The purpose of the Personal Statement is for you to share more about your past experiences and to discuss how these experiences have contributed to your personal and professional growth. It allows the applicant the opportunity to explain to the admission committee the distinct qualities and commitment they can bring to their department and to the overall Columbia Engineering community.
We recommend that your Personal Statement be between 250 and 1,000 words. Your application will not be negatively impacted should exceed this recommendation. Please do not email us to request permission to exceed this recommendation, no permission is necessary.
A few topics that you may want to address in your Personal Statement include:
Describe how your background has prepared you to pursue an advanced degree in the field of engineering or applied science at Columbia University.
Describe the reasons you are interested in this program and discuss any relevant past experience.
If you have relevant work or research experience, please indicate how it helped you decide on your career path.
What are your post-graduation plans or career goals?
What do you hope to gain from this program?
What about this program excites you?
If there are any special circumstances that need to be brought to the attention of the Admission Committee, please include that information.
COMS W4121 Computer Systems for Data Science
COMS W4721 Machine Learning for Data Science
CSOR W4246 Algorithms for Data Science
ENGI E4800 Data Science Capstone and Ethics
STAT GR5701 Probability and Statistics for Data Science
STAT GR5702 Exploratory Data Analysis and Visualization
STAT GR5703 Statistical Inference and Modeling
COMS W4995 Topics in Computer Science: Applied Machine Learning
COMS W4995 Topics in Computer Science: Applied Deep Learning
COMS W4995 Topics in Computer Science: Causal Inference for Data Science
COMS W4995 Topics in Computer Science: Data Analytics Pipeline
COMS W4995 Topics in Computer Science: Elements of Data Science
COMS E6998 Topics in Computer Science: Machine Learning with Probabilistic Programming
COMS E6998 Natural Language Processing: Computational Models of Social Meaning
EECS E6894 Topics in Information Processing: Deep Learning for Computer Vision, Speech, and Language
IEOR E4571 Topics in Operations Research: Personalization Theory & Application
IEOR E4721 Topics in Quantitative Finance: Big Data in Finance
STATS GR5293 Topics in Modern Statistics: Applied Machine Learning for Financial Modeling and Forecasting
STATS GR5293 Topics in Modern Statistics: Applied Machine Learning for Image Analysis
Cross-Registration Instructions for Non-Data Science Students