Introduction to Statistics & Data Analysis in Public Health

Go to Course: https://www.coursera.org/learn/introduction-statistics-data-analysis-public-health

Introduction

### Course Review: Introduction to Statistics & Data Analysis in Public Health #### Overview If you are looking to enhance your understanding of statistics within the context of public health, the **Introduction to Statistics & Data Analysis in Public Health** course on Coursera is a fantastic choice. This course is designed to provide learners with a foundational comprehension of key statistical concepts, enabling you to independently analyze data sets, draw meaningful conclusions, and contribute valuable insights within the public health field. The course’s focus on real-world applications and hands-on experience with statistical software prepares students not just to understand theory but also to apply their knowledge in practical scenarios. By the end of the course, you will have developed the skills necessary to conduct fundamental analyses and formulate hypotheses based on data. #### Syllabus Breakdown 1. **Introduction to Statistics in Public Health**: The course kicks off by highlighting the pivotal role of statistics in public health research. Through historical examples, such as studies from eighteenth-century London and contemporary cases by the United Nations, learners will appreciate the evolution and significance of statistical methods in public health. The practical exercise of defining a research question from real news stories offers a tangible way to connect theory with practice. 2. **Types of Variables, Common Distributions and Sampling**: This module dives into the essential components of statistical analysis, covering types of variables, common distributions, and sampling methods. Through engaging discussions, you will distinguish between "well-behaved" data distributions, like the normal and Poisson distributions, and the complexities of real-world data often encountered in public health studies. 3. **Introduction to R and RStudio**: As statistical analysis shifts toward more sophisticated software tools, this course includes an introduction to R, a powerful and free statistical programming language, and its interface, RStudio. You will learn how to import datasets and perform basic descriptive analyses using relatable examples, like fruit and vegetable consumption. 4. **Hypothesis Testing in R**: Building on previously acquired knowledge, this module focuses on hypothesis testing, a cornerstone of statistical analysis. You will learn to craft testable hypotheses, use R to conduct hypothesis tests, and interpret results, including the calculation of p-values and confidence intervals. This knowledge is vital for making informed decisions based on sample data, especially in the context of public health metrics like body mass index (BMI). #### Why You Should Enroll The **Introduction to Statistics & Data Analysis in Public Health** course is highly recommended for anyone interested in the fields of public health, epidemiology, or data analysis. Here’s why you should consider enrolling: - **Comprehensive Curriculum**: The course covers essential topics that build a strong foundation in statistics, emphasizing practical application in public health. - **Hands-On Learning**: With guided exercises using R and RStudio, you will gain experience in statistical programming that is highly valued in the job market. - **Real-World Relevance**: The use of current and historical examples illustrates the direct impact of statistical analysis on public health, making complex concepts more relatable and easier to grasp. - **Flexible Learning**: Offered on Coursera, you can learn at your own pace, allowing you to juggle other commitments while acquiring valuable skills. #### Conclusion In conclusion, the **Introduction to Statistics & Data Analysis in Public Health** course is a perfect blend of theoretical knowledge and practical application, making it an excellent choice for anyone looking to enhance their skill set in public health data analysis. Whether you're a student, a professional, or a public health enthusiast, this course will provide you with the tools necessary to make data-driven decisions and contribute to crucial public health efforts. Don’t miss the opportunity to enrich your understanding and capability in a field where data is paramount! Enroll today and take a step towards mastering the essential skills of statistics and data analysis in public health.

Syllabus

Introduction to Statistics in Public Health

Statistics has played a critical role of in public health research and practice, and you’ll start by looking at two examples: one from eighteenth century London and the other by the United Nations. The first task in carrying out a research study is to define the research question and express it as a testable hypothesis. With examples from the media, you’ll see what does and does not work in this regard, giving you a chance to define a research question from some real news stories.

This module will introduce you to some of the key building blocks of knowledge in statistical analysis: types of variables, common distributions and sampling. You’ll see the difference between “well-behaved” data distributions, such as the normal and the Poisson, and real-world ones that are common in public health data sets.

Now it’s time to get started with the powerful and completely free statistical software R and its popular interface RStudio. With the example of fruit and vegetable consumption, you’ll learn how to download R, import the data set and run essential descriptive analyses to get to know the variables.

Having learned how to define a research question and testable hypothesis earlier in the course, you’ll learn how to apply hypothesis testing in R and interpret the result. As all medical knowledge is derived from a sample of patients, random and other kinds of variation mean that what you measure on that sample, such as the average body mass index, is not necessarily the same as in the population as a whole. It’s essential that you incorporate this uncertainty in your estimate of average BMI when presenting it. This involves the calculation of a p value and confidence interval, fundamental concepts in statistical analysis. You’ll see how to do this for averages and proportions.