Housed in our Data Science program, this certificate provides the quantitative foundations, data analysis and interpretation, machine learning, artificial intelligence, and deep learning necessary for in-demand positions in Data Science and AI/ML. This certificate focuses on the intersection of artificial intelligence & machine learning and data science, where applications of predictive modeling can help make sense of enormous amounts of data. The program may be stacked with other certificates to customize your education and/or serve as an entry point to Master of Science degrees in Data Science or Artificial Intelligence and Machine Learning.
Applied Artificial Intelligence and Machine Learning for Data Science Program Requirements
- Five-course program provides the quantitative foundations of AI/Machine Learning for Data Science, including data analysis and interpretation, machine learning, artificial intelligence, deep learning, and other related electives.
- Offered online or on-campus
- Graduates of the program may immediately transfer into the following programs (if completed with predetermined grade requirements): Master of Science in Data Science, or Master of Science in Artificial Intelligence and Machine Learning
Applied Artificial Intelligence and Machine Learning for Data Science Certificate Coursework
- Three required core courses (9 credits):
- DSCI 501 Quantitative Foundations for Data Science: Linear algebra, calculus, probability and statistical methods are essential foundation areas required for an effective understanding and application of data science. In this course, students will get a gentle introduction to these important areas of quantitative reasoning. Along with introducing basics of linear algebra, calculus, probability, and statistical methods, this course will also introduce their computational application through the Python programming language. Concepts will be demonstrated using various python packages.
- DSCI 521 Data Analysis and Interpretation: Introduces methods for data analysis and their quantitative foundations in application to pre-processed data. Covers reproducibility and interpretation for project life cycle activities, including data exploration, hypothesis generation and testing, pattern recognition, and task automation. Provides experience with analysis methods for data science from a variety of quantitative disciplines. Concludes with an open-ended term project focused on the application of data exploration and analysis methods with interpretation via statistical, algorithmic, and mathematical reasoning.
- DSCI 631 Applied Machine Learning: Introduces relevant topics in the life cycle of machine learning: extracting and engineering features, tuning parameters, comparing algorithms, interpreting results, and analyzing errors. Students will be exposed to various representative algorithms in the concept level and learn their trade-offs. Students will gain hands-on experiences with assignments and a term project. Students will be prepared to attack new problems using various machine learning methods and be able to compare the performance of different algorithms for the term project.
- Two electives courses (6 credits - choose any two below):
- Sample elective courses: CS 501 Introduction to Programming or CS 570 Programming Foundations; CS 510 Introduction to Artificial Intelligence; CS 502 Data Structures and Algorithms; CS 503 System Basics; CS 613 Machine Learning; CS 615 Deep Learning; DSCI 591 Data Science Capstone I; DSCI 592 Data Science Capstone II
Curriculum subject to change, pending Drexel Faculty Senate approval.
- Graduate application for the on-campus program or the online program
- A four-year bachelor's degree or Master’s degree from a regionally accredited institution in Computer Science, Software Engineering or related STEM degree plus work experience equal to Drexel's Graduate Certificate in Computer Science.
- A GPA of 3.0 or higher, in a completed degree program, bachelor’s degree or above.
- Official final transcripts from ALL Colleges/Universities attended.
- Graduate Record Examination (GRE) Scores (scores must be five years old or less) are recommended for international students and domestic students below a 3.0 GPA.
- One (1) letter of recommendation are required. Two (2) are suggested.
- Essay/Statement of Purpose (approximately 500 words).
- Current Resume.
- Pre-requisites for all graduate level programs: computer requirements and skills
- Additional requirements for International Students.
Additional Certificates and Minors in Data Science Available
CCI also offers graduate data science certificates (available online or on campus) in Intro to Data Science, Big Data Analytics, Computational Data Science, and Applied Data Science.
For current graduate students who are interested in gaining data science knowledge to complement their master's degree, the College offers graduate minors in Computational Data Science and Applied Data Science.