MIDS课程：
- Unifying Data Science (IDS 701)
This course is focused on how to answer questions effectively using quantitative data. By the end of the course, students will be able to recognize different types of questions (e.g. descriptive, causal, and predictive questions), have an understanding of what methodological approaches are most appropriate for answering each type of question, be able to design and critically evaluate data analysis plans, and understand how to tailor their presentation of results to different audiences.
- Modeling and Representation of Data (IDS 702)
Statistical models are necessary for analyzing the type of multivariate (often large) datasets that are usually encountered in data science. In this course, you will learn the general work flow for building statistical models and using them to answer inferential questions. You will learn several parametric models such as generalized linear models, models for multilevel data and time series models.
- Introduction to Natural Language Processing (IDS 703)
Introduction to the rich opportunities for using textual data produced by websites, social media platforms, digitization of administrative and historical records, and new monitoring technologies to gain insights and make decisions.
- Data Science Ethics (IDS 704)
Data Science tools are not morally neutral. This course is designed to help students think explicitly about their social responsibility as data scientists and the impact on the world of what they are building and analyzing.
- Practicing Machine Learning (IDS 705)
Automating prediction and decision-making based on data and past experience. Students will learn how and when to apply supervised, unsupervised, and reinforcement learning techniques, and how to evaluate performance. Common pitfalls such as overfitting and data leakage will be explored and how they can be avoided.
- Data Engineering Systems (IDS 706)
Data Engineering Systems is a course about data and how to manage and build systems. Divided into two halves, part 1 focuses on Relational Databases. These systems are the most common type of database used today and are found in applications ranging from holding cell phone contact lists (both Android and iOS use SQLlite3 internally) to managing every aspect of a large bank or insurance company.
- Data Logic, Visualization, and Storytelling (IDS 707)
Principles of communicating the implications of a data analysis.
Students will cultivate the ability to think critically and skeptically about the questions they need to answer in a data project and the strategies they are using to answer them. Students will learn the principles behind effective data visualization and how to implement them in real analyses using Tableau software.
- Data Science Dialogues (IDS 791)
A series of discussions that give students snapshots of data science projects from practitioners and researchers.
- Capstone Project (IDS 798)
MIDS students join Capstone partnerships their second year and during that year make substantial contributions to these real, complex projects. Project teams can be as large as necessary and can include multiple faculty, postdocs, graduate and undergraduate students, and other staff. Although students work collaboratively, each MIDS student must achieve a specific outcome of interest for the outside party and give a final presentation.
A series of workshops to gain soft skills such as interviewing, negotiating, and networking.
选修课：
· Practicing Data Science (IDS 720)
This course will provide students with extensive hands-on experience manipulating real (often messy, error ridden, and poorly documented) data using the a range of bread-and-butter data science tools (like the command line, git, python (especially numpy and pandas), jupyter notebooks, and more). The goal of these exercises is to ensure students are comfortable working with data in most any form.
· Data Analysis at Scale in Cloud (IDS 721)
Data Analysis at Scale in the Cloud is a project based course with extensive hands-on assignments. This course is designed to give students a comprehensive view of cloud computing including Big Data and Machine Learning. A variety of learning resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure).
Popular Electives (Sample)
- Intro to Deep Learning (COMPSCI 675D)
- Statistical Computation (STA 663L)
- Theory & Algorithm Machine Learning (ECE 687D)
- Machine Learning and Imaging (BME 548L)
- Foundations of GIS and Geospatial Analysis (ENVIRON 559)
- Introduction to Social Networks (SOCIOL 728)
- Machine Learning for FinTech (FINTECH 540)
- Research Tech Translation (I&E 710)
- Mathematical Finance (MATH 780)
- Social Networks & Pol Interdependence (POLSCI 634)
