data_science_curriculum
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| + | * Using wearables data to monitor and prevent health problems | ||
| + | * Improving diagnostic accuracy and efficiency | ||
| + | * Turning patient care into precision medicine | ||
| + | * Advancing pharmaceutical research to find cure for cancer and Ebola | ||
| + | * Optimizing clinic performance through actionable insights | ||
| + | * Taking the risk out of prescription medicine | ||
| + | * Reducing hospital readmissions to cut healthcare costs | ||
| + | |||
| + | * Medical image analysis | ||
| + | * Genetics and Genomics | ||
| + | * Virtual assistance for patients and customer support | ||
| + | * Predictive medicine: prognosis and diagnostic accuracy | ||
| + | * Managing customer data | ||
| + | * Industry knowledge | ||
| + | |||
| + | |||
| + | * Health Care Management and Strategy | ||
| + | * Medical Informatics and Decision Management | ||
| + | * Health IT Project Management | ||
| + | * Population and Community Health Analytics | ||
| + | * Business Intelligence & The Internet of Medical Things (IoMT) | ||
| + | * Research Analytics & Predictive Analytics | ||
| + | * Health Innovation and Entrepreneurship / Capstone | ||
| + | ====== Harvard Univ. ====== | ||
| + | 2.6.1 Course Requirements for the Health Data Science SM60 Degree | ||
| + | The degree requirements include a 20 credit ordinally graded core curriculum consisting of: | ||
| + | * BST 222 Basics of Statistical Inference (Fall, 5 credits) | ||
| + | * BST 260 Introduction to Data Science (Fall, 5 credits) | ||
| + | * BST 261 Data Science II (Spring 2, 2.5 credits) | ||
| + | * BST 262 Computing for Big Data (Fall 2, 2.5 credits) | ||
| + | * BST 263 Applied Machine Learning (Spring, 5 credits) | ||
| + | An additional five credits must be taken in computer science from the following list: | ||
| + | * BST 234 Introduction to Data Structures and Algorithms (5 credits) | ||
| + | * BST 281 Genomic Data Manipulation (5 credits) | ||
| + | * APMTH 120 Applied Linear Algebra and Big Data (5 credits) | ||
| + | * BMI 713 Computational Statistics for Biomedical Science (5 credits) | ||
| + | * CS 105 Privacy and Technology (5 credits) | ||
| + | * CS 124 Data Structures and Algorithms (5 credits) | ||
| + | * CS 164 Software Engineering Computer Science (5 credits) | ||
| + | * CS 165 Data Systems (5 credits) | ||
| + | * CS 171 Visualization (5 credits) | ||
| + | * CS 187 Computational Linguistics (5 credits) | ||
| + | * STAT 171 Introduction to Stochastic Processes (5 credits) | ||
| + | 5 Twenty-five additional credits must be taken. Courses that would satisfy these requirements may come from | ||
| + | the following list of elective courses: | ||
| + | * BST 210 Applied Regression Analysis (5 credits) | ||
| + | * BST 216 Introduction to Quantitative Methods for Monitoring and Evaluation (2.5 credits) | ||
| + | * BST 223 Applied Survival Analysis (5 credits) | ||
| + | * BST 226 Applied Longitudinal Analysis (5 credits) | ||
| + | * BST 228 Applied Bayesian Analysis (5 credits) | ||
| + | * BST 254 Sec 3 Measurement Error and Misclassification (2.5 credits) | ||
| + | * BST 267 Introduction to Social and Biological Networks (2.5 credits) | ||
| + | * BST 280 Introductory Genomics & Bioinformatics for Health Research (2.5 credits) | ||
| + | * BST 282 Introduction to Computational Biology and Bioinformatics (5 credits) | ||
| + | * BST 283 Cancer Genome Analysis (5 credits) | ||
| + | * EPI 202 Elements of Epidemiologic Research: Methods 2 (2.5 credits) | ||
| + | * EPI 203 Study Design in Epidemiologic Research (2.5 credits) | ||
| + | * EPI 204 Analysis of Case-Control and Cohort Studies (2.5 credits) | ||
| + | * EPI 233 Research Synthesis & Meta-Analysis (2.5 credits) | ||
| + | * EPI 271 Propensity Score Analysis (1.25 credits) | ||
| + | * EPI 286 Database Analytics in Pharmacoepidemiology (2.5 credits) | ||
| + | * EPI 288 Data Mining and Prediction (2.5 credits) | ||
| + | * EPI 293 Analysis of Genetic Association Studies (2.5 credits) | ||
| + | * ID 271 Advanced Regression for Environmental Epidemiology (2.5 credits) | ||
| + | * RDS 280 Decision Analysis for Health and Medical Practices (2.5 credits) | ||
| + | * RDS 282 Economic Evaluation of Health Policy and Program Management (2.5 credits) | ||
