Mark Ravina

Historians use a range of analytical skills and our discipline, like the rest of the world, is entering the age of big data. In this class we will explore changes in American society using a massive data source, the hundreds of millions of names in the Social Security database. We will treat changes in baby names as evidence of broader political, social, and cultural change. When and why did the name Adolph drop in popularity? That should be obvious, but which name dropped in popularity the fastest: Adolph, Benito, or Hillary? Which name switched genders the fastest: Ashleigh, Kerry, or Jackie? Have personal names in the US become more or less diverse? Do the answers to these questions vary by state or region? Is Texas more “name diverse” than Wisconsin? Through these questions, we will explore the intersection of history with the interdisciplinary field of data science.

So that we can analyze name trends, this course will introduce the computer language R and review some basic algebra. Math and coding-related questions will include how to measure name diversity and how to calculate it by state and year. We will also explore more conventional historical sources and methods: newspapers, magazines, fiction and non-fiction books, and archival materials. Which politicians, celebrities, or fictional characters might have changed the popularity of a name? Was the name Marion, for example, already trending female when Marion Robert Morrison chose the screen name John Wayne? Did Cassius Clay spark a trend toward Islamic and Afro-centric names when he became Muhammed Ali? How do biographies, autobiographies, and other sources explain trends in names? How do those explanation match our quantitative evidence?

You do not need any special background in mathematics or computer science, just curiosity and a lack of “math anxiety.” If you have a strong math, stats, or coding background, you will learn to apply those skills to real world data. If not, this is a great introduction to data science. For all students, by combining humanistic critical thinking with computational analysis, this course will give you skills applicable to a range of careers.

Overview: This is an introductory survey of modern Japanese history, covering 1850 to 1950. There are no prerequisites. Topics include a brief survey of traditional Japanese society and politics; the fall of the shogunate and the Meiji Restoration; industrialization and economic development; the rise of political parties; militarism and World War II; the American occupation and postwar recovery. Although the emphasis will be on major political events and institutional developments, we will trace social and cultural currents through literature, including dramas and novels.


Readings: The following books are available at the bookstore.

Gordon, Origins of Modern Japan, ISBN 9780190920555. Earlier editions are similar.
Tanizaki Junichirō, Naomi, ISBN: 9780375724749. Other editions are fine.
Cook, Japan at War, ISBN 9781565840393


Grading:
Quizzes (5): 5% each for 25%. These will be short, point-of-fact exams at the beginning of class.

Historians use a range of analytical skills and our discipline, like the rest of the world, is entering the age of big data. In this class we will explore changes in American society using a massive data source, the hundreds of millions of names in the Social Security database. We will treat changes in baby names as evidence of broader political, social, and cultural change. When and why did the name Adolph drop in popularity? That should be obvious, but which name dropped in popularity the fastest: Adolph, Benito, or Hillary? Which name switched genders the fastest: Ashleigh, Kerry, or Jackie? Have personal names in the US become more or less diverse? Do the answers to these questions vary by state or region? Is Texas more “name diverse” than Wisconsin? Through these questions, we will explore the intersection of history with the interdisciplinary field of data science.

So that we can analyze name trends, this course will introduce the computer language R and review some basic algebra. Math and coding-related questions will include how to measure name diversity and how to calculate it by state and year. We will also explore more conventional historical sources and methods: newspapers, magazines, fiction and non-fiction books, and archival materials. Which politicians, celebrities, or fictional characters might have changed the popularity of a name? Was the name Marion, for example, already trending female when Marion Robert Morrison chose the screen name John Wayne? Did Cassius Clay spark a trend toward Islamic and Afro-centric names when he became Muhammed Ali? How do biographies, autobiographies, and other sources explain trends in names? How do those explanation match our quantitative evidence?

You do not need any special background in mathematics or computer science, just curiosity and a lack of “math anxiety.” If you have a strong math, stats, or coding background, you will learn to apply those skills to real world data. If not, this is a great introduction to data science. For all students, by combining humanistic critical thinking with computational analysis, this course will give you skills applicable to a range of careers.

Same as Asian Studies 341K. This course focuses on Japan’s early modern age, from the end of the warring-states period in the 1500s to the stirrings of the industrial revolution in the mid 1800s.  The central focus is on the period of government by the Tokugawa shoguns (1600–1867), a period that reveals the social-ecological dynamics of an island country at a time of chronic resource scarcity and unprecedented development of popular culture.  Topics include the classical and medieval heritage, social and economic change, national isolation and national opening, the Meiji revolution, and the origins of modern nationalism, imperialism, and democracy.   We pay special attention to the subjective experiences of Japanese men and women who lived and created Japan’s distinctive path to modernity.

This class will cover five different aspects of what is commonly called "digital humanities": data visualization (dataviz), descriptive statistics, text mining, web scraping, and mapping (GIS). Dataviz is the craft of depicting quantitative and qualitative data on the page or screen. In descriptive statistics, we will survey the basics of correlation and regression (or OLS). Text mining is the practice of finding and describing patterns and trends in corpora, collections of texts. Web scraping is a technique use to extract large amounts of data from websites. Finally, we will survey the creation of digital maps and basic spatial statistics.

