This course is designed to teach students essential concepts of computer programming and computational analytics that will be help them in their current research and future industry experiences.
● Overview and introduction to various programming languages and tools including: Python, Matlab, Bash, SQL, TensorFlow, etc.
● Gathering, organizing, and data analysis
● Use of numerical algorithms for analysis (e.g., regression, simulation, optimization, etc.)
● Generation and presentation of results using tables and graphs
Through this course students will be able to learn essential programming concepts for research and professional work. You will also learn how to choose between and use available tools/platforms and analytic methods to complete computing tasks efficiently. We will also cover a breadth of computational techniques such as principal component analysis (PCA), k-nearest neighbors (k-NN), logistic regression, basic optimization and simulation.
The course will include weekly individual assignments.
No prior programming experiences are necessary to take this course. However, we will assume that students have prior knowledge of basic linear algebra and statistics. We welcome students from all divisions and departments; Business school students will have priority when registering.
Adjunct Assistant Professor
Kriste Krstovski is an adjunct assistant professor at the Columbia Business School and an associate research scientist at the Data Science Institute. Previously he was a postdoctoral research scientist working with David Blei at Columbia University and John Lafferty at Yale University.
Kriste received his Ph.D. in computer science from University of Massachusetts Amherst with David Smith. He spent half of...