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Isabelle Bichindaritz
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/bjb2fi/big
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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.
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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.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
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Reviews summary
Practical bioinformatics with r for biomedical data
According to learners, this course offers a largely positive experience for those seeking to apply Big Data techniques in biomedical and genomic contexts. Students frequently praise the incredibly knowledgeable instructor and the emphasis on hands-on R scripts and real-world datasets, which many found highly practical and job-relevant for careers in biomedical data analytics and bioinformatics. However, some learners with limited programming backgrounds found the pacing challenging and felt it assumed a higher level of prior R knowledge, suggesting a need for more foundational R lessons or supplementary resources for beginners.
Provides valuable skills for biomedical data analytics roles.
"Truly preparing me for practical applications in biomedical data analysis. I feel much more confident applying for roles in this field now."
"It's clear that a lot of effort went into designing the curriculum to be practical and job-relevant."
"This course has opened up new career pathways for me, allowing me to move into data analytics from biological sciences."
Led by a highly knowledgeable instructor with clear explanations.
"The instructor, Dr. El-Manzalawy, is incredibly knowledgeable and makes complex topics... very accessible."
"Dr. El-Manzalawy's lectures are engaging, and the way he connects biological concepts with data science techniques is brilliant."
"The instructor's ability to explain complex bioinformatics concepts in an understandable way was truly impressive."
Focuses on hands-on coding with real-world biomedical data.
"I particularly enjoyed the hands-on R scripts and real-world datasets."
"The focus on R for practical applications was what I appreciated most. The exercises reinforced the concepts really well."
"The practical exercises in R were great, though I occasionally found myself struggling with the exact syntax."
"I gained valuable skills to harness the avalanche of data available at my fingertips."
Content is relevant but some parts felt rushed or brief.
"The pacing was a bit fast at times, but the content is undeniably valuable for anyone serious about big data in medicine."
"The potential of this course is huge, but it felt a bit rushed."
"Some parts, particularly the more advanced statistical methods, could have been elaborated more."
May require prior R programming experience, challenging for beginners.
"My main feedback would be that some parts of the R coding could use more detailed explanation, especially for those less familiar with programming."
"For me, coming from a pure medical background with limited coding, it was a steep learning curve."
"It felt like it assumed a higher level of prior knowledge than stated, especially for someone who isn't already proficient in R."
"I struggled to follow some of the R coding segments, wishing for more foundational R lessons."
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
Show steps
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
Show steps
-
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
Show steps
Start by reviewing basic concepts of genomics and molecular biology to set a strong foundation for the course.
Browse courses on
Genomics
Show steps
-
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
Show steps
Practice data analysis techniques on simulated datasets to familiarize yourself with the tools and methods used in the course.
Browse courses on
Data Analysis
Show steps
-
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
Expand to see all activities and additional details
Show all seven activities
Participate in online discussion forums or study groups
Show steps
Engage with peers to discuss course topics, share insights, and clarify concepts.
Show steps
-
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
Show steps
Supplement your learning by following tutorials that provide hands-on experience with bioinformatics tools and algorithms.
Show steps
-
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'
Show steps
Delve deeper into the concepts covered in the course by reading this highly regarded textbook.
View
Bioinformatics: A Practical Guide To The...
on Amazon
Show steps
-
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
Show steps
Gain practical experience and apply your knowledge in a real-world setting.
Show steps
-
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.
Review Concepts from Molecular Biology and Genetics
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Start by reviewing basic concepts of genomics and molecular biology to set a strong foundation for the course.
Browse courses on
Genomics
Show steps
Show steps
Show steps
- 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
Show steps
Practice data analysis techniques on simulated datasets to familiarize yourself with the tools and methods used in the course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all seven activities
Participate in online discussion forums or study groups
Show steps
Engage with peers to discuss course topics, share insights, and clarify concepts.
Show steps
Show steps
Show steps
- 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
Show steps
Supplement your learning by following tutorials that provide hands-on experience with bioinformatics tools and algorithms.
Show steps
Show steps
Show steps
- 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'
Show steps
Delve deeper into the concepts covered in the course by reading this highly regarded textbook.
View
Bioinformatics: A Practical Guide To The...
on Amazon
Show steps
Show steps
Show steps
- 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
Show steps
Gain practical experience and apply your knowledge in a real-world setting.
Show steps
Show steps
Show steps
- 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:
Physician
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.
Biomedical Data Analyst
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.
Bioinformatician
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.
Research Scientist
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.
Computational Biologist
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.
Data Scientist
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.
Science Writer
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.
Genetic Counselor
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.
Pharmacist
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.
Regulatory Affairs Specialist
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.
Public Health Analyst
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.
Biostatistician
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.
Medical Physicist
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.
Epidemiologist
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.
Clinical Research Associate
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.
For more career information including salaries, visit:
OpenCourser.com/course/bjb2fi/big
Reading list
We've selected ten 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
Big Data, Genes, and Medicine.
Save
A book on deep learning that is written by leading researchers in the field. Useful for more advanced learners.
A comprehensive textbook on molecular biology that is commonly used as a reference by scientists and researchers in the field.
Provides a comprehensive overview of statistical methods used in bioinformatics, covering topics such as sequence analysis, gene expression analysis, and population genetics. It valuable resource for anyone who wants to learn more about the statistical methods used in bioinformatics.
Save
Provides an overview of algorithms used in bioinformatics, covering topics such as sequence alignment, gene finding, and phylogenetic tree construction. It valuable resource for anyone who wants to learn more about the algorithms used in bioinformatics.
Provides a comprehensive overview of bioinformatics, covering topics such as sequence analysis, gene expression analysis, and protein structure prediction. It valuable resource for anyone who wants to learn more about bioinformatics.
Provides an overview of the field of genomics, including topics such as DNA sequencing, gene expression, and genome editing. It good resource for anyone who wants to learn more about the basics of genomics.
A textbook on genetic analysis that is commonly used in undergraduate courses. Useful for learners who need additional background or review of the subject matter.
Save
A textbook on data mining that is commonly used in undergraduate and graduate courses. Useful for learners who need additional background or review of the subject matter.
Provides an overview of machine learning methods used in bioinformatics, covering topics such as supervised learning, unsupervised learning, and feature selection. It valuable resource for anyone who wants to learn more about the machine learning methods used in bioinformatics.
Save
A historical account of the discovery of the gene that is accessible to the general public. Useful as a supplement to provide context and deeper understanding and inspiration.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bjb2fi/big
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Rating
4.2
Effort
Approx. 40 hours
Level
Advanced
Via
Coursera
Institution
The State University of New York
Instructor
Isabelle Bichindaritz
Language
English
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