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Updated in [May 25th, 2023]

Statistics and R for the Life Sciences is a course that teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. Learners will gain an understanding of the basics of statistical inference, including computing p-values and confidence intervals, and will be able to analyze data with R code. Visualization techniques will be used to explore new data sets and determine the most appropriate approach. Robust statistical techniques will be discussed as alternatives when data do not fit assumptions required by the standard approaches. Through R scripts, learners will learn the basics of conducting reproducible research. The course is divided into seven parts, allowing learners to take the entire series or individual courses that interest them. The course is self-paced and is supported in part by NIH grant R25GM114818.

[Applications]
Upon completion of this course, students can apply their knowledge of the R programming language and statistical data analysis to their own research projects. They can use the skills they have learned to analyze data sets, create visualizations, and conduct reproducible research. Additionally, they can use the knowledge they have gained to develop their own R scripts and packages for data analysis. Furthermore, they can use the skills they have acquired to develop their own statistical models and apply them to their research.

[Career Paths]
Recommended Career Paths:

1. Data Scientist: Data Scientists use their knowledge of statistics, programming, and data analysis to uncover insights from large datasets. They are responsible for developing and implementing algorithms to analyze data, creating visualizations to communicate their findings, and developing predictive models to forecast future trends. Data Scientists are in high demand in many industries, and the demand is only increasing as more organizations recognize the value of data-driven decision making.

2. Bioinformatician: Bioinformaticians use their knowledge of biology, computer science, and statistics to analyze and interpret biological data. They are responsible for developing algorithms and software tools to analyze large datasets, such as genomic and proteomic data, and for interpreting the results of these analyses. Bioinformaticians are in high demand in the life sciences, and the demand is only increasing as more organizations recognize the value of data-driven decision making.

3. Statistical Programmer: Statistical Programmers use their knowledge of statistics and programming to develop software tools for data analysis. They are responsible for developing algorithms to analyze data, creating visualizations to communicate their findings, and developing predictive models to forecast future trends. Statistical Programmers are in high demand in many industries, and the demand is only increasing as more organizations recognize the value of data-driven decision making.

4. Data Analyst: Data Analysts use their knowledge of statistics and programming to analyze data and uncover insights. They are responsible for developing algorithms to analyze data, creating visualizations to communicate their findings, and developing predictive models to forecast future trends. Data Analysts are in high demand in many industries, and the demand is only increasing as more organizations recognize the value of data-driven decision making.

[Education Paths]
Recommended Degree Paths:

1. Bachelor of Science in Statistics: This degree program provides students with a comprehensive understanding of statistical methods and techniques, as well as the ability to apply them to real-world problems. Students learn how to design experiments, analyze data, and interpret results. They also gain an understanding of the ethical implications of data analysis and the importance of accuracy and precision. This degree is ideal for those interested in pursuing a career in data science, analytics, or research.

2. Master of Science in Data Science: This degree program provides students with the skills and knowledge necessary to analyze and interpret large datasets. Students learn how to use data to make decisions, develop predictive models, and create visualizations. They also gain an understanding of the ethical implications of data analysis and the importance of accuracy and precision. This degree is ideal for those interested in pursuing a career in data science, analytics, or research.

3. Doctor of Philosophy in Statistics: This degree program provides students with an in-depth understanding of statistical methods and techniques, as well as the ability to apply them to real-world problems. Students learn how to design experiments, analyze data, and interpret results. They also gain an understanding of the ethical implications of data analysis and the importance of accuracy and precision. This degree is ideal for those interested in pursuing a career in data science, analytics, or research.

4. Master of Science in Artificial Intelligence: This degree program provides students with the skills and knowledge necessary to develop and implement artificial intelligence (AI) systems. Students learn how to design and build AI systems, as well as how to use AI to solve complex problems. They also gain an understanding of the ethical implications of AI and the importance of accuracy and precision. This degree is ideal for those interested in pursuing a career in AI, machine learning, or robotics.