an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation.
A data scientist’s skill set comes from various expertise, irrespective of the background one comes from. However, the data science foundation is built on four pillars; i.e., Statistics, Computer Science/Software Programming (technical skills), Business Acumen, and Communication Skills. Each of these skills plays a major role in a data science professional.
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