The cart is empty

PhD PROGRAM MODULES

PUT 902: Advanced Biostatistics and Data Science
PRE-REQUISITES: 
 Prior knowledge of basic statistics.
MODULE DESCRIPTION
This course teaches the concepts of biostatistics and the application of biostatistics in real world issues. Statistical methods and principles necessary for understanding and interpreting data used in environmental health and policy evaluation and formation. Topics include descriptive statistics, graphical data summary, sampling, statistical comparison of groups, correlation, and regression.
Course Content
·         Probability and advanced statistical theories
·         parametric and non-parametric statistics,
·         Poisson distribution, Regression modelling, statistical software appreciation and bioinformatics.
Data Science introduces the concept and tools needed in turning open and real-world data into solving real world problems via mastering Data communication, data investigation, data wrangling, cleaning, sampling, exploratory analysis and data Visualization skills.
Students will learn the powerful statistical program in R and how to use R for effective data analysis and statistical programming. The course will also cover practical issues in statistical computing with R, especially in reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
MODULE AIMS
  • To guide students on proper methods of design of experiments, data collection/collation
  • To introduce students to statistical methods in public health research.
  • To provide guidance on various scientific methods of analyzing public health statistical data.
  • To introduce students to data analysis using various statistical software packages like SPSS, R, Minitab, etc.
INTENDED LEARNING OUTCOMES 
On successful completion of this module student should be able to:
1)      interpret results to suit experimental objectives.
2)      present research / study results and inferences therein.
3)      correctly perform basic descriptive statistics on public health data.
4)      effectively design simple survey to obtain public health data.
5)      analyse data using the parametric tests.
6)      analyse data using different statistical software.
MODULE EXECUTION PLAN:
This module shall consist of 14 lectures covering 21 topics to be delivered in a classroom setting. Additional learning experiences shall be in form of group-based tutorial, and individual seminar presentation which shall weekly or on prearranged dates for the duration of the course. Each lecturer shall ensure formative assessment of students learning achievements as well as take feedback on students’ experiences with each teaching contact. Assignments (formative and summative) shall comprise individual works and small group activities. Formative assessment shall be conducted to cover the recently completed series. At the end of the module, a final summative assessment shall be undertaken by students which will cover the entire syllabus of the module.
TEACHING AND LEARNING EXPERIENCES WITH CONTACT HOURS
 
Activity type
A (Applicable)/
N/A (Not applicable)
Contact hours
1
Lectures (L)
A
23
2
Tutorials (T)
A
3
3
Seminar presentation (SP)
A
8
4
Course paper/assignment (CP/A)
A
4
5
Practical/demonstrations (PR)
A
6
 
Self-directed learning
-
-
6
Others, pls specify
-
-
 
CONTENT/ACTIVITY SCHEDULE
 
ACTIVITY
TYPE                        
TOPIC
CONTACT
 HOURS
                    INSTRUCTOR
1
Lecture
Review of Descriptive Statistics
1
 
