Data science topics span a large variety of disciplines and require a collection of skills. This course is intended to cover data science's fundamental principles and techniques, emphasizing data-centric quantitative thinking. We will tour the basic data science techniques from manipulation and summarizing the essential characteristics of a data set, basic statistical modeling, visualization, and prediction.
Learning outcomes:
• Learning the fundamentals of data science pipeline
• Learning how to explore and experiment with data
• Learn basic statistics (sampling techniques, mean, variance, outliers, distributions) and machine learning techniques that are necessary to analyze data
• Perform statistical analysis on sample socio-economic data
• Building an understanding of data analytics techniques (data collection, cleaning, exploratory techniques, modeling, and presentation)
• Develop competency in the Python programming language within the course projects and assignments
• Design and run experimental tests to evaluate hypotheses about data