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Brian Caffo, PhD
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
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OpenCourser.com/course/va2g74/mathematical
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What's inside
Syllabus
Hypothesis Testing
In this module, you'll get an introduction to hypothesis testing, a core concept in statistics. We'll cover hypothesis testing for basic one and two group settings as well as power. After you've watched the videos and tried the homework, take a stab at the quiz.
Read more
Syllabus
Hypothesis Testing
In this module, you'll get an introduction to hypothesis testing, a core concept in statistics. We'll cover hypothesis testing for basic one and two group settings as well as power. After you've watched the videos and tried the homework, take a stab at the quiz.
Read more
Two Binomials
In this module we'll be covering some methods for looking at two binomials. This includes the odds ratio, relative risk and risk difference. We'll discussing mostly confidence intervals in this module and will develop the delta method, the tool used to create these confidence intervals. After you've watched the videos and tried the homework, take a crack at the quiz!
Discrete Data Settings
In this module, we'll discuss testing in discrete data settings. This includes the famous Fisher's exact test, as well as the many forms of tests for contingency table data. You'll learn the famous observed minus expected squared over the expected formula, that is broadly applicable.
Techniques
This module is a bit of a hodge podge of important techniques. It includes methods for discrete matched pairs data as well as some classical non-parametric methods.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores statistical inference, a core concept in data science and machine learning
Taught by Brian Caffo, PhD, a recognized expert in statistics and biostatistics
Develops foundational skills in statistical inference and data analysis
Covers hypothesis testing, a crucial technique for making informed decisions from data
Examines the odds ratio and risk difference, essential measures for understanding the relationship between variables
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Save Mathematical Biostatistics Boot Camp 2 to your list so you can find it easily later:
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Reviews summary
Challenging and insightful biostatistics course
Learners say this advanced biostatistics course is challenging but insightful. The lectures are well received, though some say that the last week of the course was rushed and unclear. Some students also compliment the instructor, Brian Caffo, for his knowledge and enthusiasm, but advise that he focus more and present the material on a chalkboard instead of using slides
Brian Caffo is knowledgeable and enthusiastic, but could improve his focus and presentation.
"Brian Caffo clearly knows his stuff and is excited about the subject matter, but needs to zero in his focus."
Learners say this course is more demanding than its prerequisites, but still a good learning experience.
"I think that this course is a little bit more demanding than Mathematical Biostatistics Boot Camp 1(MBBC1)."
"This aside, I believe that this course is amazing for learning some more statistics and I like the insights provided by the instructor."
The slides used in the course have errors.
"There are tons of errors on slides throughout the lectures."
The last week of the course was rushed and unclear.
"The last week was a bit rushed and unclear."
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 2 with these
activities:
Summarize Previous Inferential Statistics Course
Show steps
Review the basic concepts of inferential statistics to enhance your understanding of the material covered in this course.
Browse courses on
Inferential Statistics
Show steps
-
Review notes and assignments from your previous inferential statistics course.
-
Read through your old textbook or course materials to refresh your memory on the key concepts.
-
Practice solving problems related to hypothesis testing, confidence intervals, and regression analysis.
Follow Online Tutorials on Hypothesis Testing
Show steps
Supplement your learning with online tutorials that provide step-by-step guidance on hypothesis testing procedures.
Browse courses on
Hypothesis Testing
Show steps
-
Search for reputable online platforms or YouTube channels offering tutorials on hypothesis testing.
-
Choose tutorials that align with your learning level and interests.
-
Watch the tutorials carefully and take notes on the key concepts and techniques.
Solve Practice Problems
Show steps
Regularly solve practice problems to reinforce the concepts of hypothesis testing, confidence intervals, and other statistical techniques.
Browse courses on
Hypothesis Testing
Show steps
-
Find practice problems in your textbook, online resources, or problem sets provided by your instructor.
-
Allocate specific time each week to work on these problems.
-
Seek help from classmates, the instructor, or a tutor if you encounter difficulties.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a Study Group with Classmates
Show steps
Collaborate with classmates in a study group to enhance your understanding through discussions and mutual support.
Browse courses on
Hypothesis Testing
Show steps
-
Reach out to classmates to form a study group.
-
Meet regularly to discuss course material, review concepts, and work on practice problems together.
-
Take turns leading the discussions and presenting your interpretations to the group.
Analyze Real-World Data
Show steps
Apply the concepts of hypothesis testing and statistical inference to real-world data to solidify your understanding.