2
Lecture
Sampling Techniques / Methods
2
 
3
Lecture
Concept of Biostatistics
1
 
4
Lecture
Probability and Advanced Statistical Theory I: Normal and Binomial Distributions
2
 
5
Lecture
Probability and Advanced Statistical Theory II: Poisson and Exponential Distributions
2
 
6
Lecture
Parametric Statistics I
2
 
7
Lecture
Parametric Statistics II
2
 
8
Lecture
Non-Parametric Statistics
1
 
9
Lecture
Population Growth Models
1
 
10
Lecture
Regression Models I: Simple and Multiple Linear Regression
2
 
11
Lecture
Regression Models II: Logistic Regression and Transformations
2
 
12
Lecture
Simple Survival Analysis and Clinical Trials
1
 
13
Lecture
Correlation Coefficients I: Spearman and Pearson
2
 
14
Lecture
Correlation Coefficients II: Partial and Multiple
2
 
15
Practical
Statistical Software Application
3
 
16
Practical
Statistical Software Application
3
 
17
Assignment
Exercise on Parametric tests
NA
 
18
Seminar
A statistical analysis using any of the Software
8
 
19
Assignment
Exercise on Non-Parametric tests
NA
 
20
Assignment
Analyzing sample health data using statistical software application.
NA
 
21
Assignment
Analyzing actual public health data using two statistical software applications.
NA
 
MODULE ASSESSMENT
FORMATIVE
This shall be based on the discretion of course instructor and may include but not limited to activities such as class participation and hands-on application of software, seminar presentation, take-home assignment and classroom written test.
SUMMATIVE
This shall be constituted by the continuous assessment scores, oral presentation and final examination score. Students shall be notified at least a week before a continuous summative assessment while final  examination shall be as scheduled in the session calendar and according to the examination time table which shall be released as at when due. Continuous summative assessment can also be derived from class participation, seminar presentation, course paper/written assignment and classroom written tests. On the other hand, the summative assessment shall variably consist of MCQs, OSCE, Essays, and Practical.
RESIT EXAMINATION
Students whose assignments and course papers are considered unsatisfactory shall undertake compensatory tasks (e.g. write an essay or do a synopsis) to make up for the defect in performance. A student who fails to obtain a mean score of 50% and./or fail to satisfy the requirement for ‘Pass’ in a module will be entitled to re-assessment in a re-sit examination three months later. However, during the three months of preparation, the student must be given opportunity for fresh continuous assessment scores. The same criteria for the main examination shall apply to the re-sit examination.
RESOURCES (Materials for further readings in addition to the taught content of a lecture)
BOOKS:
1. Rosmer. Fundamentals of Biostatistics, 7th Ed.
2. Steel, R.G.D. and Torrie, J.H. Principles and Procedures of Statistics: A Biomedical Approach, 2nd Ed.
3. K. Visweswara Rao. Biostatistics: A Manual of Statistical Methods for Use in Health, Nutrition and Anthropology. Jaypee Brothers Medical Publishers (P) Ltd., 1996.
4. Nduka, E.C. and Ogoke, U.P. Principles of Applied Statistics, Regression and Correlation Analysis.
5. Nduka, E.C. Statistics Concept and Methods
JOURNALS:
1.    Computational Statistics & Data Analysis (Publisher: Elsevier)
2.    The International Journal of Biostatistics (Publisher: De Gruyter)
3.    Journal of Biometrics & Biostatistics (Publisher: Omics International)
4.    International Journal of Clinical Biostatistics and Biometrics (Publisher: ClinMed International Library)
5.    Scientific Journal of Biometrics & Biostatistics (Publisher: ONOMY Science)
WEB-BASED RESOURCES:
1.    https:/hpr.weill.cornell.edu/education/programs/biostatistics-and-data-science/curriculum.html
2.    https:/science.ucalgary.ca/data-science/graduate-programs/diploma-health-data-science-biostatistics
 
PROFILE OF MODULE INSTRUCTORS
 
NDUKA, Ethelbert is a Professor of Statistics in the Faculty of Science, University of Port Harcourt with effect from 2005. He holds a Ph.D from the University of Ibadan (1994). He was Dean of Science (2008-2010) and Deputy Vice-Chancellor, Administration (2011-2015) of University of Port Harcourt. He is a Fellow of Nigerian Statistical Association. His current research interest is on modeling in biometric studies, outliers/missing values in regression analysis. He has successfully supervised 5 Ph.Ds. His email address is below ethelbert.nduka@uniport.edu.ng.
Download CV: CV_Nduka-Ethelbert-Chinaka_ecncv1.docx
 
Dr. (Mrs) Ogoke earned B.Sc (Ed) (Mathematics) from the University of Nigeria Nsukka, M.Sc and Ph.D degrees in Statistics from the University of Port Harcourt. Presently she is a lecturer in the Department of Mathematics and Statistics, University of Port Harcourt. She has attended many local and international workshops and conferences where she presented her work and won a number of awards. She has published widely in both local and international journals. She is a member of relevant professional bodies such as Nigerian Statistical Association, Nigerian Mathematical Society and International Biometric Society, Washington DC, USA. She has her research interest in the area of biostatistics.
PUT 903: ICT, Technical Writing & Presentation Skills
PUT 904: Environmental Epidemiology, Exposure Science And Risk Assessment
PUT 906: Research Methods
PUT 908: Environmental Toxicology
PUT 909: Advanced Nutritional Biochemistry
PUT 910: Analytical methods in Environmental health

Contact Us

ACE-PUTOR
University of Port Harcourt
Rivers State, Nigeria

Email: info@aceputoruniport.edu.ng

Phone: 234(0)8136592033, +234(0)8129429447

Website: www.aceputoruniport.edu.ng

 

Quick Links

About PUTOR

Meet the Team

Learning at PUTOR

PUTOR profile download