Information about the MSE in Data Science https://dats.seas.upenn.edu August 24, 2017

Who are we? Professor Zack Ives, Associate Dean for Masters and Professional Programs Professor Susan Davidson, Faculty Director University of Pennsylvania

Webinar Goals Give an overview of the new program and our goals Explain how to apply to transfer, submatriculate, do a dual degree Provide answers to your questions (type these into the Questions box in GotoWebinar) University of Pennsylvania

What is the goal of the program? Data science has become essential to making decisions, understanding observations, and solving problems in today’s world.   Penn’s MSE in Data Science prepares students for a wide range of data- centric careers, whether in technology and engineering, consulting, science, policy-making, or understanding patterns in literature, art or communications. (CS+Stat+Math) ∩ (Science | Economics | Sociology | Business | Law |…) University of Pennsylvania

Eligibility (for 2017-18) A student in an SEAS Master’s program who has completed at least one semester An undergraduate SEAS student who wishes to submatriculate GPA of 3.4 or higher Demonstrated proficiency and background in mathematical foundations, including probability, statistics, optimization, and/or mathematical modeling. Linear algebra is preferred. Demonstrated background in programming in Python, Java, R, Matlab, or equivalent. It is strongly suggested that applicants have completed at least two of the courses listed in the Background, Core, and/or Depth areas with an A- or higher grade. Backgrounds in technical, engineering, or informatics disciplines will also be favored. University of Pennsylvania

The Curriculum – 10 Course Units Foundations (2CU) – fundamental skills and background Core (3CU) – the courses every Data Science student will take Technical and Depth Electives (3CU) – data science techniques / applications Thesis / Practicum / 2-Course Sequence (2CU) – a focused project or specific topic of study as a capstone Disclaimer: The reason why we have a “soft opening” restricted to current Penn students is that the curriculum is “in flux” – many new courses are becoming available across the university that we need to understand how to include. University of Pennsylvania

The Curriculum – 10 Course Units Foundations (2CU) – fundamental skills and background Probability (ENM 503, Fall; STAT 510, Fall/Spring; MATH 546, Fall; submats: STAT 430 or ESE 301) Programming (CIT 590, Fall; submats: CIS 120) If these requirements are met, students should take higher-level grad courses in these areas (e.g., statistics, advanced programming) Core (3CU) – the courses every Data Science student will take Technical and Depth Electives (3CU) –data science techniques / applications Thesis / Practicum / 2-Course Sequence (2CU) – a focused project or specific topic of study as a capstone University of Pennsylvania

The Curriculum – 10 Course Units Foundations (2CU) – fundamental skills and background Core (3CU) – the courses every Data Science student will take Statistics (STAT 512, Spring) Machine learning (CIS 519, Fall; CIS 520, Fall/Spring) Big data analytics (CIS 545, Spring) Technical and Depth Electives (3CU) – data science techniques / applications Thesis / Practicum / 2-Course Sequence (2CU) – a focused project or specific topic of study as a capstone University of Pennsylvania

The Curriculum – 10 Course Units Foundations (2CU) – fundamental skills and background Core (3CU) – the courses every Data Science student will take Technical and Depth Electives (3CU) – data science techniques / applications 1 approved Technical Elective, in consultation with the Program Director 2 additional courses from a Depth Area, in consultation with the Program Director Computer and information science: machine learning, algorithms, speech, infrastructure Electrical and systems engineering: optimization, adaptive control Network and social science Digital humanities Biomedicine: radiology, genetics, neuroscience Thesis / Practicum / 2-Course Sequence (2CU) – a focused project or specific topic of study as a capstone University of Pennsylvania

The Curriculum – 10 Course Units Foundations (2CU) – fundamental skills and background Core (3CU) – the courses every Data Science student will take Technical and Depth Electives (3CU) – data science techniques / applications Thesis / Practicum / 2-Course Sequence (2CU) – a focused project or specific topic of study as a capstone Thesis: with an Engineering advisor, develop a new general data science algorithm, tool, technique Practicum: with a project mentor in a problem domain (eg genetics), do a significant project to integrate data, evaluate hypotheses, build models, and / or create classifiers 2-Course Sequence: in consultation with the Program Director, choose a specific topic (e.g., text analysis) and take two courses in this topic University of Pennsylvania

Questions? University of Pennsylvania