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Mathematical Biostatistics Boot Camp 1

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

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What's inside

Syllabus

Introduction, Probability, Expectations, and Random Vectors

You are about to undergo an intense and demanding immersion into the world of mathematical biostatistics. Over the next few weeks, you will learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and much more. Module 1 covers experiments, probability, variables, mass functions, density functions, cumulative distribution functions, expectations, variations, and vectors.

Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics

This module covers Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics. These are the most fundamental core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.

Confidence Intervals, Bootstrapping, and Plotting

This module covers Confidence Intervals, Bootstrapping, and Plotting. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.

Binomial Proportions and Logs

This module covers Binomial Proportions and Logs. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a foundational knowledge of probability and statistics, essential for data analysis

Introduces concepts at an introductory level, making it suitable for students with a junior or senior college-level mathematical background

While a small amount of linear algebra and programming knowledge is useful, it is not a requirement, expanding the accessibility of the course

Covers a comprehensive range of topics, including conditional probability, Bayes' Rule, confidence intervals, and binomial proportions, providing a thorough foundation in mathematical biostatistics

Emphasizes core concepts such as distributions, asymptotics, and plotting, which are crucial for understanding and interpreting statistical data

Taught by Brian Caffo, PhD, an esteemed professor with expertise in biostatistics, ensuring the quality and accuracy of the content

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Reviews summary

Mathematics statistics in biostatistics

According to students, Mathematical Biostatistics Boot Camp 1 is a challenging, well-documented, and engaging course that does a good job covering topics in mathematical statistics. It's most suitable for learners with prior exposure to this material looking to refresh or broaden their knowledge. However, learners new to this material or without a calculus background may find this course difficult.

Well-documented materials

"Very enjoyable!"

"Clearly more than intermediate class which tends to mathematics statistics, but very well documented and presented materials by professor Caffo."

Prior exposure to statistics

"Still, as someone with a STEM background but not in biology, I found this course to be excellent."

"This course is most helpful if you already have prior exposure to the basic concepts of statistics but want a quick refresher to bring yourself back up to speed."

Emphasis on calculus

"I am hampered at the moment by trying to learn the calculus required to complete the quizzes."

Challenging material

"This is a good course on basic statistics but it's a very academic."

"This class moves extremely quickly over the high level topics of statistics."

"It is focused on applications and there are very few derivations or intuitions."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mathematical Biostatistics Boot Camp 1 with these activities:

Organize Course Materials for Efficient Study

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Enhance your learning efficiency by creating a structured study environment.

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Gather all relevant course materials.
Organize materials into logical categories.
Create a study schedule and stick to it.
Review and update your notes regularly.

Review Calculus and Linear Algebra Prerequisites

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Ensure a strong foundation in prerequisite mathematical concepts.

Browse courses on Calculus

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Review key concepts and formulas in calculus.
Refresh your understanding of linear algebra.
Practice solving relevant problems.

Review 'Probability and Statistical Inference' by Robert V. Hogg

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Supplement your understanding with a comprehensive reference text.

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Read the assigned chapters and take notes.
Solve practice problems and exercises.
Discuss key concepts with peers or the instructor.

Four other activities

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Follow Online Tutorials on Probability and Statistics Concepts

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Supplement your learning by seeking additional guidance from online tutorials.

Browse courses on Probability

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Identify specific areas where you need clarification.
Search for online tutorials or videos that cover these concepts.
Follow the tutorials and take notes.
Practice applying the concepts learned.

Solve Probability and Statistics Practice Problems

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Enhance your understanding of key concepts by practicing problem-solving.

Browse courses on Probability

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Review course materials to identify areas for practice.
Find online or textbook practice problems.
Solve problems and check your answers.
Seek clarification on any difficulties encountered.

Create Visualizations to Illustrate Probability and Statistics Concepts

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Solidify your understanding by creating visual representations of key concepts.

Browse courses on Probability

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Choose a probability or statistics concept you want to visualize.
Select an appropriate visualization technique.
Gather and prepare the necessary data.
Create the visualization using suitable software.
Analyze and interpret the visualization.

Develop a Statistical Model for a Real-World Problem

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Apply your knowledge to solve a practical problem and enhance your analytical skills.

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Identify a real-world problem that can be addressed using statistical modeling.
Gather relevant data and explore exploratory analysis.
Choose an appropriate statistical model and fit it to the data.
Evaluate the model's performance and make predictions.
Communicate your findings in a clear and concise manner.

Career center

Learners who complete Mathematical Biostatistics Boot Camp 1 will develop knowledge and skills that may be useful to these careers:

Biostatistician

A Biostatistician designs, analyzes, and interprets studies that involve human subjects and other elements in clinical research. Most biostatisticians work in the pharmaceutical industry and academia, but they may also find employment with government agencies. This course helps build a foundation for a career in biostatistics by teaching fundamental probability and statistical concepts used in elementary data analysis.

