Deriving Knowledge from Data at Scale
This course is designed to pull together what has been learned so far about the structuring and manipulation of data and core statistical and machine learning techniques and add knowledge on the machinery used to leverage those techniques in real-world scenarios in which data scientists are asked to generate knowledge. Students will be asked to apply the course content to real-life scenarios and think creatively as well as critically through issues. By the end of the class, students will be able to attack data sciences questions impacting their business, establish robust experimental tests of data-driven hypotheses, generate meaningful and reliable findings and communicate them clearly.
- Motivation & Applications of Machine Learning
- Supervised Learning
- Linear and Non-Linear Learning Models
- Classification, Clustering and Dimensionality Reduction
- Advanced Non-Linear Models
- Collaborative Filtering and Recommendation
- Models that are Robust
- Data Sciences with Text and Language
- Data Sciences with Location
- Social Network Analysis
Find the Offering That's Right for You
We offer programs throughout the year in different locations and learning formats designed to meet your education needs. To learn more about this program, select one of the following sections.