"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."
The process of examining and summarizing data sets, and presenting the results in a way that highlights patterns and relationships.
Data collection: The process of gathering and organizing data from various sources for analysis.
Descriptive statistics: Summarizing and describing data through measures such as mean, median, mode, standard deviation, etc.
Inferential statistics: Using data to make inferences or predictions about a larger population.
Hypothesis testing: A method used to test the validity of a claim or hypothesis using data.
Correlation and regression analysis: Analyzing the relationship between two or more variables and predicting future outcomes.
Time-series analysis: Analyzing data that changes over time, such as economic indicators or stock prices.
Probability distributions: Understanding probability distributions and their role in data analysis.
Bayesian statistics: A statistical approach that uses prior knowledge to update probabilities for new information.
Econometric modelling: Building statistical models to explain and predict economic phenomena.
Data visualization: Creating graphical representations of data to facilitate understanding and insights.
Machine learning: Using computer algorithms to analyze and predict patterns in data.
Big Data: Handling and analyzing large and complex datasets that traditional statistical methods can't handle.
Data cleansing: Preparing data for analysis by detecting and correcting errors, inconsistencies, and missing values.
Data integration: Combining data from multiple sources and formats into a unified set for analysis.
Data warehousing: Storing large quantities of data in a centralized location for later use.
Descriptive Statistics: This is used to describe data by summarizing and presenting it visually.
Inferential Statistics: This type of analysis is used to make inferences about a population from a sample.
Regression Analysis: This analysis method is used to ascertain the relationship between variables.
Time Series Analysis: This method is used to determine patterns in data over time.
Panel Data Analysis: This is used to assess data across individuals.
Bayesian Analysis: This type of analysis involves making predictions and updating them as more data comes in.
Principal Component Analysis: This analysis method involves extracting important information from large datasets.
Data Mining: This is a broad term for several types of analysis methods that involve identifying patterns in data.
Machine Learning: This type of analysis involves using algorithms to analyze and develop predictive models.
"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."