See salaries and explore the career path for Biostatistician

Data Scientist

A Data Scientist gathers, analyzes, and interprets large sets of data, extracting meaningful insights to solve business problems. They use statistical methods and machine learning algorithms to analyze data and present their findings to stakeholders. This course may be useful for aspiring data scientists as it covers probability, expectations, conditional probabilities, distributions, confidence intervals, and bootstrapping.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models to solve business problems. They use statistical methods and programming to build models that can learn from data and make predictions. This course may be useful for aspiring machine learning engineers as it covers probability, expectations, and distributions, which are fundamental concepts in machine learning.

See salaries and explore the career path for Machine Learning Engineer

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical methods to solve business problems. They develop and analyze models to improve efficiency and productivity in various industries, such as manufacturing, logistics, and healthcare. This course may be useful for aspiring operations research analysts as it covers probability, expectations, and distributions, which are used to build models for optimization problems.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical methods to analyze financial data and make investment decisions. They develop and implement models to assess risk and return, and provide recommendations to clients. This course may be useful for aspiring quantitative analysts as it covers probability, expectations, and distributions, which are used to build models for financial analysis.

See salaries and explore the career path for Quantitative Analyst

Risk Manager

A Risk Manager identifies, assesses, and manages risks in various industries, such as finance, insurance, and healthcare. They use statistical methods to quantify risks and develop strategies to mitigate them. This course may be useful for aspiring risk managers as it covers probability, expectations, and distributions, which are used to assess and manage risks.

See salaries and explore the career path for Risk Manager

Statistician

A Statistician collects, analyzes, and interprets data to solve problems in various fields, such as science, engineering, and business. They use statistical methods to design experiments, analyze data, and draw conclusions. This course may be useful for aspiring statisticians as it covers fundamental probability and statistical concepts used in elementary data analysis.

See salaries and explore the career path for Statistician

Data Analyst

A Data Analyst collects, analyzes, and interprets data to solve business problems. They use statistical methods and programming to analyze data and present their findings to stakeholders. This course may be useful for aspiring data analysts as it covers probability, expectations, and distributions, which are used to analyze data and draw conclusions.

See salaries and explore the career path for Data Analyst

Market Researcher

A Market Researcher conducts research to understand consumer behavior and market trends. They use statistical methods to analyze data and provide insights to businesses. This course may be useful for aspiring market researchers as it covers probability, expectations, and distributions, which are used to analyze market data and draw conclusions.

See salaries and explore the career path for Market Researcher

Business Analyst

A Business Analyst analyzes business processes and systems to identify opportunities for improvement. They use statistical methods to analyze data and make recommendations to businesses. This course may be useful for aspiring business analysts as it covers probability, expectations, and distributions, which are used to analyze business data and draw conclusions.

See salaries and explore the career path for Business Analyst

Financial Analyst

A Financial Analyst analyzes financial data to make investment recommendations. They use statistical methods to assess risk and return, and provide recommendations to clients. This course may be useful for aspiring financial analysts as it covers probability, expectations, and distributions, which are used to analyze financial data and draw conclusions.

See salaries and explore the career path for Financial Analyst

Actuary

An Actuary analyzes financial data to assess risk and develop insurance policies. They use statistical methods to calculate premiums and determine the probability of events. This course may be useful for aspiring actuaries as it covers probability, expectations, and distributions, which are used to analyze financial data and calculate risks.

See salaries and explore the career path for Actuary

Epidemiologist

An Epidemiologist studies the distribution and determinants of health-related states or events in specified populations. They use statistical methods to analyze data and make recommendations for public health interventions. This course may be useful for aspiring epidemiologists as it covers probability, expectations, and distributions, which are used to analyze health data and draw conclusions.

See salaries and explore the career path for Epidemiologist

Clinical Trial Manager

A Clinical Trial Manager plans, conducts, and manages clinical trials to evaluate the safety and efficacy of new drugs and treatments. They use statistical methods to design studies and analyze data. This course may be useful for aspiring clinical trial managers as it covers probability, expectations, and distributions, which are used to design and analyze clinical trials.

See salaries and explore the career path for Clinical Trial Manager

Quality Assurance Analyst

A Quality Assurance Analyst ensures that products and services meet quality standards. They use statistical methods to analyze data and identify areas for improvement. This course may be useful for aspiring quality assurance analysts as it covers probability, expectations, and distributions, which are used to analyze data and draw conclusions.

See salaries and explore the career path for Quality Assurance Analyst

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Mathematical Biostatistics Boot Camp 1.

Fundamentals of Machine Learning for Predictive...

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Provides a comprehensive overview of mathematical statistics and data analysis, covering topics such as probability, distributions, estimation, hypothesis testing, and regression analysis. It valuable reference for students and researchers in biostatistics and related fields.

Mathematical Biostatistics Boot Camp 1

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Mathematics statistics in biostatistics

Activities

Career center

Reading list

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