Big Data, Genes, and Medicine from Coursera

This course distills for you expert knowledge and skills mastered by professionals in Health Big Data Science and Bioinformatics. You will learn exciting facts about the human body biology and chemistry, genetics, and medicine that will be intertwined with the science of Big Data and skills to harness the avalanche of data openly available at your fingertips and which we are just starting to make sense of. We’ll investigate the different steps required to master Big Data analytics on real datasets, including Next Generation Sequencing data, in a healthcare and biological context, from preparing data for analysis to completing the analysis, interpreting the results, visualizing them, and sharing the results.

Needless to say, when you master these high-demand skills, you will be well positioned to apply for or move to positions in biomedical data analytics and bioinformatics. No matter what your skill levels are in biomedical or technical areas, you will gain highly valuable new or sharpened skills that will make you stand-out as a professional and want to dive even deeper in biomedical Big Data. It is my hope that this course will spark your interest in the vast possibilities offered by publicly available Big Data to better understand, prevent, and treat diseases.

What's inside

Syllabus

Genes and Data

After this module, you will be able to 1. Locate and download files for data analysis involving genes and medicine. 2. Open files and preprocess data using R language. 3. Write R scripts to replace missing values, normalize data, discretize data, and sample data.

Preparing Datasets for Analysis

After this module, you will be able to: 1. Locate and download files for data analysis involving genes and medicine. 2. Open files and preprocess data using R language. 3. Write R scripts to replace missing values, normalize data, discretize data, and sample data.

Finding Differentially Expressed Genes

After this module, you will be able to 1. Select features from highly dimensional datasets. 2. Evaluate the performance of feature selection methods. 3. Write R scripts to select features from datasets involving gene expressions.

Predicting Diseases from Genes

After this module, you will be able to 1. Build classification and prediction models. 2. Evaluate the performance of classification and prediction methods. 3. Write R scripts to classify and predict diseases from gene expressions.

Determining Gene Alterations

After this module, you will be able to 1. List different types of gene alterations. 2. Compare and contrast methods for detecting gene mutations. 3. Compare and contrast methods for detecting methylation. 4. Compare and contrast methods for detecting copy number variations. 5. Quantify genomic alterations. 6. Connect genomic alterations to differential expression of genes. 7. Write programs in R for determining gene alterations and their relationship with gene expression.

Clustering and Pathway Analysis

After this module, you will be able to 1. Find clusters in biomedical data involving genes.2. Analyze and visualize biological pathways. 3. Write R scripts for clustering and for pathway analysis.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides actionable skills that translate into improved healthcare diagnostics, therapeutics, and prognostics

Provides skills that are increasingly required in various roles in biology, clinical research, biomedical research, and medicine

Develops skills in preparing datasets, feature selection, and prediction models, all of which are high-demand skills

Provides skills in determining gene alterations, which are essential for understanding the genetic basis of diseases

Develops skills in clustering and pathway analysis, which are essential for understanding the relationships between genes and diseases

Requires students to come in with some background knowledge in biology, statistics, and computer science

Reviews summary

Bioinformatics basics in big data

Learners say that this course in bioinformatics is an introductory course for those who are new to big data and bioinformatics. Many state that the course is a great starting point for those who are interested in learning more about computer science in the context of big data and biological data. Students who are new to R may want to learn R before starting this course. Learners should note that some parts of this course may be outdated, especially in relation to cBioPortal and other external site references used in exams and quizzes. However, students who wish to understand the basics of bioinformatics may find this course to be a helpful overview.

The course provides a good overview of big data in genetics and medicine.

"A very very cool course and very well structured."

"is a good introduction for some subjects"

"The course is a nice overview of the field"

This is a course for beginners in bioinformatics and big data.

"For a completely new student in computers, like me, this course has provided me with some good basics of "R" and biological analytical tools."

"Good information and training course for the beginner."

"I really enjoyed the workshop and advanced R were taught."

The course provides practical skills using Jupyter Notebooks.

"Very practical and easy to follow."

"The hands-on with Jupyter Notebook was my favourite part of this course!"

"Great place to start for those wanting to obtain a bioinformatic skill base."

The instructor is knowledgeable and explains concepts clearly.

"The instructor shows proficency and explains really well."

