"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."
This is the process of examining raw data to draw conclusions, identify patterns, and make informed business decisions.
Data mining: The process of discovering patterns in large datasets using mathematical and statistical techniques.
Data preprocessing: The process of cleaning, transforming, and enhancing data to prepare it for analysis.
Statistical analysis: The use of statistical methods to analyze quantitative data and draw conclusions.
Machine learning: The use of algorithms to find patterns in data and predict future outcomes.
Business intelligence: The use of technology to analyze data and provide insights for decision-making.
Data visualization: The creation of charts, graphs, and other visual elements to represent data.
Predictive modeling: The use of statistical and machine learning techniques to forecast future trends.
Spreadsheet analysis: The use of tools such as Microsoft Excel to organize, manipulate, and analyze data.
Big data: The analysis of extremely large datasets, often requiring specialized tools and techniques.
Cloud computing: The use of remote servers to securely store and process data.
Data governance: The management of data quality, security, and privacy.
Data analytics tools: The use of specialized software to facilitate data analysis.
Data warehouse: A centralized repository of data that can be accessed and analyzed by business analysts.
Decision support systems: Computer-based systems that provide decision-making support for business operations.
Time series analysis: The study of trends and patterns over time, often used for forecasting.
Quantitative analysis: The use of mathematical and statistical methods to analyze and interpret data.
A/B testing: The process of comparing two versions of a product or service to determine which performs better.
Marketing analytics: The use of data analysis to optimize marketing strategies and campaigns.
Customer segmentation: The process of dividing customers into groups based on common characteristics or behaviors.
Text analytics: The process of analyzing and extracting insights from unstructured text data, such as social media posts or customer feedback.
Descriptive Analytics: Descriptive analytics refers to the analysis of historical data to identify patterns, trends and insights about past business performance.
Predictive Analytics: Predictive analytics is the analysis of historical data to make predictions about future events or trends. This can be used in forecasting demand for products/services or predicting the likelihood of a customer churn.
Prescriptive Analytics: Prescriptive analytics uses mathematical and statistical algorithms to determine the best course of action for a particular business problem. It involves using the insights generated from predictive analytics to make decisions.
Diagnostic Analytics: Diagnostic analytics is an analysis that helps businesses identify the root cause of a specific problem or issue. This type of data analysis is commonly used to investigate the reasons for an unexpected result, such as a fall in sales, and to determine what led to the outcome.
Text Analytics: Text analytics, also known as text mining, is the process of extracting valuable insights from unstructured data and converting them into a structured format. This type of analysis is commonly used in customer service or survey feedback analysis to understand customer sentiment.
Web Analytics: Web analytics refers to the analysis of website data to determine how visitors interact with a website, which pages are most visited, how long they stay on the website, and more. This information is critical in measuring and optimizing website performance.
Social Media Analytics: Social media analytics is the process of monitoring and analyzing social media activity, such as Twitter, Facebook, Instagram or LinkedIn, to understand the effectiveness of marketing campaigns, increase engagement or identify trends and sentiment.
Financial Analytics: Financial Analytics refers to the use of analytics and statistical techniques to optimize financial decisions and improve performance.
Marketing Analytics: Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI).
Operations Analytics: Operations analytics focuses on optimizing business operations by analyzing and improving various processes within an organization. This can include analyzing supply chain and supply chain management or production-floor optimization.
Healthcare Analytics: Healthcare analytics is the analysis of data collected in healthcare facilities to optimize healthcare services and operations, and improve the quality of patient care. This includes optimizing resource allocation, patient outcomes analysis, and clinical trials.
Retail Analytics: Retail Analytics involves the analysis of data generated from the point of sale (POS) systems, customer loyalty programs, and other sources to improve customer experience, optimize inventory and sales, as well as increase sales and customer engagement.
"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."