12/05/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Social media analytics is a method of collecting and analyzing data from social media to gain valuable insights into consumer preferences, behaviors, and other trends. It can be used to measure brand awareness, identify target audiences and understand what users think about a company, product or service. It also offers companies the opportunity to determine their competitive advantage by finding out how they compare to similar companies on social media.
12/05/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Semantic analysis is a technique used to understand the meaning of a text or language. It involves breaking down a sentence into its constituent parts and examining the relationships between them. It assumes that the meaning of a sentence emerges from the words and their semantic relationships, rather than from the mere sequence of words. Semantic analysis can also be applied to other types of texts, such as images or videos, to understand the meaning and context.
12/05/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Linear regression is a simple model of statistical analysis used to predict values by establishing a linear relationship between one variable (dependent) and several other variables (independent). It involves fitting a linear equation to the observed data points to minimize the least square deviation between the observed data points and the fitted linear function.
12/05/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Logistic regression is a type of statistical modeling used to predict a binary output of an event. It is commonly used in areas such as disease risk prediction, customer satisfaction, and advertising. It is a type of regression analysis that predicts a binary outcome from a set of variables. It is typically used to determine whether or not an event will occur based on a number of influencing factors.
12/05/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Factor analysis is a statistical technique used to examine the structure of a data set. It is used to reduce the variables in a data set by grouping similar variables together to create a single variable called a factor. This allows for a better understanding of the relationships between variables. It can also be used to determine which variables contribute most to a particular outcome.