"Me gustó mucho el curso y, la parte practica que es muy importante, es sencilla de entender y realizar"

"A very very cool course and very well structured."

Some course content is outdated.

"Outdated"

"explanation is only for the theoretical part"

"The course contents were released around 2015 and should be updated, especially on the instructions about cBioPortal, and the environment of the R version."

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 Big Data, Genes, and Medicine with these activities:

Review Concepts from Molecular Biology and Genetics

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Reviewing these foundational concepts will solidify your understanding of the terminology and principles that are essential for success in this course.

Browse courses on Molecular Biology

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Review your high school or undergraduate textbooks
Explore online resources like Khan Academy or Coursera
Take practice quizzes

Review concepts of genomics and molecular biology

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Start by reviewing basic concepts of genomics and molecular biology to set a strong foundation for the course.

Browse courses on Genomics

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Refer to textbooks or online resources to review the structure and function of nucleic acids.
Go through notes or watch videos to refresh your memory on DNA replication, transcription, and translation.
Complete practice questions or quizzes to test your understanding of gene expression and regulation.

Practice data analysis on simulated datasets

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Practice data analysis techniques on simulated datasets to familiarize yourself with the tools and methods used in the course.

Browse courses on Data Analysis

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Load simulated datasets into statistical software or programming environment.
Apply data analysis techniques to preprocess, explore, and visualize data.
Interpret results and draw conclusions from the analyzed data.

Four other activities

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Participate in online discussion forums or study groups

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Engage with peers to discuss course topics, share insights, and clarify concepts.

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Join online discussion forums or study groups related to the course.
Actively participate in discussions by asking questions, sharing perspectives, and providing support.
Collaborate with peers on assignments or projects.

Follow tutorials on specific bioinformatics tools and algorithms

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Supplement your learning by following tutorials that provide hands-on experience with bioinformatics tools and algorithms.

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Identify specific bioinformatics tools or algorithms relevant to the course.
Find tutorials or online courses that provide step-by-step instructions.
Follow the tutorials and practice using the tools or algorithms on provided datasets.
Explore additional resources or documentation to deepen your understanding.

Review 'Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins'

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Delve deeper into the concepts covered in the course by reading this highly regarded textbook.

View Bioinformatics: A Practical Guide To The... on Amazon

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Read through the relevant chapters and sections of the book.
Take notes and highlight important concepts.
Complete the exercises or review questions at the end of each chapter to test your understanding.

Volunteer in a laboratory or research setting related to biomedical data science

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Gain practical experience and apply your knowledge in a real-world setting.

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Identify opportunities to volunteer in laboratories or research institutions.
Apply for positions and undergo any necessary training.
Assist with data collection, analysis, or other tasks under the guidance of experienced professionals.

Career center

Learners who complete Big Data, Genes, and Medicine will develop knowledge and skills that may be useful to these careers:

Biomedical Data Analyst

A Biomedical Data Analyst collects, analyzes, and interprets data to identify trends and patterns in the field of medicine. They use statistical methods to identify relationships between variables and develop predictive models. A course in Big Data, Genes, and Medicine can help a Biomedical Data Analyst to develop the skills needed to work with large datasets and use statistical methods to draw meaningful conclusions from them.

See salaries and explore the career path for Biomedical Data Analyst

Computational Biologist

A Computational Biologist uses computational methods to study biological systems. They develop and use mathematical models and simulations to understand how biological systems work and to predict how they will respond to changes in the environment. A course in Big Data, Genes, and Medicine can help a Computational Biologist to develop the skills needed to work with large datasets and use computational methods to identify patterns and trends in biological data.

See salaries and explore the career path for Computational Biologist

Research Scientist

A Research Scientist conducts research to develop new knowledge and technologies. They use their knowledge of science and medicine to develop new ways to diagnose, treat, and prevent diseases. A course in Big Data, Genes, and Medicine can help a Research Scientist to develop the skills needed to work with large datasets and use statistical methods to identify patterns and trends in scientific data.

See salaries and explore the career path for Research Scientist

Bioinformatician

A Bioinformatics works with computational tools and techniques to understand biological data. They use their knowledge of biology and computer science to develop new methods for analyzing and interpreting data from biological experiments. A course in Big Data, Genes, and Medicine can help a Bioinformatics to develop the skills needed to work with large datasets and use computational methods to identify patterns and trends in biological data.

