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Show Results For
- All HBS Web
(1,720)
- People (9)
- News (316)
- Research (1,050)
- Events (15)
- Multimedia (10)
- Faculty Publications (863)
- Sep 23 2015
- Interview
Interview with Faculty Chair Frank Cespedes
- 06 Oct 2011
- What Do You Think?
How Will the ‘Moneyball Generation’ Influence Management?
Summing Up Should "Moneyball Analytics" Play a Greater Role in Preparation for Management? There was general agreement among respondents to this month's column that we will see a growing emphasis on analytics among managers as... View Details
Keywords: by James Heskett
- November 1996
- Article
Localized Autocorrelation Diagnostic Statistic for Sociological Models: Times-series, Network, and Spatial Datasets
By: C. I. Nass and Y. Moon
Nass, C. I., and Y. Moon. "Localized Autocorrelation Diagnostic Statistic for Sociological Models: Times-series, Network, and Spatial Datasets." Sociological Methods & Research 25, no. 2 (November 1996): 223–247.
- 08 Aug 2023
- Research & Ideas
The Rise of Employee Analytics: Productivity Dream or Micromanagement Nightmare?
hiring and productivity, says Jeffrey T. Polzer, the UPS Foundation Professor of Human Resource Management at Harvard Business School. His recent paper probes how organizational researchers should study people analytics practices, which... View Details
Keywords: by Ben Rand
- June 2018 (Revised January 2019)
- Background Note
Visualizing Data & Effective Communication
By: Srikant M. Datar and Caitlin N. Bowler
This note explores three specific ways an analyst can use visualization. Section 1 considers visualization to explore data. Section 2 discusses visualization as a tool for developing a deeper understanding of trends and phenomena encoded in the data. Section 3... View Details
Keywords: Data Visualization; Graphical Guidelines; Charts; Analytics and Data Science; Communication
Datar, Srikant M., and Caitlin N. Bowler. "Visualizing Data & Effective Communication." Harvard Business School Background Note 118-114, June 2018. (Revised January 2019.)
- November 28, 2017
- Editorial
Active Investing v.2.0
By: Gabriel Karageorgiou and George Serafeim
Keywords: Investment; Investing; Technology; Big Data; Quantitative Analysis; ESG; ESG (Environmental, Social, Governance) Performance; Sustainability; Analytics and Data Science
Karageorgiou, Gabriel, and George Serafeim. "Active Investing v.2.0." Pensions & Investments (online) (November 28, 2017).
- Feb 21 2017
- Testimonial
Reaching the Next Level
- Profile
Maliha Khan
Why was getting a business education important to you? I saw an MBA as important for two reasons: I wanted key analytical and quantitative skills that would help me make decisions and give me more confidence in my judgment; and secondly,... View Details
- 18 Oct 2016
- News
China, artificial intelligence, and Jim Breyer
partner level,” he said. “They are brilliant, work 100 hours a week and are intensely competitive.” When considering in which startups to invest capital, Breyer said if the company does not include an artificial intelligence data analysis component he will not consider... View Details
Keywords: Jennifer Myers
- March 2022 (Revised January 2025)
- Technical Note
Exploratory Data Analysis
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.... View Details
Keywords: Data Analysis; Data Science; Statistics; Data Visualization; Exploratory Data Analysis; Analytics and Data Science; Analysis
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Exploratory Data Analysis." Harvard Business School Technical Note 622-098, March 2022. (Revised January 2025.)
- 2020
- Working Paper
A General Theory of Identification
By: Iavor Bojinov and Guillaume Basse
What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree
on a definition in the context of parametric statistical models — roughly, a parameter θ in a model
P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective.... View Details
Bojinov, Iavor, and Guillaume Basse. "A General Theory of Identification." Harvard Business School Working Paper, No. 20-086, February 2020.
- November 1998
- Article
Modeling Large Data Sets in Marketing
By: Sridhar Balasubramanian, Sunil Gupta, Wagner Kamakura and Michel Wedel
Balasubramanian, Sridhar, Sunil Gupta, Wagner Kamakura, and Michel Wedel. "Modeling Large Data Sets in Marketing." Special Issue on Large Data Sets in Business Economics. Statistica Neerlandica 52, no. 3 (November 1998).
- January–February 2025
- Article
The Double-Edged Sword of Exemplar Similarity
By: Majid Majzoubi, Eric Zhao, Tiona Zuzul and Greg Fisher
We investigate how a firm’s positioning relative to category exemplars shapes security analysts’ evaluations. Using a two-stage model of evaluation (initial screening and subsequent assessment), we propose that exemplar similarity enhances a firm’s recognizability and... View Details
Majzoubi, Majid, Eric Zhao, Tiona Zuzul, and Greg Fisher. "The Double-Edged Sword of Exemplar Similarity." Organization Science 36, no. 1 (January–February 2025): 121–144.
- 2023
- Working Paper
Design-Based Inference for Multi-arm Bandits
By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available... View Details
Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
- 2023
- Article
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models... View Details
Meyer, Anna P., Dan Ley, Suraj Srinivas, and Himabindu Lakkaraju. "On Minimizing the Impact of Dataset Shifts on Actionable Explanations." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 39th (2023): 1434–1444.
- 2022
- Article
OpenXAI: Towards a Transparent Evaluation of Model Explanations
By: Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik and Himabindu Lakkaraju
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible opensource framework for evaluating and... View Details
Agarwal, Chirag, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju. "OpenXAI: Towards a Transparent Evaluation of Model Explanations." Advances in Neural Information Processing Systems (NeurIPS) (2022).
- March 2022 (Revised January 2025)
- Technical Note
Linear Regression
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... View Details
Keywords: Data Science; Linear Regression; Mathematical Modeling; Mathematical Methods; Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Linear Regression." Harvard Business School Technical Note 622-100, March 2022. (Revised January 2025.)
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details
Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
- 09 Dec 2015
- Research Event
How Do You Predict Demand and Set Prices For Products Never Sold Before?
explained that the world of business analytics includes descriptive analytics (analyzing what has happened), predictive analytics (analyzing data to figure out what will... View Details
- 01 Mar 2013
- News
Faculty Books
Enterprise Analytics: Optimize Performance, Process, and Decisions through Big Data edited by Thomas Davenport (FT Press) This book, a collection of research papers from the International Institute for Analytics, addresses a wide variety of topics in managing business... View Details