Sofstack Big Data Analytics Service
Big Data Analytics is “the process of examining large data sets containing a variety of data types – i.e., Big Data – to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.”
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to dealt with.Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
Predictive analytics describe the use of statistics and modeling to determine future performance based on current and historical data.
The use of predictive analytics is a key milestone on your analytics journey — a point of confluence where classical statistical analysis meets the new world of artificial intelligence (AI).
Prescriptive analytics focuses on finding the best course of action in a scenario, given the available data. It’s related to both descriptive analytics and predictive analytics, but emphasizes actionable insights instead of data monitoring.
Prescriptive analytics is the third and final tier in modern, computerized data processing. These three tiers include:
Diagnostic analytics takes descriptive data a step further and provides deeper analysis to answer the question: Why did this happen? Often, diagnostic analysis is referred to as root cause analysis. This includes using processes such as data discovery, data mining, and drill down and drill through.
Descriptive analytics is a field of statistics that focuses on gathering and summarizing raw data to be easily interpreted.
Generally, descriptive analytics concentrates on historical data, providing the context that is vital for understanding information and numbers.
Audio data analysis is about analyzing and understanding audio signals captured by digital devices, with numerous applications in the enterprise, healthcare, productivity, and smart cities.
Data exploration refers to the initial step in data analysis in which data analysts use data visualization and statistical techniques to describe dataset characterizations, such as size, quantity, and accuracy, in order to better understand the nature of the data.
A sample of data will form a distribution, and by far the most well-known distribution is the Gaussian distribution, often called the Normal distribution.
A distribution is simply a collection of data, or scores, on a variable. Usually, these scores are arranged in order from smallest to largest and then they can be presented graphically
Correlation is used to test relationships between quantitative variables or categorical variables. In other words, it’s a measure of how things are related. The study of how variables are correlated is called correlation analysis.
Some examples of data that have a high correlation:
Your caloric intake and your weight.
Your eye color and your relatives’ eye colors.
Survival analysis corresponds to a set of statistical approaches used to investigate the time it takes for an event of interest to occur.
Survival analysis is used in a variety of field such as:
Cancer studies for patients survival time analyses,
Sociology for “event-history analysis”,
and in engineering for “failure-time analysis”.
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