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Updated in [February 21st, 2023]

Introduction to the Course | Fundamentals of Biostatistics.
Introduction | Fundamentals of Biostatistics.
Summaries of Data | Lecture 1 | Fundamentals of Biostatistics #StudyAtHome.
Summaries of Data | Lecture 2 | Fundamentals of Biostatistics #StudyAtHome #WithMe.
Summaries of Data | Lecture 3 | Fundamentals of Biostatistics #StudyAtHome #WithMe.
Probability | Lecture 1 | Fundamentals of Biostatistics.
Probability | Lecture 2 | Fundamentals of Biostatistics.
Introduction, Random Variables & Expected Value | Discrete Probability Distributions | Biostatistics.
Permutations and Combinations | Discrete Probability Distributions | Biostatistics.
Binomial and Poisson Distribution | Discrete Probability Distributions | Biostatistics.
Normal Distributions | Continuous Probability Distributions | Biostatistics.
Normal Approximation of Binomial Distribution | Continuous Probability Distribution | Biostatistics.
Sampling Distributions | Lecture 1 | Fundamentals of Biostatistics.
Sampling Distributions | Lecture 2 | Fundamentals of Biostatistics.
Statistical Estimation | Lecture 1 | Biostatistics.
Statistical Estimation | Lecture 2 | Fundamentals of Biostatistics.
Hypothesis Testing: One Sample Inference | Lecture 1 | Fundamentals of Biostatistics.
Hypothesis Testing: One Sample Inference | Lecture 2 | Fundamentals of Biostatistics.
Power and Sample Size Determination of One Sample Test | Hypothesis Testing | Biostatistics.
Hypothesis Testing | Paired t-Test | Biostatistics #StudyAtHome.
Hypothesis Testing | Two Sample t-Test | Biostatistics #StudyAtHome.
Hypothesis Testing | Estimation of Power and Sample Size | Biostatistics #StudyAtHome.
Cohort Studies | Biostatistics, Epidemiology and Public Health #StudyAtHome.
Case - Control Studies | Biostatistics, Epidemiology and Public Health #StudyAtHome.
Cross Sectional Studies | Biostatistics, Epidemiology and Public Health #StudyAtHome.
Hypothesis Testing | Categorical Data | Chi - Squared Distribution | Biostatistics #StudyAtHome.
Hypothesis Testing: Single and Two Population Variances | Fundamentals of Biostatistics.
One Way ANOVA and Completely Randomized Experimental Design | Fundamentals of Biostatistics.

(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
1. You can gain a comprehensive understanding of biostatistics fundamentals, including data summaries, probability, sampling distributions, statistical estimation, hypothesis testing, power and sample size determination, cohort studies, case-control studies, cross-sectional studies, categorical data, chi-squared distribution, single and two population variances, one-way ANOVA and completely randomized experimental design.

2. You can learn how to summarize data, calculate probability, and understand the concept of sampling distributions.

3. You can gain knowledge of hypothesis testing, including one-sample inference, paired t-test, two-sample t-test, estimation of power and sample size, and categorical data.

4. You can understand the concepts of cohort studies, case-control studies, cross-sectional studies, and one-way ANOVA and completely randomized experimental design.

5. You can learn how to apply biostatistics to epidemiology and public health.

[Applications]
The application of the Fundamentals of Biostatistics course can be seen in various fields such as epidemiology, public health, and data analysis. Students can use the knowledge gained from this course to understand the basics of biostatistics and apply it to their research. Additionally, the course can be used to gain a better understanding of the different types of probability distributions, sampling distributions, hypothesis testing, and statistical estimation. Furthermore, the course can be used to gain an understanding of the different types of studies such as cohort studies, case-control studies, and cross-sectional studies. Finally, the course can be used to gain an understanding of the different types of hypothesis testing such as chi-squared distribution, single and two population variances, and one-way ANOVA.

[Career Paths]
Three job positions recommended to learners of this course are:

1. Biostatistician: Biostatisticians use their knowledge of mathematics, statistics, and computer science to analyze data and develop models to help solve problems in the field of healthcare. They are responsible for designing and conducting studies, analyzing data, and interpreting results. As the healthcare industry continues to grow, the demand for biostatisticians is expected to increase.

2. Epidemiologist: Epidemiologists use their knowledge of biostatistics to study the causes and effects of diseases and other health-related issues. They use data to identify trends and develop strategies to prevent and control the spread of diseases.

3. Data Scientist: Data scientists use their knowledge of biostatistics to analyze large datasets and develop models to help organizations make better decisions. They are responsible for collecting, cleaning, and analyzing data, as well as developing algorithms and predictive models. As the demand for data-driven insights continues to grow, the demand for data scientists is expected to increase.