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Computational Social Science | Spring 2013, Department of Applied Mathematics, Columbia University

Spring 2013, Department of Applied Mathematics, Columbia University. Department: Applied Mathematics, Columbia University. Time: Fridays, 1:00-3:40pm. Location: 337 Seeley W. Mudd. Linear algebra (APMA E3101 or equivalent), Probability and Statistics (SIEO W4150 or equivalent). Previous exposure to a high-level programming language such as Python, MATLAB (e.g. COMS W1005), Ruby, Perl, R or similar is recommended. Lectures 11 & 12: Regression. Lecture 10: Experimental Design. Lecture 9: Data Wrangling.

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Computational Social Science | Spring 2013, Department of Applied Mathematics, Columbia University | compsocialscience.org Reviews

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Spring 2013, Department of Applied Mathematics, Columbia University. Department: Applied Mathematics, Columbia University. Time: Fridays, 1:00-3:40pm. Location: 337 Seeley W. Mudd. Linear algebra (APMA E3101 or equivalent), Probability and Statistics (SIEO W4150 or equivalent). Previous exposure to a high-level programming language such as Python, MATLAB (e.g. COMS W1005), Ruby, Perl, R or similar is recommended. Lectures 11 & 12: Regression. Lecture 10: Experimental Design. Lecture 9: Data Wrangling.

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Computational Social Science | Spring 2013, Department of Applied Mathematics, Columbia University | compsocialscience.org Reviews

https://compsocialscience.org

Spring 2013, Department of Applied Mathematics, Columbia University. Department: Applied Mathematics, Columbia University. Time: Fridays, 1:00-3:40pm. Location: 337 Seeley W. Mudd. Linear algebra (APMA E3101 or equivalent), Probability and Statistics (SIEO W4150 or equivalent). Previous exposure to a high-level programming language such as Python, MATLAB (e.g. COMS W1005), Ruby, Perl, R or similar is recommended. Lectures 11 & 12: Regression. Lecture 10: Experimental Design. Lecture 9: Data Wrangling.

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compsocialscience.org

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Homework 2 | Computational Social Science

http://compsocialscience.org/2013/04/homework-2

Spring 2013, Department of Applied Mathematics, Columbia University. The first problem is a simple word count exercise over the Wikipedia corpus, the second examines Wikipedia page popularity, and the third explores tie strength between co-authors. See Amazon’s getting started videos. From lectures 5 and 6 for more information on Pig, EC2, and Elastic Mapreduce. Use the template solution files. To test and debug Pig scripts on your local machine. Upload your locally tested Pig script to S3:. April 8, 2013.

2

Lecture 3: Counting at Scale, Part I | Computational Social Science

http://compsocialscience.org/2013/02/counting-at-scale-part-i

Spring 2013, Department of Applied Mathematics, Columbia University. Lecture 3: Counting at Scale, Part I. We saw that many social science questions can be answered by simply counting relevant quantities in the data. For example looking at the median rank of movies viewed by an individual allowed us to compute the persons’s eccentricity, a value that we then used to gauge the impact of larger catalogs on people’s happiness. First introduced by Dean. In the reduce step, the programmer specifies what to do...

3

homework | Computational Social Science

http://compsocialscience.org/category/homework

Spring 2013, Department of Applied Mathematics, Columbia University. The first problem is a simple word count exercise over the Wikipedia corpus, the second examines Wikipedia page popularity, and the third explores tie strength between co-authors. See Amazon’s getting started videos. From lectures 5 and 6 for more information on Pig, EC2, and Elastic Mapreduce. Use the template solution files. To test and debug Pig scripts on your local machine. Upload your locally tested Pig script to S3:. April 8, 2013.

4

lectures | Computational Social Science

http://compsocialscience.org/category/lectures

Spring 2013, Department of Applied Mathematics, Columbia University. Lectures 11 & 12: Regression. These lectures explored both the practice of and theory behind applied regression analysis. As stated by Cook and Weisberg. Regression analysis aims “to understand, as far as possible with the available data, how the conditional distribution of the response varies across subpopulations determined by the possible values of the predictor(s).” This includes everything from simple descriptive st...Regression, p...

5

Lecture 2: An Introduction to Counting | Computational Social Science

http://compsocialscience.org/2013/02/lecture-2-an-introduction-to-counting

Spring 2013, Department of Applied Mathematics, Columbia University. Lecture 2: An Introduction to Counting. This week marked the first of three lectures on counting. While this may seem like a lot of time to devote to a very simple concept, we’ll see that counting at scale is surprisingly more powerful and unfortunately somewhat more difficult than one might think. Take, for instance, the questions we discussed around the long tail. For instance, to compute the average rating for each movie in the Netfl...

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jakehofman.com

jake hofman :: teaching

http://www.jakehofman.com/teaching

I co-taught modeling social data. Apam e4990) in columbia's applied mathematics department in the spring of 2015. I co-taught computational social science. Apam e4990) in columbia's applied mathematics department in the spring of 2013. I taught data-driven modeling. Apam e4990) in columbia's applied mathematics department in the spring of 2012, a course on data mining and applied machine learning. see the current course site. I originally designed and taught this class in the fall of 2009. After survivin...

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A Data Head's Diary | ADHD: The unstructured musings of an aspiring data scientist in grad school | Page 2

https://adataheadsdiary.wordpress.com/page/2

A Data Head's Diary. ADHD: The unstructured musings of an aspiring data scientist in grad school. Newer posts →. The Evolving Role of the Data Scientist. February 28, 2013. By A Data Head. This is a good article. About the changing role of data scientists and data science in the enterprise world. February 27, 2013. By A Data Head. A while back James Kobielus. Wrote the article, Data Scientist: Consider the Curriculum. Linear and logistic regression. Bayesian and Monte Carlo Statistics. February 24, 2013.

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Computational Social Science | Spring 2013, Department of Applied Mathematics, Columbia University

Spring 2013, Department of Applied Mathematics, Columbia University. Department: Applied Mathematics, Columbia University. Time: Fridays, 1:00-3:40pm. Location: 337 Seeley W. Mudd. Linear algebra (APMA E3101 or equivalent), Probability and Statistics (SIEO W4150 or equivalent). Previous exposure to a high-level programming language such as Python, MATLAB (e.g. COMS W1005), Ruby, Perl, R or similar is recommended. Lectures 11 & 12: Regression. Lecture 10: Experimental Design. Lecture 9: Data Wrangling.

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Computational Social Science | Spring 2013, Department of Applied Mathematics, Columbia University

Spring 2013, Department of Applied Mathematics, Columbia University. Department: Applied Mathematics, Columbia University. Time: Fridays, 1:00-3:40pm. Location: 337 Seeley W. Mudd. Linear algebra (APMA E3101 or equivalent), Probability and Statistics (SIEO W4150 or equivalent). Previous exposure to a high-level programming language such as Python, MATLAB (e.g. COMS W1005), Ruby, Perl, R or similar is recommended. Lectures 11 & 12: Regression. Lecture 10: Experimental Design. Lecture 9: Data Wrangling.

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