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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.com 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.com Reviews

https://compsocialscience.com

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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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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