Data science is a multidisciplinary field aiming to extract new insights and knowledge from huge amounts of data available in different forms. Technological advances rapidly increase the number and variety of data science tools and techniques. As new forms of data become available in substantial amounts and from different resources over time, demand for data science tools and techniques has increased substantially among social scientists. This course aims to introduce data science to social scientists and to the ones who want to learn how data science techniques are implemented in social sciences. Causal inference will be at the center. Basic programming concepts will be introduced. Text analytics, big data, and machine learning concepts will be covered.
Upon successful completion of this course, students will be able to:
1. Understand how data science applications are used and implemented in social sciences,
2. Learn the basic data science methods commonly used in social sciences,
3. Perform basic computer programming to address data science problems,
4. Apply exploratory data analysis and data visualization,
5. Explain causal inference in social sciences; sharply distinguish between causation and correlation,
6. Identify the basic concepts/methods of machine learning, big data, and data analytics.