Browse courses on
Data Analysis
Show steps
-
Identify a dataset of interest (e.g., from Kaggle, Quandl, or a government agency).
-
Formulate a research question and hypothesis to test.
-
Clean and prepare the data for analysis.
-
Perform statistical tests (e.g., t-test, chi-square test) to test your hypothesis.
-
Interpret the results and draw conclusions based on your findings.
Create Infographics or Visualizations
Show steps
Translate complex statistical concepts into visually appealing infographics or visualizations to aid in your comprehension and retention.
Browse courses on
Hypothesis Testing
Show steps
-
Identify key concepts or topics from the course material that you want to visualize.
-
Choose appropriate visual formats (e.g., charts, graphs, diagrams) to convey the information effectively.
-
Use clear and concise language to label and explain the visualizations.
Read 'Statistical Inference' by George Casella and Roger L. Berger
Show steps
Delve deeper into the concepts of statistical inference and hypothesis testing through a comprehensive textbook.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
-
Purchase or borrow a copy of the book.
-
Set aside dedicated time each week to read and study the material.
-
Take notes, highlight important passages, and work through the practice exercises.
Summarize Previous Inferential Statistics Course
Show steps
Review the basic concepts of inferential statistics to enhance your understanding of the material covered in this course.
Browse courses on
Inferential Statistics
Show steps
Show steps
Show steps
- Review notes and assignments from your previous inferential statistics course.
- Read through your old textbook or course materials to refresh your memory on the key concepts.
- Practice solving problems related to hypothesis testing, confidence intervals, and regression analysis.
Follow Online Tutorials on Hypothesis Testing
Show steps
Supplement your learning with online tutorials that provide step-by-step guidance on hypothesis testing procedures.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Search for reputable online platforms or YouTube channels offering tutorials on hypothesis testing.
- Choose tutorials that align with your learning level and interests.
- Watch the tutorials carefully and take notes on the key concepts and techniques.
Solve Practice Problems
Show steps
Regularly solve practice problems to reinforce the concepts of hypothesis testing, confidence intervals, and other statistical techniques.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Find practice problems in your textbook, online resources, or problem sets provided by your instructor.
- Allocate specific time each week to work on these problems.
- Seek help from classmates, the instructor, or a tutor if you encounter difficulties.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a Study Group with Classmates
Show steps
Collaborate with classmates in a study group to enhance your understanding through discussions and mutual support.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Reach out to classmates to form a study group.
- Meet regularly to discuss course material, review concepts, and work on practice problems together.
- Take turns leading the discussions and presenting your interpretations to the group.
Analyze Real-World Data
Show steps
Apply the concepts of hypothesis testing and statistical inference to real-world data to solidify your understanding.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a dataset of interest (e.g., from Kaggle, Quandl, or a government agency).
- Formulate a research question and hypothesis to test.
- Clean and prepare the data for analysis.
- Perform statistical tests (e.g., t-test, chi-square test) to test your hypothesis.
- Interpret the results and draw conclusions based on your findings.
Create Infographics or Visualizations
Show steps
Translate complex statistical concepts into visually appealing infographics or visualizations to aid in your comprehension and retention.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Identify key concepts or topics from the course material that you want to visualize.
- Choose appropriate visual formats (e.g., charts, graphs, diagrams) to convey the information effectively.
- Use clear and concise language to label and explain the visualizations.
Read 'Statistical Inference' by George Casella and Roger L. Berger
Show steps
Delve deeper into the concepts of statistical inference and hypothesis testing through a comprehensive textbook.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of the book.
- Set aside dedicated time each week to read and study the material.
- Take notes, highlight important passages, and work through the practice exercises.
