This course provides a foundation of the history, concepts, purpose and application of both data science and analytics in a business environment. This includes the methods of data collection, preparation, analysis, visualization, management, security, and preservation of large sets of information. Also covered in the course are the primary methods of analytics, including predictive, prescriptive, and descriptive. The course will examine the various uses of analytics and how these methods identify and leverage competitive advantage in the era of ever-growing information requirements. Through Python programming for beginners and real-world public datasets, some business problems will be analyzed in this course. Tools such as Pandas, Jupyter Notebooks, or Spyder are used to identify and understand relationships in data and visualize information. The course will offer opportunities to create expressive data science projects while utilizing case studies, trends, techniques, and best practices in the data science field.
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