Empirical studies in economics and business analysis is entering a new era of “Big Data”. A diverse range of new unstructured/unformatted data from texts (e.g. online discussion, social media post, and product description, etc.), maps (e.g. transportation network, satellite images, and digitalized map, etc.), and networks (e.g. tweet and retweet network, bilateral trade, and citation network, etc.) becomes increasingly accessible from web-scraping and other sources. How can we take advantage of these new data sources and improve our understanding of the economy and the business world? This course introduces various regression and machine learning techniques to process, simplify and analyse these spatial, textual and network data for business and economic analytics. Real life data analytic examples will be used to walk students through the intuitions behind those statistical techniques, as well as demonstrate step by step the programming language (either R or Python) applied for each data task.