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Data preparation is a necessary pre-processing step in analytics. It aims to clean the data from various resources and improve its quality for better productivity. This process includes many tasks such as fusion, cleaning, and augmentation of data. This teaching note will focus on illustrating data cleaning using the programming language Python, with all codes completed in Google Laboratory. Different solutions using the programming languages R and Microsoft Excel are also provided. To effectively illustrate the data preparation process, the relatively simple dataset Bengaluru House Prices is used. This is a relatively messy dataset with a few variables and many records, making it ideal for explaining data preparation steps.

Most managers in an international business are likely to be involved with international joint ventures (IJVs) during their career. IJVs have a greater chance of success if the businesses involved have good advisers on their teams. However, it can be a challenge to know what advisers to hire and how best to utilize their expertise. Drawing on their extensive career experience, the authors recommend best practices for hiring and working with international joint venture advisers based on three perspectives: legal, business, and academic. The possible roles of advisers, value versus cost of advisers, and what to look for and avoid when hiring advisers are outlined and framed within specific business examples.

Machine-learning (ML) models have become a common tool used across a multitude of industries to help people make decisions. As these models have increased in predictive power, many have also grown in complexity. The pursuit to improve the accuracy of predictions has diminished the interpretability of many models-leaving users with little understanding of the model's behavior or trust in its predictions. The field of eXplainable artificial intelligence (XAI) seeks to encourage the development of interpretable models. Google Cloud Platform offers two Explainable AI functions in BigQuery ML that allow users to examine the attribution of model features, which aids in model behavior verification and bias recognition. One of the functions available in BigQuery provides a global perspective on the features used to train the model, while the second function examines local feature attribution associated with individual predictions in more detail. This note offers an overview of Explainable AI in BigQuery ML, using as an example a (fictional) realtor's linear regression model that predicted a home's latest sale price based on predictor variables such as the total tax assessment from the year of the last sale, the square footage of the house, the number of bedrooms, the number of bathrooms, and whether the condition of the home is below average. After training the linear model, the feature attribution can be studied from a global and local perspective in BigQuery.

Google Cloud Platform (GCP) offers a popular cloud computing resource for writing Structured Query Language (SQL) queries. The resource is called BigQuery. This note provides step-by-step information about accessing BigQuery and using it to upload and explore data. It also includes a quick-start guide.

Tableau is a powerful visualization software that supports the data analysis process. The program allows users to create custom visualizations from datasets of all sizes and types using a simple drag-and-drop interface. This note introduces new users to Tableau by providing guidance on connecting to data, exploring the interface, and creating a number of common visualizations.

Google Cloud Platform (GCP) offers Cloud Storage, a popular cloud computing resource for data storage. This note provides information about uploading data to Cloud Storage, creating BigQuery tables from data found in Cloud Storage, previewing files in Cloud Storage, and sharing Cloud Storage buckets.

This module note provides an overview of exploratory data analysis for an introduction to data science course. It begins by defining the term "data", and then describes the different types of data that companies work with (structured v. unstructured, categorical v. numeric, etc.). Next, the note describes the basic summary statistics that firms use to track key business outcomes. Finally, the note provides an overview of different visualizations. An appendix is provided, which includes the R code for creating all of the figures and visualizations shown in the note.

This note provides an overview of statistical inference for an introductory data science course. First, the note discusses samples and populations. Next the note describes how to calculate confidence intervals for means and proportions. Then it walks through the logic of hypothesis testing and the interpretation of p-values (in the context of two-sample hypothesis testing for means and proportions). The appendix of the note contains R code for all of these topics.

This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares , and how to interpret the r-squared and model coefficient values of a simple linear regression model. Next, the note describes how the interpretation of a model coefficient changes when there are multiple independent variables in the model. Finally, the note explains how to interpret the coefficients on dummy variables in a regression model. The appendix includes R code for implementing all of these topics.

This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional statistical modeling and machine learning. Next, the note covers two models used for classification, logistic regression and decision trees. After introducing these two models, the note explains how train, validation, and holdout sets (and k-fold cross validation) are used to tune and evaluate different models. Finally, the note concludes with a discussion of different performance metrics (ROC cruves, confusion matrices, log loss) that are used to evaluate classification models.

This background Note introduces the implications of climate change (global warming) for American cities. In the U.S., partisan political divides and unaddressed economic and racial disparities in climate vulnerabilities can inhibit action. The two main fronts for action are mitigation (reducing emissions) and adaptation (preparing for change by building resilience). The Note reviews main sources of emissions - energy, buildings, transportation, waste - and examples of mitigation actions, showing differential costs and impact. Adaptation actions depend on geographic location and specific climate hazards, with resilience of infrastructure and institutions a major factor. The most risk-prone are often the least prepared. Crises tend to propel action, and foundations have facilitated adaptation capacity, but leadership challenges require cross-sector goal-setting and collaboration.

This Note introduces a module of cases used at Harvard Business School to teach fundamental concepts about navigating nascent industries and product categories. It elaborates a set of 'innovation tensions' that managers must address in these domains. In connecting the cases to scholarly research on nascent industries (including research by the author), the module builds out a structured process for identifying the tensions and proposes product-strategy sequences and operational processes for resolving them effectively. It concludes by describing core ideas an instructor can teach via the module's six case studies.

A new entrepreneur planning to start a new exports and/or imports business is confronted with the issue of managing and mitigating various risks in international trade. Risks are inherent to any business, be it domestic or international. However, when business becomes international, the dimensions, complexities, and quantum of risks get amplified. This technical note discusses the nuances of various risks faced by the exporters, such as; credit risk, country risk, and currency risk faced by the exporters, popularly known as the three C’s of international trading risks, and the strategies of mitigating those risks. The note also discusses the various risks encountered by the importers, such as; currency risk, supplier risk, and product risk and the methods of managing such risks.

This note provides an update on the space financing state of play in 2021.

Most managers in an international business are likely to be involved with international joint ventures (IJVs) during their career. IJVs have a greater chance of success if the businesses involved have good advisers on their teams. However, it can be a challenge to know what advisers to hire and how best to utilize their expertise. Drawing on their extensive career experience, the authors recommend best practices for hiring and working with international joint venture advisers based on three perspectives: legal, business, and academic. The possible roles of advisers, value versus cost of advisers, and what to look for and avoid when hiring advisers are outlined and framed within specific business examples.