See salaries and explore the career path for Bioinformatician

Physician

A Physician diagnoses and treats patients with various illnesses and diseases. They use their knowledge of medicine and science to provide the best possible care for their patients. A course in Big Data, Genes, and Medicine can help a Physician to develop the skills needed to understand medical data and use it to improve patient care.

See salaries and explore the career path for Physician

Data Scientist

A Data Scientist collects, analyzes, and interprets data to identify trends and patterns in various fields, including healthcare. They use statistical methods to identify relationships between variables and develop predictive models. A course in Big Data, Genes, and Medicine can help a Data Scientist to develop the skills needed to work with large datasets and use statistical methods to draw meaningful conclusions from them.

See salaries and explore the career path for Data Scientist

Science Writer

A Science Writer communicates complex scientific information to a non-scientific audience. They use their writing skills to explain scientific concepts in a clear and concise way. A course in Big Data, Genes, and Medicine can help a Science Writer to develop the skills needed to understand medical data and communicate it to a non-scientific audience.

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Pharmacist

A Pharmacist dispenses medications and provides information about their use. They work with physicians and other healthcare professionals to ensure that patients are taking their medications safely and effectively. A course in Big Data, Genes, and Medicine can help a Pharmacist to develop the skills needed to understand medication data and use it to improve patient care.

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Regulatory Affairs Specialist

A Regulatory Affairs Specialist ensures that medical products are safe and effective before they are marketed. They work with regulatory agencies to review clinical data and ensure that products meet all safety and efficacy requirements. A course in Big Data, Genes, and Medicine can help a Regulatory Affairs Specialist to understand medical data and use it to ensure that medical products are safe and effective.

See salaries and explore the career path for Regulatory Affairs Specialist

Genetic Counselor

A Genetic Counselor provides information and support to individuals and families affected by genetic disorders. They help individuals and families understand the risks and benefits of genetic testing and make informed decisions about their healthcare. A course in Big Data, Genes, and Medicine can help a Genetic Counselor to develop the skills needed to understand genetic data and provide accurate information to individuals and families.

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Biostatistician

A Biostatistician designs and analyzes statistical studies in the medical field. They use statistical methods to interpret data and make recommendations. A course in Big Data, Genes, and Medicine may be useful to a Biostatistician, as it can help them to develop the skills needed to analyze large datasets and draw meaningful conclusions from them.

See salaries and explore the career path for Biostatistician

Epidemiologist

An Epidemiologist investigates the causes and distribution of disease in populations. They use statistical methods to analyze data and identify risk factors for disease. A course in Big Data, Genes, and Medicine can help an Epidemiologist to develop the skills needed to work with large datasets and use statistical methods to identify patterns and trends in disease data.

See salaries and explore the career path for Epidemiologist

Medical Physicist

A Medical Physicist uses physics principles to develop and use medical imaging technologies. They work with physicians and other healthcare professionals to ensure that medical imaging is used safely and effectively. A course in Big Data, Genes, and Medicine can help a Medical Physicist to develop the skills needed to understand medical imaging data and use it to improve patient care.

See salaries and explore the career path for Medical Physicist

Public Health Analyst

A Public Health Analyst collects, analyzes, and interprets data to identify and address public health issues. They use statistical methods to identify trends and patterns in data and develop recommendations for public health policy. A course in Big Data, Genes, and Medicine can help a Public Health Analyst to develop the skills needed to work with large datasets and use statistical methods to identify patterns and trends in public health data.

See salaries and explore the career path for Public Health Analyst

Clinical Research Associate

A Clinical Research Associate manages clinical trials, which are research studies that evaluate the safety and effectiveness of new drugs and treatments. They work with investigators, patients, and other healthcare professionals to ensure that clinical trials are conducted according to protocol. A course in Big Data, Genes, and Medicine can help a Clinical Research Associate to develop the skills needed to manage clinical trials and ensure that data is collected and analyzed accurately.

See salaries and explore the career path for Clinical Research Associate

Big Data, Genes, and Medicine

What's inside

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Reviews summary

Bioinformatics basics in big data

Activities

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