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This course introduces fundamental concepts, technologies, and business analytics applications using Big Data. It covers state-of-the-art topics in data mining including Python programming language, data collection, clean, business analysis such as market basket analysis, and data mining models such as predictive analytics, text and graph analytics, etc. It will have a special focus on data mining applications in accounting, as well as the selection of appropriate models and performance evaluation frameworks based on business scenarios.
本课程将介绍使用大数据商业分析的基本概念，技术和应用。内容包括，Python编程语言，数据采集、清理和商业分析（如购物篮分析），数据挖掘模型（如预测分析, 文本和关系网络分析）等话题。课程亦会特别讨论数据挖掘在会计领域的用，以及如何根据商业场景选择合适的模型和评估框架。
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
With this handbook, we will learn how to use:IPython and Jupyter: provide computational environments for data scientists using PythonNumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in PythonPandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in PythonMatplotlib: includes capabilities for a flexible range of data visualizations in PythonScikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms.
