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Course Overview:

This beginner-level course provides an introduction to the core concepts of Biostatistics and R Programming for data analysis, with a focus on applications in public health, medicine, and biology. You will learn the foundational principles of biostatistics, covering essential topics such as data types, measures of central tendency, probability distributions, and hypothesis testing. The course will also guide you through the basics of R programming, enabling you to perform statistical analyses and create data visualizations effectively. By the end, you’ll be equipped with practical skills in biostatistics and R programming to analyze real-world medical and biological data.

 

Eligible Audience:

  • Beginners in Biostatistics: Students and professionals starting their journey in biostatistics.
  • Public Health and Medicine Professionals: Individuals working in public health, epidemiology, or medical research who want to enhance their statistical skills.
  • Aspiring Data Analysts: Anyone interested in learning data analysis using R programming for scientific applications.
  • Life Sciences Students: Students in biology, medicine, or related fields seeking to understand and apply biostatistics in their studies.

 

Course Outcomes:

By the end of this course, participants will be able to:

  • Understand and apply basic biostatistics concepts to real-world research problems.
  • Use R programming to analyze data and perform basic statistical tests.
  • Visualize data effectively to communicate statistical findings.
  • Perform hypothesis testing, including Chi-Square, T-tests, and correlation analysis.
  • Gain hands-on experience with data analysis techniques commonly used in public health, medicine, and biology.

 

Course Outline:

Module 1: Introduction to Biostatistics

  • Definition and Importance: Understand what biostatistics is and why it’s essential in research and public health.
  • Applications in Research: Explore how biostatistics is applied in medicine, biology, and public health studies.

Module 2: Basic Statistical Concepts

  • Types of Data: Learn the difference between qualitative vs. quantitative and discrete vs. continuous data.
  • Scales of Measurement: Understand nominal, ordinal, interval, and ratio scales.
  • Population vs. Sample: Get familiar with the concepts of population and sample in statistical studies.
  • Parameters vs. Statistics: Learn the distinction between population parameters and sample statistics.

Module 3: Data Organization and Visualization

  • Data Collection Techniques: Overview of data collection methods used in biostatistics.
  • Frequency Distributions: Learn to organize data into frequency distributions.
  • Graphical Representation: Master the basics of visualizing data using bar charts, histograms, pie charts, box plots, and scatter plots.

Module 4: Descriptive Statistics

  • Measures of Central Tendency: Learn how to calculate and interpret mean, median, and mode.
  • Measures of Dispersion: Understand range, variance, standard deviation, and interquartile range.
  • Percentiles and Quartiles: Learn about data division and interpretation of percentiles and quartiles.

Module 5: Probability Basics

  • Introduction to Probability: Understand the fundamentals of probability theory.
  • Basic Rules of Probability: Learn about probability principles and their application.
  • Probability Distributions: Explore key probability distributions like binomial, normal, and uniform distributions.

Module 6: Introduction to R Programming

  • R Basics: Introduction to the R programming language, including data import, basic operations, and exploratory data analysis.
  • Basic Statistics in R: Learn to calculate measures of central tendency and dispersion using R.

Module 7: Hypothesis Testing in R

  • Chi-Square Test: Learn how to use the Chi-Square test for categorical data and interpret the results.
  • T-Test and Wilcoxon Test: Learn how to compare two groups using parametric and non-parametric tests in R.
  • Correlation Analysis: Perform Pearson and Spearman correlation tests to analyze relationships between variables.

Module 8: Data Visualization with ggplot2

  • ggplot2 Basics: Master the creation of advanced data visualizations such as histograms, boxplots, and scatter plots.
  • Effective Communication: Learn how to present your statistical findings clearly and accurately.

Module 9: Advanced R Techniques

  • Secret Functions: Discover hidden functions in R to enhance your data analysis skills.
  • Project Application: Work on a real-world project that applies all the concepts learned throughout the course.

 

 Instrutor:

 Dr. Reham Gad

  • Master’s degree in biostatistics – Medical Research Institute
  • Harvard Medical School -ECSRT Batch B
  • Pharmacy Pharmacotherapy Diploma [BPs Diploma with supervision of Alexandria pharmacists syndicate]
  • Bachelor degree – Faculty of  pharmacy, Alexandria University