Doing Clinical Research: Biostatistics with the Wolfram Language from Coursera

This course aims to empower you to do statistical tests, ready for incorporation into your dissertations, research papers, and presentations. The ability to summarize data, create plots and charts, and to do the statistical tests that you commonly see in the literature is a powerful skill indeed.

There are powerful tools readily available to achieve these goals. None are quite as easy to learn, yet as powerful to use, as the Wolfram Language. Knowledge is literally built into the language. With its well-structured and consistent approach to creating code, you will become an expert in no time.

This course follows the approach of learning statistical analysis through the use of a computer language. It requires no prior knowledge of coding. An exciting journey awaits. If you want even more, there are optional Honors lessons on machine learning that cover the support in the Wolfram Language for deep learning.

What's inside

Syllabus

Week 1

This first week establishes the aims of the course and motivation for using the Wolfram Language. We aim to support you in gaining a remarkable new set of skills for doing statistical analysis that you can continue to use long after you complete the course. We will also describe the process of procuring the software that you will use in the course. The first is the absolutely free version, which is software as a service, meaning it runs in any web browser. The second is the desktop version. If you work or study at an institution with a site licence, you will be able to get the software for free. There is also the option to purchase your own licence.

Week 2

In week 2, we start with some actual coding, now that you know about the Wolfram Language and its different coding environments. We start off with a demonstration of a completed project. It is just a little teaser, showcasing what you will be able to do at the end. Next, we are going to learn to code by doing simple arithmetic. That is simple addition, subtraction, multiplication, and so on. Once you have realized just how simple these tasks are, you will be introduced to the way in which data is stored in a computer language. These are the stepping stone required to bringing in your own data, ready for the analyses in the following weeks.

Week 3

In week 3, its time to start analyzing data, now that you can write some code and import your data. The two most important steps to understand the message hidden in data, are to summarize and visualize it. Descriptive statistics turn rows and columns of data into something that we as humans can understand. By summarizing values and replacing them with single values, we start to get an idea of what our analyses might show. Visualizing the data is an even better way of getting to grips with data. Box-and-whisker plots, scatter plots, bar charts, and the like are wonderful ways to augment your understanding of the data. The Wolfram Language makes summary statistics easy but it really shines when creating plots. There are almost no limits to customizing plots. No matter what your project requirements, you will learn to create plots that work for you. Starting this week is an optional Honors lessons that introduce machine learning using the Wolfram Language.

Week 4

This final week covers all the common statistical tests - going from Student's t-test to analysis of variance to chi-squared tests. We conclude the course with a run-through of the demonstration research project that you saw at the beginning of week two. This brings together all the skills that you have acquired during the course and prepares you for the final exam. You will also have the opportunity to create your own computational essay, if you are not content with just working through the demonstration project. For those following the optional Honors lessons there is an introduction to deep learning using the Wolfram Language.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

An ideal fit for academics who need statistical analysis methods for their research papers

Develops statistical analysis skills using the powerful and easy-to-learn Wolfram Language

Offers optional lessons on machine learning for those seeking to expand their knowledge

This course has the potential to elevate your research and presentations through robust statistical analysis

Provides hands-on training by guiding you through real-world statistical analysis projects

Reviews summary

Wolfram language for biostatistics

Learners say Wolfram Language for Biostatistics has great and easy-to-follow presentations, practical exercises, and a knowledgeable instructor who explains concepts clearly.

Clear and well-presented

"Material is well presented and the instructor knows the material very well."

Engaging and practical coding exercises

"Easy to follow. great practical exercises"

Knowledgeable instructor

"Dr. Juan Klopper's explanations were clear and very helpful."

"Simply excellent class."

"His enthusiasm not just fro Mathematica / Wolram, but also for presenting results in a nice story (research essay) really comes through."

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 Doing Clinical Research: Biostatistics with the Wolfram Language with these activities:

Review the book 'Introductory Statistics with R' by Peter Bruce

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Reading this book will provide you with a solid foundation in statistical concepts and R programming.

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Read the chapters relevant to the topics covered in the course.
Work through the practice exercises at the end of each chapter.
Use the book as a reference during the course for further clarification.

Refresh your high school Algebra fundamentals

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Practicing essential pre-requisite Algebra skills can enhance your comprehension and success rate in this course.

Browse courses on Algebra

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Review your course notes or textbooks.
Take a practice quiz or test to identify areas that need improvement.
Seek help from a tutor or online resources if needed.

Follow online tutorials on statistical analysis

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Watching and following along with guided tutorials will help you grasp complex statistical concepts more easily and at your own pace.

Browse courses on Statistical Analysis

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Search for reputable online tutorials on statistical analysis.
Choose tutorials that cover the topics you need to reinforce.
Take notes and practice the concepts demonstrated in the tutorials.
Share your insights and ask questions in online forums related to the tutorials.

