Analytics refers to the process of using data analysis techniques to extract insights and knowledge from data, with the goal of making data-driven decisions. It involves collecting, cleaning, transforming, and modeling data to identify patterns, trends, and relationships that can be used to understand and optimize a system or process.

Analytics can be applied in various fields, such as business, finance, healthcare, sports, and social sciences. There are different types of analytics that can be used depending on the goals and context, such as descriptive analytics, which summarize past performance, predictive analytics, which forecast future trends, and prescriptive analytics, which recommend actions to optimize performance.

Some common techniques used in analytics include:

  1. Data visualization: Using graphs, charts, and other visual aids to represent data in a way that is easy to understand and interpret.

  2. Statistical analysis: Using statistical methods to identify patterns and relationships in data, such as correlation, regression, and hypothesis testing.

  3. Machine learning: Using algorithms and models to automatically learn from data and make predictions or recommendations based on that learning.

  4. Text mining: Using natural language processing techniques to extract insights from text data, such as sentiment analysis, topic modeling, and text classification.

Overall, analytics plays a critical role in helping organizations and individuals make data-driven decisions, improve performance, and gain competitive advantages.

What is self-service analytics?
In this video, Nick Petrosyan explains what is self-service analytics in a process manufacturing context.
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TrendMiner develops and delivers advanced analytics solutions for the process industry, helping companies with their digital transformation so they can optimize their production processes, increase plant productivity and improve the overall equipment effectiveness.
Getting started with self-service analytics and TrendMiner
In this video, Nick Petrosyan explains what is the best way to get started with self-service analytics in a process manufacturing context
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