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Updated in [April 29th, 2023]

This course, Foundations of Inference in R, will provide students with an introduction to the process of statistical inference. Students will learn how to draw conclusions about a population from a sample of data via a process known as statistical inference. Through the use of the R programming language, students will learn how to calculate p-values, confidence intervals, and other measures of statistical significance. By the end of the course, students will be able to use R to draw meaningful conclusions from data.

[Applications]
The application of this course is to provide a foundation for understanding the principles of inference in R. After completing this course, learners should be able to use R to draw conclusions about a population from a sample of data. They should be able to calculate p-values and confidence intervals, and be able to interpret the results of their analysis. Additionally, learners should be able to use R to test hypotheses and draw conclusions about the population from the data.

[Career Paths]
1. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover trends and insights. They use a variety of techniques, such as machine learning, statistical inference, and data visualization, to draw conclusions from data. Data Scientists are in high demand, as organizations are increasingly relying on data-driven decision making.

2. Statistician: Statisticians use statistical inference to draw conclusions from data. They use a variety of techniques, such as hypothesis testing, regression analysis, and experimental design, to analyze data and make predictions. Statisticians are in high demand, as organizations are increasingly relying on data-driven decision making.

3. Data Analyst: Data Analysts are responsible for analyzing data to uncover trends and insights. They use a variety of techniques, such as data mining, statistical inference, and data visualization, to draw conclusions from data. Data Analysts are in high demand, as organizations are increasingly relying on data-driven decision making.

4. Data Engineer: Data Engineers are responsible for designing and building data pipelines and systems to store, process, and analyze large datasets. They use a variety of techniques, such as distributed computing, statistical inference, and data modeling, to create efficient data systems. Data Engineers are in high demand, as organizations are increasingly relying on data-driven decision making.

[Education Paths]
1. Bachelor of Science in Statistics: A Bachelor of Science in Statistics is a four-year degree program that provides students with a comprehensive understanding of the principles and methods of statistical analysis. Students learn how to use statistical software to analyze data, interpret results, and draw conclusions. Additionally, they learn how to apply statistical methods to solve real-world problems. This degree is becoming increasingly popular as businesses and organizations rely more heavily on data-driven decision-making.

2. Master of Science in Data Science: A Master of Science in Data Science is a two-year degree program that focuses on the application of data science techniques to solve complex problems. Students learn how to use machine learning algorithms, data mining techniques, and statistical methods to analyze large datasets. Additionally, they learn how to develop predictive models and interpret results. This degree is becoming increasingly popular as businesses and organizations rely more heavily on data-driven decision-making.

3. Doctor of Philosophy in Statistics: A Doctor of Philosophy in Statistics is a four-year degree program that provides students with a comprehensive understanding of the principles and methods of statistical analysis. Students learn how to use statistical software to analyze data, interpret results, and draw conclusions. Additionally, they learn how to apply statistical methods to solve real-world problems. This degree is becoming increasingly popular as businesses and organizations rely more heavily on data-driven decision-making.

4. Master of Science in Applied Statistics: A Master of Science in Applied Statistics is a two-year degree program that focuses on the application of statistical methods to solve real-world problems. Students learn how to use statistical software to analyze data, interpret results, and draw conclusions. Additionally, they learn how to develop predictive models and interpret results. This degree is becoming increasingly popular as businesses and organizations rely more heavily on data-driven decision-making.