Career center
Learners who complete Mathematical Biostatistics Boot Camp 2 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make predictions. They work in a variety of industries, including technology, healthcare, and finance. Mathematical Biostatistics Boot Camp 2 can be useful for those interested in a career as a Data Scientist as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Data Analyst
Data Analyst
Data Analysts use statistical methods to analyze data and extract meaningful insights from it. They work in a variety of industries, including healthcare, finance, and marketing. Mathematical Biostatistics Boot Camp 2 can be useful for those interested in a career as a Data Analyst as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and government. Mathematical Biostatistics Boot Camp 2 can help those interested in a career as a Statistician as it can build a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. They design studies, collect data, and analyze results to identify risk factors and develop prevention strategies. Mathematical Biostatistics Boot Camp 2 can be useful for those interested in a career as an Epidemiologist as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to improve the efficiency of systems. They work in a variety of industries, including manufacturing, transportation, and healthcare. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as an Operations Research Analyst as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Risk Analyst
Risk Analyst
Risk Analysts use mathematical and statistical methods to assess risk and uncertainty. They work in a variety of industries, including finance, insurance, and healthcare. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as a Risk Analyst as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including biology, chemistry, and physics. They design and conduct experiments, analyze data, and publish their findings. Mathematical Biostatistics Boot Camp 2 can be useful for those interested in a career as a Research Scientist as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Business Analyst
Business Analyst
Business Analysts use data to analyze business processes and make recommendations for improvement. They work in a variety of industries, including technology, healthcare, and finance. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as a Business Analyst as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Clinical Research Associate
Clinical Research Associate
Clinical Research Associates (CRAs) manage clinical trials, ensuring that they are conducted in accordance with Good Clinical Practice (GCP) guidelines. They work with investigators, study coordinators, and other members of the research team to ensure that the trial is conducted safely and efficiently. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as a CRA as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Quality Assurance Specialist
Quality Assurance Specialist
Quality Assurance Specialists ensure that products and services meet quality standards. They develop and implement quality control procedures, and they monitor and evaluate the quality of products and services. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as a Quality Assurance Specialist as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Regulatory Affairs Specialist
Regulatory Affairs Specialist
Regulatory Affairs Specialists ensure that products and services comply with government regulations. They work with regulatory agencies to ensure that products are safe and effective, and they develop and implement compliance programs. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as a Regulatory Affairs Specialist as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to data in the fields of biology, medicine, and public health. They design studies, collect and analyze data, and interpret the results. Mathematical Biostatistics Boot Camp 2, in particular, can build a foundation in data analysis with a focus on one and two independent samples. This course may be useful for those interested in a career as a Biostatistician.
Medical Writer
Medical Writer
Medical Writers create written materials about medical products and services. They work with healthcare professionals, scientists, and other experts to develop accurate and informative materials. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as a Medical Writer as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and consulting. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as an Actuary as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including technology, healthcare, and finance. Mathematical Biostatistics Boot Camp 2 may be useful for those interested in a career as a Software Engineer as it can help with building a foundation in data analysis and statistical inference with a focus on one and two independent samples.
For more career information including salaries, visit:
OpenCourser.com/course/va2g74/mathematical
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 2.
A comprehensive textbook that covers the fundamentals of statistical methods used in psychology, including hypothesis testing, analysis of variance, and regression analysis.
A highly influential textbook that provides a comprehensive overview of modern statistical learning methods, including regression, classification, and clustering.
An introductory textbook that provides a solid foundation in biostatistics, with a focus on the application of statistical methods to health sciences research.
A textbook that provides a comprehensive overview of statistical methods used in medical research, with a focus on the design, conduct, and analysis of clinical trials.
A classic textbook that provides a comprehensive overview of statistical power analysis, with a focus on the concepts of effect size, sample size, and power.
A comprehensive textbook that provides an introduction to Bayesian data analysis, a statistical approach that uses probability theory to update beliefs in light of new evidence.
Save
A textbook that provides a comprehensive overview of epidemiology, with a focus on the application of epidemiological methods to public health research.
A comprehensive textbook that covers a wide range of nonparametric statistical methods, including hypothesis testing, confidence intervals, and regression analysis.
A textbook that provides a concise introduction to causal inference, a statistical approach to understand the relationship between cause and effect.
A textbook that provides a rigorous introduction to statistical modeling, with a focus on the development of statistical models for real-world applications.
A classic textbook that provides a comprehensive overview of reinforcement learning, a subfield of machine learning that deals with learning how to make decisions in an environment.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/va2g74/mathematical
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Johns Hopkins University
Instructor
Brian Caffo, PhD
Language
English
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This course belongs to a
collection
called
Advanced Statistics for Data Science. It's comprised of four courses:
- Mathematical Biostatistics Boot Camp 2 (viewing)
- Mathematical Biostatistics Boot Camp 1
- Advanced Linear Models for Data Science 1: Least Squares
- Advanced Linear Models for Data Science 2: Statistical Linear Models
Good to know
Explores statistical inference, a core concept in data science and machine learning
Taught by Brian Caffo, PhD, a recognized expert in statistics and biostatistics
Develops foundational skills in statistical inference and data analysis
Covers hypothesis testing, a crucial technique for making informed decisions from data
Examines the odds ratio and risk difference, essential measures for understanding the relationship between variables
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