Learning the fundamentals of data science pipeline
Learning how to explore and experiment with data
Learn basic statistics (sampling techniques, mean, variance, outliers, Central Limit theorem, distributions) and machine learning techniques (clustering) that are necessary to analyze data: big and small
Perform a 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 project
Design and run experimental tests to evaluate hypotheses about data