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The Master of Science in Data Analytics and Statistics at Washington University in St. Louis is a comprehensive, rigorous program designed to prepare students for the rapidly evolving field of data science. This program combines core principles in statistics, computer science, and domain-specific knowledge to equip graduates with the skills necessary to extract meaningful insights from large and complex data sets. Students will learn advanced statistical methods, data visualization techniques, programming languages such as R and Python, and machine learning algorithms, enabling them to design, develop, and implement data-driven solutions across various industries. The curriculum emphasizes practical application through hands-on projects, industry collaborations, and opportunities to work with real-world data, fostering critical thinking and problem-solving abilities. It aims to produce graduates who are not only proficient in technical skills but also possess the business acumen required to make strategic decisions based on data analysis. The program is suitable for students from diverse backgrounds, including those with degrees in mathematics, computer science, engineering, economics, or related fields, who wish to deepen their knowledge in data analytics and statistics. Students will also benefit from the university’s strong connections with industry partners, access to state-of-the-art laboratories, and a vibrant academic community that encourages innovation and research. Upon completion, graduates will be well-positioned for careers in data science, analytics consulting, data engineering, research, or pursuing further academic research. The program emphasizes ethical considerations in data handling and promotes responsible use of data in decision-making processes. With its flexible coursework options and faculty expertise, the Master of Science in Data Analytics and Statistics at Washington University in St. Louis aims to cultivate future leaders in the data-driven economy.
Statistics Track | |||
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Year 1: Fall ESE 520: Probability and Stochastic Processes Math 439: Linear Statistical Models ESE 425: Random Processes and Kalman Filtering |
Year 1: Spring ESE 524: Detection and Estimation Theory Math 494: Mathematical Statistics CSE 514: Data Mining ESE 415: Optimization |
Year 2: Fall Math 475: Statistical Computing Math 5061: Theory of Statistics I ESE 551: Linear Dynamic Systems I |
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Optimization and Decision Theory Track | |||
Year 1: Fall ESE 520: Probability and Stochastic Processes ESE 403: Operations Research Math 439: Linear Statistical Models / ESE 551: Linear Dynamic Systems I |
Year 1: Spring ESE 524: Detection and Estimation Theory Math 494: Mathematical Statistics CSE 514: Data Mining ESE 415: Optimization |
Year 2: Fall CSE 541: Advanced Algorithms ESE 427: Financial Mathematics [or other application class] ESE 516: Optimization in Function Space |
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Computing Track | |||
Year 1: Fall ESE 520: Probability and Stochastic Processes Math 475: Statistical Computation CSE 511: Introduction to Artificial Intelligence |
Year 1: Spring ESE 524: Detection and Estimation Theory Math 494: Mathematical Statistics CSE 514: Data Mining CSE 517: Machine Learning |
Year 2: Fall ESE 516: Optimization in Function Space Math 439: Linear Statistical Models ESE 403: Operations Research |
Sample Programs Joint with MSEE
Joint MSEE and Statistics Track | |||
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Year 1: Fall ESE 520: Probability and Stochastic Processes ESE 551: Linear Dynamic Systems I ESE 425: Random Processes and Kalman Filtering |
Year 1: Spring ESE 524: Detection and Estimation Theory Math 494: Mathematical Statistics Math 459: Bayesian Statistics ESE 415: Optimization |
Year 2: Fall Math 475: Statistical Computing Math 5061: Theory of Statistics I ESE 545: Stochastic Control ESE 523: Information Theory |
Year 2: Spring CSE 514: Data Mining CSE 517: Machine Learning ESE 553: Nonlinear Dynamic Systems Math 5062: Theory of Statistics II |
Joint MSEE and Optimization and Decision Theory Track | |||
Year 1: Fall ESE 520: Probability and Stochastic Processes ESE 403: Operations Research ESE 551: Linear Dynamic Systems I |
Year 1: Spring ESE 524: Detection and Estimation Theory Math 494: Mathematical Statistics CSE 514: Data Mining ESE 415: Optimization |
Year 2: Fall CSE 541: Advanced Algorithms ESE 427: Financial Mathematics [or other application class] ESE 516: Optimization in Function Space ESE 588: Quantitative Image Processing |
Year 2: Spring CSE 517: Machine Learning ESE 553: Nonlinear Dynamic Systems ESE 544: Optimization and Optimal Control Math 459: Bayesian Statistics |
Requirements
- Application Fee ($75), credit card or check by mail
- Unofficial copies of undergraduate and/or graduate transcripts
- Three Letters of Recommendation
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- Input recommendation providers' names and email addresses. Recommendation providers are automatically sent an email requesting a recommendation.
- Paper and email recommendations will not be accepted.
- The recommendations must be posted by the published deadline for final application submission.
- Statement of Purpose and Resume/CV
- The Statement of Purpose should be a brief document explaining your goals and ambitions. (3 page maximum)
- Current Resume or Curriculum Vitae is to be uploaded in the section immediately following the Statement of Purpose.
- GRE Scores
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GRE scores are required for all PhD and full-time Master’s applicants with the exception of applicants to the M. Eng. in Biomedical Innovation degree program.
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GRE scores are not required for applicants to part-time Master’s or the Bachelor’s/Master’s programs.
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If submitting scores, applicants must report their official scores via ETS at the time of application submission for evaluation purposes. The WashU School Code is 6929.
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- TOEFL or IELTS Scores
- Required for all international applicants.
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Applicants must report their official scores via ETS at the time of application submission for evaluation purposes. The WashU School Code is 6929.
Note: This requirement may be waived if the applicant has a minimum of three years of documented study at an English-speaking institution, in a country where English is the primary language of daily living. Based on the evaluation of your application package, we retain the right to require English testing upon arrival and you may be required to take additional English classes. If you are recommended to take English classes, the cost of the courses will be your responsibility.
Scholarships
- Chancellor's Graduate Fellowship Program
- Need-based financial aid assistance
- Merit-based scholarships
The Data Analytics program at Washington University in St. Louis offers students a comprehensive education designed to equip them with the essential skills and knowledge needed to analyze complex data sets and make data-driven decisions across various industries. The curriculum emphasizes both theoretical foundations and practical applications, including statistical methods, data management, machine learning, and visualization techniques. Students engage in hands-on projects, often collaborating with industry partners, to develop real-world problem-solving abilities. The program aims to prepare graduates for careers in data analysis, business intelligence, and related fields by providing a strong grounding in statistical theory, programming languages such as R and Python, and data wrangling skills. Instructors are experienced faculty members with expertise in statistics, computer science, and business analytics, ensuring a multidisciplinary approach that reflects the evolving landscape of data science. The program typically offers a flexible study format, accommodating both full-time and part-time students, and may include opportunities for internships, research projects, and participation in seminars and workshops. Students also gain exposure to ethical considerations in data analysis, ensuring responsible and fair use of data. The university leverages its connections with local and national organizations to provide networking opportunities and practical experience. Graduates of the Data Analytics program are well-prepared to enter the workforce, pursue advanced degrees, or contribute to the development of innovative data solutions in sectors such as healthcare, finance, technology, and government. The program's rigorous coursework, combined with experiential learning opportunities, embodies Washington University in St. Louis's commitment to academic excellence and real-world relevance, ensuring students are adept at transforming data into actionable insights.