## Spring 2020 Courses

### Backbone

## Title |
## Course Number |
## Times & Locations |
## Description |
## Instructor |
## Units |

Foundations of Data Science (Data 8) |
STAT C8 Class #: 22140 |
MWF 10-11am Wheeler 150 |
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership. | 4 | |

Principles & Techniques of Data Science (Data 100) |
COMPSCI C100 Class #: 28679 |
TTh 9:30-11am Wheeler 150 |
In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing. | Joseph Edgar Gonzalez, Ani Adikari | 4 |

Data, Inference, and Decisions (Data 102) |
STAT 102 Class #: 31062 |
TTh 9:30-11am Lewis 100 |
This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods. | Jacob Noah Steinhardt | 4 |

Probability for Data Science |
STAT 140 Class #: 22613 |
TuTh 3:30-5pm Li Ka Shing 245 |
An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems. Random variables, discrete and continuous families of distributions. Bounds and approximations. Dependence, conditioning, Bayes methods. Convergence, Markov chains. Least squares prediction. Random permutations, symmetry, order statistics. Use of numerical computation, graphics, simulation, and computer algebra. | Ani Adhikari | 4 |