Data science and analytics sound the same, but they’re focused on two different areas. Data science is a precursor to data analytics and focuses on structuring the datasets while the later focuses on extracting insights from it. The study of the way we collect, organize, and maintain data is data science and it is what enables analysis of the data. Data science has proven itself by catching critical information and trends that humans miss, especially on a big scale. Also, it significantly speeds up the process of gathering and analyzing data if done correctly. If you apply data science in cybersecurity, you can collect detail on things like your user’s data, network traffic, and security events. For example, just on endpoints like switches alone can collect massive amount of data that will help you predict future network trends which will help you decide whether to take actions like investing in a load balancer using data analytics.
Data science will ultimately streamline data collection on critical areas, where volume of data collected may be too high by writing algorithm to clean, sort, and accurately calculate and produce useful charts. By collecting and analyzing data on a larger scale, especially for time sensitive information, one can make decisions to get ahead of the curve.
Main goal for data analytics is to extract insights and use it to create a strategy. One needs to not only understand what has already happened, but why it happened and what can be done about it in the future.
Understanding data ecosystem and lifecycle is critical. Data ecosystem are hardware and software solutions that are used to collect, store, analyze, and leverage data. Basically, resources that handles everything that processes data. Data lifecycle is path data takes from when it is first generated to being interpreted. 8 steps of data lifecycle are generation, collection, processing, storage, management, analysis, visualization, and interpretation.
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