"Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making."
Explains how to analyze data collected in program evaluation, including quantitative and qualitative analysis methods, and how to interpret and present results.
Statistics: Statistics is a fundamental topic for data analysis as it is the study of collection, analysis, interpretation, presentation, and organization of data. It covers descriptive and inferential statistics, probability theory, hypothesis testing, etc.
Data Visualization: Data visualization deals with the graphical representation of data and is important in Program Evaluation in Public Administration to present analyses of data to decision-makers.
Regression Analysis: Regression analysis is a statistical technique that helps in estimating the relationship between a dependent variable and one or more independent variables.
Sampling Techniques: Sampling techniques are used while collecting data and help in selecting a representative sample from a population.
Qualitative Research: Qualitative research helps in developing an understanding of the attitudes, behaviors, and motivations of individuals.
Quantitative Research: Quantitative research involves numerical data and statistical analyses.
Experimental Design: Experimental design refers to the structured process of planning and executing experiments in a way that ensures accurate and reproducible results.
Survey Design: Survey design involves designing surveys to gather data from a population.
Causal Inference: Causal inference helps in understanding the relationship between cause and effect.
Machine Learning: Machine learning techniques can be used to automate the data analysis process.
Data Cleaning: Data cleaning refers to the process of identifying and correcting errors and inconsistencies in data.
Data Wrangling: Data wrangling involves the process of transforming and restructuring data to make it suitable for analysis.
Database Management: Database management is essential for managing large datasets.
Data Mining: Data mining involves the extraction of valuable insights and knowledge from large datasets.
Time Series Analysis: Time series analysis is the process of analyzing time-dependent data to identify trends and patterns.
Data Ethics: Data ethics are the principles that govern the responsible use of data.
Data Governance: Data governance involves managing and ensuring the quality, availability, usability, integrity, and security of data.
Data Security: Data security ensures the confidentiality, integrity, and availability of data.
Text Mining: Text mining involves the extraction and analysis of information from unstructured text data.
Analytics Tools: Analytics tools are software applications used for data analysis, such as R, Python, and SAS.
Descriptive data analysis: Refers to the identification, summarization, and interpretation of the key characteristics of a data set, such as mean, median, mode, range, and standard deviation.
Inferential data analysis: Refers to the process of drawing conclusions about a population from a sample. This may involve hypothesis testing and confidence interval estimation.
Exploratory data analysis: Involves visualizing and summarizing data to detect patterns or trends that can form the basis of research questions or hypotheses.
Predictive data analysis: Involves using statistical models and algorithms to make predictions about future outcomes based on historical data.
Time series data analysis: Involves analyzing data collected over time to identify trends or patterns, such as seasonality, trends, and cycles.
Spatial data analysis: Refers to the application of statistical techniques to analyze data that has a spatial component, such as location data from GPS systems or satellite imagery.
Text data analysis: Involves analyzing large volumes of text data, such as reviews, social media posts, and customer feedback to extract insights and trends.
Network data analysis: Involves analyzing complex data networks and relationships, such as social networks, to gain insights into patterns of behavior.
Multivariate data analysis: Refers to the analysis of data sets with multiple variables, such as regression analysis, factor analysis, and cluster analysis.
Qualitative data analysis: Involves the analysis of data obtained through qualitative methods, such as interviews, focus groups, and ethnography, to identify themes and patterns.
"In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively."
"Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains."
"Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes."
"Business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information."
"In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA)."
"EDA focuses on discovering new features in the data."
"CDA focuses on confirming or falsifying existing hypotheses."
"Predictive analytics focuses on the application of statistical models for predictive forecasting or classification."
"Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources."
"Data integration is a precursor to data analysis."
"All of the above are varieties of data analysis."
"Data analysis is closely linked to data visualization."
"Data analysis plays a role in making decisions more scientific and helping businesses operate more effectively."
"Data mining focuses on statistical modeling and knowledge discovery for predictive purposes."
"Business intelligence focuses mainly on business information."
"Data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA)."
"EDA focuses on discovering new features in the data."
"CDA focuses on confirming or falsifying existing hypotheses."
"Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources."