Introduction:

Welcome to the comprehensive course on Epidemiology and Biostatistics with ODK, R, Python, Stata, SPSS, Excel, NVivo, Power BI, and GIS! This course is designed to provide you with a deep understanding of epidemiological principles, data analysis techniques, and advanced tools used in public health research and practice.

Epidemiology is the study of the distribution and determinants of health-related events in populations. It plays a crucial role in identifying health trends, risk factors, and opportunities for disease prevention and control. Biostatistics, on the other hand, complements epidemiology by providing the tools and methods to analyze and interpret health-related data, enabling evidence-based decision-making.

In this course, we will cover a wide range of topics, from study design and data collection using ODK for field-based research to data analysis and visualization using various software packages, including R, Python, Stata, SPSS, Excel, NVivo, Power BI, and GIS. You will gain hands-on experience in handling diverse datasets, performing statistical analyses, and creating informative visualizations.

Throughout the course, we will focus on the practical application of epidemiological and biostatistical concepts to real-world public health scenarios. You will have the opportunity to work on case studies, research projects, and a capstone project to apply the knowledge and skills acquired during the training.

By the end of this course, you will be equipped with the tools and techniques necessary to conduct epidemiological studies, analyze health-related data, and contribute to evidence-based decision-making in public health and research settings. Whether you are a public health professional, researcher, or student, this course will empower you to make a meaningful impact in the field of epidemiology and biostatistics.

We are excited to embark on this learning journey with you and look forward to exploring the fascinating world of epidemiology and biostatistics together! Let's get started and make a positive difference in public health through data-driven insights and informed decision-making.

 

Duration:

The course is carefully designed to provide comprehensive coverage of the topics while ensuring sufficient time for hands-on practice and discussions. The duration of the course may vary, but it is typically structured to be completed over three weeks, allowing participants to gain a deep understanding of the subject matter.

Course Objective: The course aims to achieve the following objectives:

1. Provide a solid understanding of epidemiological principles and study designs.
2. Familiarize participants with data collection using ODK for field-based research.
3. Develop data analysis and visualization skills using R, Python, Stata, SPSS, Excel, Power BI, and GIS.
4. Introduce participants to qualitative data analysis using NVivo in the context of mixed-methods research.
5. Enhance participants' ability to communicate epidemiological findings effectively.
6. Prepare participants to contribute to public health research, policy, and practice using evidence-based approaches.

Organization Benefit:

• Strengthen the organization's capacity to conduct rigorous epidemiological studies and data analysis.
• Foster a data-driven decision-making culture by equipping employees with advanced data analysis skills.
• Improve data management and analysis capabilities using a variety of software tools.
• Enhance the organization's reputation in the field of public health and research.
• Facilitate evidence-based policy formulation and program planning.

Target Participants:

• Public health professionals, epidemiologists, and researchers seeking to expand their knowledge and skills in epidemiology and biostatistics.
• Data analysts and statisticians interested in applying their expertise to health-related data.
• Medical professionals and students interested in research and public health.
• Government and non-governmental organization (NGO) personnel involved in health research and surveillance.
• Anyone passionate about public health and data-driven decision-making.

Introduction

Module 1: Introduction to Epidemiology and Public Health

• Overview of epidemiology and its significance in public health
• Key concepts in epidemiological research

Module 2: Principles of Disease Transmission and Control

• Understanding disease transmission dynamics
• Strategies for disease control and prevention

Module 3: Epidemiological Study Designs

• Cross-sectional studies
• Cohort studies
• Case-control studies
• Interventional studies

Module 4: Data Collection Methods and Tools

• Surveys and questionnaires
• Observational and experimental data collection
• Introduction to ODK for mobile data collection

Module 5: Data Management and Quality Assurance with ODK

• Designing data collection forms in ODK
• Data validation and quality control procedures

Module 6: Introduction to Data Analysis with Excel

• Data entry and organization in Excel
• Basic data analysis using Excel

Module 7: Advanced Data Analysis with Excel

• Statistical analysis in Excel
• Data visualization and charting in Excel

Module 8: Data Visualization with Excel

• Creating informative and visually appealing charts and graphs in Excel

Module 9: Introduction to Biostatistics

• Fundamentals of biostatistics and its role in public health
• Descriptive statistics and data summarization

Module 10: Descriptive Biostatistics using SPSS

• Data management and analysis using SPSS
• Summary statistics and data visualization in SPSS

Module 11: Inferential Biostatistics using SPSS

• Hypothesis testing and p-values in SPSS
• Comparing means and proportions using SPSS

Module 12: Advanced Data Analysis with SPSS

• Regression analysis in SPSS
• ANOVA and post-hoc tests in SPSS

Module 13: Data Visualization with SPSS

• Creating interactive and publication-ready visualizations in SPSS

Module 14: Introduction to R Programming

• R fundamentals and data manipulation
• Data import and export in R

Module 15: Data Manipulation with R

• Data wrangling and cleaning in R
• Working with data frames and factors in R

Module 16: Data Visualization with R

• Creating customized plots and visualizations in R
• Geospatial visualization using R and GIS