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Practice coding challenges and exercises

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Regular practice with coding problems will help you develop your programming skills and prepare you for the course's assignments.

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Find online coding challenges or exercises.
Set aside time each week to practice solving these challenges.
Review your solutions and identify areas for improvement.
Seek help from online forums or mentors if you get stuck.

Join a study group or discussion forum

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Engaging with peers in a study group or discussion forum can enhance your understanding through shared perspectives and collaborative problem-solving.

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Find or create a study group with classmates or online.
Set regular meeting times to discuss course material.
Collaborate on assignments and projects.

Attend a workshop on statistical software

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Attending a workshop will provide you with structured guidance, hands-on practice, and opportunities to interact with experts in statistical software.

Browse courses on Statistical Software

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Research and identify workshops related to statistical software.
Register for a workshop that aligns with your interests and learning goals.
Attend the workshop and actively participate in the activities and discussions.

Create a data visualization project

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Working on a data visualization project will provide you with hands-on experience applying the concepts learned in this course and enhance your portfolio.

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Choose a dataset and define your visualization goals.
Select appropriate visualization techniques and tools.
Create your visualization and analyze the results.
Present your project to others for feedback.

Write a blog post or article on a statistical topic

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Writing about statistical concepts will help you solidify your understanding, improve your communication skills, and contribute to the broader learning community.

Browse courses on Statistical Analysis

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Choose a statistical topic that interests you and aligns with the course content.
Research and gather information from credible sources.
Organize your thoughts and write a well-structured post or article.
Publish your writing on a blog or online platform.

Career center

Learners who complete Doing Clinical Research: Biostatistics with the Wolfram Language will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians apply mathematical and statistical techniques to collect, analyze, interpret, and present data. They work in a variety of industries, including healthcare, finance, and government. This course can help you develop the skills you need to be a successful statistician, including data analysis, statistical modeling, and data visualization.

See salaries and explore the career path for Statistician

Data Analyst

Data analysts use data to solve business problems. They work in a variety of industries, including healthcare, finance, and retail. This course can help you develop the skills you need to be a successful data analyst, including data cleaning, data analysis, and data visualization.

See salaries and explore the career path for Data Analyst

Data Scientist

Data scientists use data to build models that can predict future events. They work in a variety of industries, including healthcare, finance, and manufacturing. This course can help you develop the skills you need to be a successful data scientist, including data mining, machine learning, and artificial intelligence.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers build and maintain machine learning models. They work in a variety of industries, including healthcare, finance, and transportation. This course can help you develop the skills you need to be a successful machine learning engineer, including data engineering, machine learning, and deep learning.

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Business Analyst

Business analysts use data to help businesses make better decisions. They work in a variety of industries, including healthcare, finance, and manufacturing. This course can help you develop the skills you need to be a successful business analyst, including data analysis, data visualization, and project management.

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Financial Analyst

Financial analysts use data to make investment decisions. They work in a variety of industries, including investment banking, asset management, and hedge funds. This course can help you develop the skills you need to be a successful financial analyst, including data analysis, financial modeling, and valuation.

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Market Researcher

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Operations Research Analyst

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Risk Analyst

Risk analysts use data to identify and assess risks. They work in a variety of industries, including insurance, finance, and healthcare. This course can help you develop the skills you need to be a successful risk analyst, including data analysis, risk modeling, and risk management.

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Software Engineer

Software engineers design, develop, and maintain software applications. They work in a variety of industries, including healthcare, finance, and manufacturing. This course can help you develop the skills you need to be a successful software engineer, including programming, data structures, and algorithms.

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Quantitative Analyst

Quantitative analysts use data to develop trading strategies. They work in a variety of industries, including investment banking, asset management, and hedge funds. This course can help you develop the skills you need to be a successful quantitative analyst, including data analysis, financial modeling, and machine learning.

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Actuary

Actuaries use data to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course can help you develop the skills you need to be a successful actuary, including data analysis, risk modeling, and financial mathematics.

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Data Engineer

Data engineers build and maintain data pipelines. They work in a variety of industries, including healthcare, finance, and manufacturing. This course can help you develop the skills you need to be a successful data engineer, including data engineering, data warehousing, and data mining.

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Database Administrator

Database administrators design, implement, and maintain databases. They work in a variety of industries, including healthcare, finance, and manufacturing. This course can help you develop the skills you need to be a successful database administrator, including database design, database administration, and data security.

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Information Security Analyst

Information security analysts protect computer systems and networks from unauthorized access. They work in a variety of industries, including healthcare, finance, and manufacturing. This course can help you develop the skills you need to be a successful information security analyst, including data security, network security, and risk management.

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