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data_science_curriculum [2018/07/25 12:19]
hkimscil
data_science_curriculum [2018/07/25 12:47] (current)
hkimscil
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   * Medical Informatics and Decision Management   * Medical Informatics and Decision Management
   * Health IT Project 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:​ Methods I (2.5 credits)  
 +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 ​ Clinical Informatics (5 credits)
  
 ====== temp ====== ====== temp ======
data_science_curriculum.txt · Last modified: 2018/07/25 12:47 by hkimscil