Data Science Methods

Explores methods used in Data Science (DS), assuming a strong foundation mathematical and analytical methods relevant to DS. To gain a deeper understanding of the complexities and limitations of these methods, this class focuses on real-life datasets and on challenging assumptions. Students work on practical projects that require creative problem-solving and critical thinking skills. Class provides a valuable opportunity to bridge the gap between theoretical knowledge and real-world applications in the field of Data Science. Topics covered include exploratory data analysis, linear regression, support vector machine, cluster analysis, principal components analysis, and neural networks.