to explore the following dataset:
53
58. An example
A bar graph showing resistance of each bacteria to each drug:
What do you notice?
54
59. An example
Bar graph showing resistance of each bacteria to each drug
(grouped by Group Number):
Now what do you notice?
55
60. An example
Scatter plot of Drug #1 vs Drug #3 resistance:
Key: the process of data exploration is iterative (visualize for
trends, re-visualize to confirm)!
56
61. Quantifying Relationships
We can see that birth weight is positively correlated with femur
length.
Can we quantify exactly how they are correlated? Can we predict
birthweight based on femur length (or vice versa) through a
statistical model?
57
62. Prediction
We can see that types of iris seem to be distinguished by petal and
sepal lengths.
Can we predict the type of iris given petal and sepal lengths
through some sort of statistical model?
58