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Massive volumes of intricately structured data - including text, video, and live streams - are continuously gathered across various sectors. These span social media platforms (like Google, Twitter, Facebook), life sciences (such as pharmaceutical research and healthcare), STEM fields (including astronomy, network systems, and smart infrastructure), and commercial industries (from automotive to finance, insurance, and digital advertising), along with government and societal initiatives. Data science professionals play a crucial role in measuring and tackling contemporary challenges in healthcare, environmental sustainability, cybersecurity, social justice, and economic systems. This interdisciplinary undergraduate program, collaboratively offered by the EECS Department in Engineering and the Statistics Department in LSA, cultivates versatile data scientists equipped to handle diverse large-scale data challenges prevalent today. The curriculum emphasizes computational techniques, statistical modeling, and practical application. This demanding major establishes core competencies in computer science, statistics, and mathematics essential for processing and interpreting complex, voluminous datasets. Students will gain both hands-on experience with data science applications and theoretical understanding of methodological frameworks and algorithmic performance. Program alumni will emerge prepared for dynamic data science careers and advanced academic pursuits.
High school transcript
Generally Accepted Minimum Scores
English proficiency scores are one component in the overall review of an application. Generally accepted minimum scores for each of the accepted tests are:
MELAB: 80-85 range with section scores of at least 80
TOEFL (PBT): 570-600 range with section scores of at least 57
TOEFL (iBT): 88-100 range with section scores of at least 23 in listening and reading, and at least 21 in speaking and writing
IELTS: 6.5-7.0 range with section scores of at least 6.5