Text mining algorithms – infographic

text mining algorithms list

Text mining algorithms are nothing more but specific data mining algorithms in the domain of natural language text.

The text can be any type of content – postings on social media, email, business word documents, web content, articles, news, blog posts, and other types of unstructured data.

Algorithms for text analytics incorporate a variety of techniques such as text classification, categorization, and clustering.

All of them aim to uncover hidden relationships, trends, and patterns which are a solid base for business decision-making.

In this infographic:

  • A list of the top text meaning algorithms.
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10 key types of data analysis methods – Infographic

10 Key Types of Data Analysis Methods Infographic

Our modern information age leads to a dynamic and extremely high growth of the data mining world.

No doubt, that it requires adequate and effective different types of data analysis methods, techniques, and tools that can respond to constantly increasing business research needs.

In fact, data mining does not have its own methods of data analysis. It uses the methodologies and techniques of other related areas of science.

Among the methods used in data analysis are: mathematical and statistical techniques, methods based on machine learning and graphical methods.

In this infographic:

  • A list of the most known classic and modern types of data analysis methods and models.
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Linear regression models – Infographic

linear regression models infographic

Linear regression models are the most basic types of statistical techniques and widely used predictive analysis.

They show a relationship between two variables with a linear algorithm and equation.

Linear regression modeling and formula have a range of applications in the business.

For example, they are used to evaluate business trends and make forecasts and estimates.

They can also be used to analyze the result of price changes on the consumer behavior. It can be given numerous examples.

In this infographic:

  • Simple linear regression model
  • Multiple linear regression modeling
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The two main data mining objectives – Infographic

the two main data mining objectives infographic

Nowadays, the majority of the corporations possess the data they need for business intelligence.

However how they use and analyze these data is what differs the winners from the losers, the successful ones from the failed businesses.

That is why the big companies and data intelligence specialists struggle to find unexpected patterns, trends, correlations, and clustering in the data.

And this is where the core goals/objectives of data mining and business intelligence lie.

In this infographic:

  • What is data mining?
  • What are the two main objectives associated with data mining? Explained.
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The key data quality metrics – Infographic

Data Quality Metrics Infographic

Nowadays data quality metrics are crucial for the assessment of the overall health of a business. Why?

Simply because bad or low-quality data can dramatically impact productivity and overall ROI of an organization.

If you are working in the business intelligence and data analysis world, you should know how to measure data quality, which dimensions to use and what are the standards and best practices for the measurement.

In this infographic:

  • What is data quality?
  • Key data quality metrics/dimensions to assess and assure high data quality.
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Types of statistical analysis – Infographic

types of statistical analysis infographic

Statistics science is used widely in so many areas such as market research, business intelligence, financial and data analysis and many other areas.

Why? Simply because statistics is a core basis for millions of business decisions made every day.

The two main types of statistical analysis and methodologies are descriptive and inferential.

However, there are other types that also deal with many aspects of data including data collection, prediction, and planning.

In this infographic:

  • The different types of statistics
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35 Key Data Scientist Qualifications And Skills - A Whitepaper