| + | * RDS 285 Decision Analysis Methods in Public Health and Medicine (2.5 credits) | ||
| + | * APMTH 207 Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization (5 credits) | ||
| + | * APMTH 221 Advanced Optimization (5 credits) | ||
| + | * BMI 701 Introduction to Biomedical Informatics (5 credits) | ||
| + | * BMI 702 Foundation of Biomedical Informatics II (2.5 credits) | ||
| + | * BMI 703 Precision Medicine I: Genomic Medicine (2.5 credits) | ||
| + | * BMI 705 Precision Medicine II: Integrating Clinical and Genomic Data (2.5 credits) | ||
| + | * BMI 706 Data Visualization for Biomedical Applications (2.5 credits) | ||
| + | * CI 722.0 Clinical Data Science: Comparative Effectiveness Research I (2.5 credits) | ||
| + | * ME 530M.1 Clinical Informatics (5 credits) | ||
| + | * STAT 260 Design and Analysis of Sample Surveys (5 credits) | ||
| + | Other courses may also be acceptable. EPI 201 (see section 2.4.3) will count as one of the 55 credit | ||
| + | ordinal courses required. Students are advised to consult with the Executive Director about any substitutions. | ||
| + | |||
| + | Core courses | ||
| + | * BST 222 Basics of Statistical Inference (5 credits) | ||
| + | * BST 260 Introduction to Data Science (5 credits) | ||
| + | * BST 261 Data Science II (2.5 credits) | ||
| + | * BST 262 Computing for Big Data (2.5 credits) | ||
| + | * BST 263 Applied Machine Learning (5 credits) | ||
| + | Epidemiology Requirement | ||
| + | * EPI 201 Introduction to Epidemiology: | ||
| + | Computing Requirement | ||
| + | * BST 234 Introduction to Data Structures and Algorithms (5 credits) | ||
| + | * BST 281 Genomic Data Manipulation (5 credits) | ||
| + | * BMI 713 Computational Statistics for Biomedical Science (5 credits) | ||
| + | * CS 105 Privacy and Technology (5 credits) | ||
| + | * CS 164 Software Engineering Computer Science (5 credits) | ||
| + | * CS 165 Data Systems (5 credits) | ||
| + | * CS 171 Visualization (5 credits) | ||
| + | * CS 187 Computational Linguistics (5 credits) | ||
| + | * STAT 171 Introduction to Stochastic Processes (5 credits) | ||
| + | Project-Based Research Course | ||
| + | * HDS 325 Health Data Science Practice (7.5 credits) | ||
| + | |||
| + | Elective Courses | ||
| + | * BST 210 Applied Regression Analysis (5 credits) | ||
| + | * BST 223 Applied Survival Analysis (5 credits) | ||
| + | * BST 226 Applied Longitudinal Analysis (5 credits) | ||
| + | * BST 228 Applied Bayesian Analysis (5 credits) | ||
| + | * BST 267 Introduction to Social and Biological Networks (2.5 credits) | ||
| + | * BST 270 Reproducible Data Science (2.5 credits) | ||
| + | * BST 282 Introduction to Computational Biology and Bioinformatics (5 credits) | ||
| + | * BST 283 Cancer Genome Analysis (5 credits) | ||
| + | * EPI 202 Elements of Epidemiologic Research: Methods 2 (2.5 credits) | ||
| + | * EPI 203 Study Design in Epidemiologic Research (2.5 credits) | ||
| + | * EPI 204 Analysis of Case-Control and Cohort Studies (2.5 credits) | ||
| + | * EPI 271 Propensity Score Analysis (1.25 credits) | ||
| + | * EPI 288 Data Mining and Prediction (2.5 credits) | ||
| + | * ID 271 Advanced Regression for Environmental Epidemiology (2.5 credits) | ||
| + | * BMI 701 Introduction to Biomedical Informatics (5 credits) | ||
| + | * BMI 702 Foundation of Biomedical Informatics II (2.5 credits) | ||
| + | * BMI 703 Precision Medicine I: Genomic Medicine (2.5 credits) | ||
| + | * BMI 705 Precision Medicine II: Integrating Clinical and Genomic Data (2.5 credits) | ||
| + | * BMI 706 Data Visualization for Biomedical Applications (2.5 credits) | ||
| + | * CI 722.0 Clinical Data Science: Comparative Effectiveness Research I (2.5 credits) | ||
| + | * ME 530M.1 | ||
| + | |||
| ====== temp ====== | ====== temp ====== | ||
| + | <WRAP col3> | ||
| + | 클라우드 컴퓨팅 | ||
| + | 머신 러닝 | ||
| + | 텍스트 마이닝 - 분석 | ||
| + | |||
| Edutainment & Media | Edutainment & Media | ||
| * 데이터조사방법론 | * 데이터조사방법론 | ||
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| ====== Curriculum Design ====== | ====== Curriculum Design ====== | ||
| 아래 대 분류 섹션은 각 대학교의 DS 프로그램의 커리큘럼 내용입니다. 큰 그림으로 보면 미국의 이런 프로그램은 대개 " | 아래 대 분류 섹션은 각 대학교의 DS 프로그램의 커리큘럼 내용입니다. 큰 그림으로 보면 미국의 이런 프로그램은 대개 " | ||
data_science_curriculum.1458621453.txt.gz · Last modified: 2016/03/22 13:07 by hkimscil