Data Analytics and Data Mining

Data analytics refers to the discipline of using big data science to support business decisions and improve the performance of companies. Data is basically unstructured, often unprocessed human knowledge that can be used to make insightful and sometimes quantitative decisions about business. Data analytics is a systematic process of cleaning, analyzing, transforming and modulating raw data with the objective of finding useful information, inform decisions and support decision-making in all business domains.

Analytics is also known as data visualization. Data visualizations is the practice of representing a set of data sets in a way that makes it easy for users to interpret or explore the data. This form of business skills data analytics is useful for all types of industries. There are a variety of tools and technologies that are used in data visualization to facilitate the analysis, interpretation and presentation of large sets of data.

Data mining is another form of statistical method that analyzes unstructured data. It is a more time-consuming approach to mining data that does not involve programming but rather has a focus on finding those relationships that are most relevant to the users. It is an extremely valuable skill required by managers, executives, and entrepreneurs who need to make quick decisions about strategic partnerships, acquisitions, mergers, and divestitures. The value of data mining can be seen when companies that produce or process certain products must make a decision on whether or not to proceed with a transaction based on the data mined. A manager may need to make a decision based on raw price data without considering the influence of the supplier, the dealer, the distributors, and many other suppliers.

Data mining enables managers to analyze data points in real time without having to wait for the results from complicated mathematical algorithms. It also allows a manager to do more with limited data sets because he can automatically adjust the parameters to refine the results. For instance, a company could use data mining to identify customer segments for a product line. If they are unable to identify a segment and target that segment with other products, they would be wasting a lot of money by not reaching their goals.

Most business people don’t understand the potential analytical and statistical abilities of data analytics. To improve your analytical and statistical skills, you need to understand the complexity of statistical programs like R and Python and how it can help you with your day-to-day business skills and business goals. To better understand R and Python, it is helpful to get some online training for statistical programming and problem solving in R and Python. You can also get help from your data analytics consultant or from the internet.

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