Module 17: Basic Statistical Analysis with R

• Summary statistics and data distribution analysis in R
• Hypothesis testing and p-values in R

Module 18: Advanced Statistical Modeling with R

• Linear regression and multiple regression in R
• Logistic regression for binary outcomes in R

Module 19: Hypothesis Testing with R

• ANOVA and post-hoc tests in R
• Non-parametric tests in R

Module 20: Introduction to Python Programming

• Python fundamentals and data manipulation
• Data handling using pandas in Python

Module 21: Data Manipulation with Python

• Data cleaning and transformation in Python
• Working with pandas data frames in Python

Module 22: Data Visualization with Python

• Creating interactive visualizations with matplotlib and seaborn in Python
• Geospatial visualization using Python and GIS

Module 23: Basic Statistical Analysis with Python

• Descriptive statistics and data distribution analysis in Python
• Hypothesis testing and p-values in Python

Module 24: Advanced Statistical Modeling with Python

• Linear regression and polynomial regression in Python
• Logistic regression and decision tree classification in Python

Module 25: Hypothesis Testing with Python

• One-way ANOVA and Tukey's post-hoc test in Python
• Non-parametric tests in Python

Module 26: Introduction to Stata for Epidemiology

• Stata interface and basic commands
• Data management and analysis in Stata

Module 27: Data Management with Stata

• Data cleaning and transformation in Stata
• Handling missing data and outliers in Stata

Module 28: Basic Statistical Analysis with Stata

• Summary statistics and t-tests in Stata
• Chi-square tests in Stata

Module 29: Advanced Statistical Modeling with Stata

• Linear regression and multiple regression in Stata
• Logistic regression and survival analysis in Stata

Module 30: Hypothesis Testing with Stata

• Non-parametric tests in Stata
• Effect sizes and confidence intervals in Stata

Module 31: Introduction to NVivo for Qualitative Data Analysis

• Principles of qualitative research and data coding in NVivo
• Managing and analyzing qualitative data in NVivo

Module 32: Data Coding and Analysis using NVivo

• Thematic analysis and content analysis in NVivo
• Mixed-methods research and data integration in NVivo

Module 33: Data Visualization with Power BI

• Introduction to Power BI and its features
• Creating interactive dashboards and reports

Module 34: Geographic Information Systems (GIS) in Epidemiology

• Introduction to GIS and spatial data analysis
• Mapping health data using GIS tools

Module 35: Spatial Data Analysis and Mapping

• Spatial analysis techniques and geospatial statistics
• Creating informative maps for epidemiological research

Module 36: Application of Epidemiology and Biostatistics in Public Health Research

• Conducting epidemiological studies and interpreting research findings
• Applying biostatistical methods to real-world public health scenarios

Module 37: Ethical Considerations in Epidemiology and Biostatistics

• Ethical principles in research and data analysis
• Protecting participant confidentiality and data privacy

Module 38: Integration of Tools: R, Python, Stata, SPSS, NVivo, Excel, Power BI, and GIS

• Utilizing the strengths of different software for comprehensive data analysis
• Integrating data from various sources for a holistic approach

Module 39: Handling Large Datasets and Big Data Analytics

• Managing and analyzing large-scale epidemiological datasets
• Big data sources and challenges in epidemiological research

Module 40: Capstone Project and Presentation

• Applying knowledge and skills acquired throughout the course to a real-world epidemiological research project
• Presenting the capstone project to peers and instructors

General Notes

• All our courses can be Tailor-made to participants' needs
• The participant must be conversant in English
• Presentations are well-guided, practical exercises, web-based tutorials, and group work. Our facilitators are experts with more than 10 years of experience.
• Upon completion of training the participant will be issued with a Foscore development center certificate (FDC-K)
• Training will be done at the Foscore development center (FDC-K) centers. We also offer inhouse and online training on the client schedule
• Course duration is flexible and the contents can be modified to fit any number of days.
• The course fee for onsite training includes facilitation training materials, 2 coffee breaks, a buffet lunch, and a Certificate of successful completion of Training. Participants will be responsible for their own travel expenses and arrangements, airport transfers, visa application dinners, health/accident insurance, and other personal expenses.
• Accommodation, pickup, freight booking, and Visa processing arrangement, are done on request, at discounted prices.
• Tablet and Laptops are provided to participants on request as an add-on cost to the training fee.
• One-year free Consultation and Coaching provided after the course.
• Register as a group of more than two and enjoy a discount of (10% to 50%)
• Payment should be done before commence of the training or as agreed by the parties, to the FOSCORE DEVELOPMENT CENTER account, so as to enable us to prepare better for you.
• For any inquiries reach us at training@fdc-k.org or +254712260031
• Website:www.fdc-k.org