News / Blog: #analysis

What is causal analysis?

Causal analysis is a method used to investigate what factors led to certain outcomes. It is a method used to identify possible causes of a particular outcome and then examine the relationship between the causes and the outcome. It is often used in research to examine relationships between variables to understand why certain events or outcomes occur.

What is a forecasting tool?

A forecasting tool is a program or system used to predict future events or outcomes. It is commonly used in various industries, including finance, marketing, business, and weather. Forecasting tools can use different types of methods and algorithms to analyze data and make predictions. These include simple statistical analysis, complex machine learning algorithms, regression analysis, and data mining techniques.

What is Data Engineering?

Data Engineering is a branch of computer science that deals with the development, design, implementation and operation of databases, systems and applications for data processing. It includes the use of technologies such as databases, database queries, database administration, data analysis, data warehousing, data mining, business intelligence, and Big Data. It is a very important part of data science and machine learning.

What is data preparation?

Data preparation is a process in which data is prepared for various purposes. In this process, the data is sorted, structured, analyzed and prepared in order to make it usable for specific applications. This can be achieved through various processes such as databases, data aggregation, data manipulation, data analysis and data visualization. Data preparation is an important part of data warehouse design and database management technologies.

What is data enrichment?

Data enrichment is a process of enhancing and improving data by adding additional information to bring the data into a more structured or informed form. The purpose of data enrichment is to give users more insight into their data, allowing them to make better decisions and perform better analysis. Examples of data enrichment can include mapping geolocation data, demographic information, customer histories, or other external sources to existing data.