Data science is the study of extracting useful insights from noisy, structured, and unstructured data using scientific methodologies, programming skills, and understanding of mathematics and statistics. In the following sections, we'll go through some of the major fields of data science, which will help you narrow down your options when deciding on a speciality.
Probabilistic and Statistical Data Analysis offers a new viewpoint on the core tenets and main obstacles of modern Data Science. Its clear presentation gives you the grounding you need to comprehend the fundamental concepts behind exchangeable and dynamic network models, network sampling, and network statistics like sparsity and power law, all of which are important in today's data science and machine learning applications. It gives your staff a deeper technical understanding of data science by incorporating basic data insights.
Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets. Given the evolution of data warehousing technology and the growth of big data, adoption of data mining techniques has rapidly accelerated over the last couple of decades, assisting companies by transforming their raw data into useful knowledge. However, despite the fact that that technology continuously evolves to handle data at a large-scale, leaders still face challenges with scalability and automation.
Data Visualization is a prominent part of Data Science that deals with analyzing data using graphical representations and software that involves the usage of programming languages such as Python. Visualization softwares such as Power BI, Excel, Tableau, and Looker use statistics to communicate information clearly and efficiently.
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