We will examine both theoretical questions, such as how digital methods can change humanistic inquiry, and technical questions of data management processing. The course will focus on the computer language R and the RStudio interface.

You do not need any special background in mathematics or computer science, just curiosity and a lack of “math anxiety.” If you have a strong math, stats, or coding background, you will learn to apply those skills to real world data. If not, this is a great introduction to data science.

Historians use a range of analytical skills and our discipline, like the rest of the world, is entering the age of big data. In this class we will explore changes in American society using a massive data source, the hundreds of millions of names in the Social Security database. We will treat changes in baby names as evidence of broader political, social, and cultural change. When and why did the name Adolph drop in popularity? That should be obvious, but which name dropped in popularity the fastest: Adolph, Benito, or Hillary? Which name switched genders the fastest: Ashleigh, Kerry, or Jackie? Have personal names in the US become more or less diverse? Do the answers to these questions vary by state or region? Is Texas more “name diverse” than Wisconsin? Through these questions, we will explore the intersection of history with the interdisciplinary field of data science.

So that we can analyze name trends, this course will introduce the computer language R and review some basic algebra. Math and coding-related questions will include how to measure name diversity and how to calculate it by state and year. We will also explore more conventional historical sources and methods: newspapers, magazines, fiction and non-fiction books, and archival materials. Which politicians, celebrities, or fictional characters might have changed the popularity of a name? Was the name Marion, for example, already trending female when Marion Robert Morrison chose the screen name John Wayne? Did Cassius Clay spark a trend toward Islamic and Afro-centric names when he became Muhammed Ali? How do biographies, autobiographies, and other sources explain trends in names? How do those explanation match our quantitative evidence?

You do not need any special background in mathematics or computer science, just curiosity and a lack of “math anxiety.” If you have a strong math, stats, or coding background, you will learn to apply those skills to real world data. If not, this is a great introduction to data science. For all students, by combining humanistic critical thinking with computational analysis, this course will give you skills applicable to a range of careers.

Same as Asian Studies 341K. This course focuses on Japan over roughly 150 years, from the 1850s to the early 21st century.  Topics include a brief survey of traditional Japanese society and politics; the fall of the shogunate and the Meiji Restoration of 1868; industrialization and economic development; the rise of consumer culture and mass politics in the 1910s and 1920s; 1930s militarism and World War II; the American occupation and postwar recovery; the rise of Japan Inc. and the long postwar economic boom of the 1960s and 1970s, the “bubble economy” of the 1980s and Japan’s “lost decade(s)” since the 1990s. Although the emphasis will be on major political events and institutional developments, we will trace social and cultural currents through literature, including dramas, novels, and movies.
Required texts:
·      Andrew Gordon, A Modern History of Japan: From Tokugawa Times to the Present, ISBN-13: 978-0199930159
·      Tanizaki Junichirō, Naomi, ISBN-13: 978-0375724749
·      Cook and Cook ed, Japan at War: An Oral History, ISBN-13: 978-1565840393
·      Handouts, reserves, and on-line readings.
Course requirements and grading:
·      two in-class midterm exams (20% each)
·      two take-home mid-term exams (20% each)
·      active in-class discussion work (10%)
·      short final essay (film or novel response) (10%)

This course is an introduction to the history and historiography of empires: what they were, what they are, how they work, and how researchers have explored these questions. We will examine a range of explanations for empire: institutional, geopolitical, economic, and cultural. Readings will include explorations of ancient Roman, Ottoman, Ming, Qing, modern British, French, Japanese, and American empires. Our major questions will include

·      Can a single definition of empire account for polities as diverse as ancient Rome and Qing China? What are the advantages of such general definitions over regional and chronological specificity?

·      What drove empire formation? How should we weigh economic demands, geopolitical rivalries, and domestic pressures?

·      At the beginning of the last century, much of the world lived within a European empire, either in a colony or in the metropole. Was nineteenth-century European colonialism unique, or simply an intense instance of a broader historical process?

·      How does imperialism relate to nationalism and local political identities? Does imperialism efface or create national identities?

·      How do empires shape quotidian lived experience? How do empires transform gender identities and family practices?

Requirements:

Weekly assignments: During the semester, write six short responses (800-1000 words) to the week’s readings. Try not to summarize, or focus on a single work, but to explore a central issue that connects the readings. Think about how and why questions have been framed, and the questions that remain unanswered or not even imagined.
Mock ACLS grant proposal: Begin formulating your research question with mock grant proposal. The proposal should include a title, an 800-character abstract, a 2000-character proposal, and a bibliography.
Research paper (5000 words): Explore a question in your specialty and relate it to the course readings. Should your research topic be considered an instance of imperialism? Use both secondary and primary sources and include a bibliography.