About this Course
Are you interested in learning how to program [in Python] within a scientific setting?
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 19 hours to complete
English
Subtitles: Arabic, French, Portuguese [European], Italian, Portuguese [Brazilian], Vietnamese, German, Russian, English, Spanish
Skills you will gain
- Bioinformatics
- Bioinformatics Algorithms
- Biology
- Python Programming
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 19 hours to complete
English
Subtitles: Arabic, French, Portuguese [European], Italian, Portuguese [Brazilian], Vietnamese, German, Russian, English, Spanish
Having context is always motivating, but unfortunately, having an understanding of what a study did can require experience in programming.
Many bioinformatic packages rely on understanding a Linux environment, and since I spent a lot of time just using that to process information/data, I would say it is time better spent to understand how to install software, organize your experiments on your computer, operate in a cluster, and other Linux-based tasks. There’s a website called ryanstutorials that has a nice intro.
If you want to learn python because you think R is hard, that’s probably not a great reason, and you need to spend time finding a resource that works for you. I thought codecademy had a good interactive python course, and in general I would recommend an interactive environment that proof checks your code. ‘Python for absolute beginners’ was a nice introductory text, published by apress. If you access springerlink from a university vpn, you can get the ebook for free.
Finally, if you are interested in specific bioinformatic software packages, and if you can install them, they usually come with test data. Following the documentation, see if you can recreate their output. Miniconda might be a nice package manager for you to save the headache of installing and dependencies, so you can focus on working with programs and learning.
Programming vector created by storyset - www.freepik.com
Biology Meets Programming: Bioinformatics for Beginners - University of California San Diego
Located in San Diego, California, the University of California San Diego is one of the top 10 public universities as per the U.S. News and World Report. The course – “Biology Meets Programming: Bioinformatics for Beginners” offers a gently-paced introduction to the Bioinformatics Specialization, preparing learners to take the first course in the Specialization, "Finding Hidden Messages in DNA". With over 134,016 students enrolled, the course has an overall rating of 4.2/5.
The Biology Meets Programming: Bioinformatics for Beginners is designed to guide the learners through programming from the ground up and apply introductory topics in coding to solve real problems in modern biology. Through the “Learn Python" track on Codecademy, the following topics on Python are covered to help the learners understand the algorithms used for solving various biological problems- syntax, strings and console, conditionals and control flow to create programs that generate different outcomes, learn how to create and use functions, and data structures lists and dictionaries.
To know more about the course structure and syllabus, visit the link - //www.coursera.org/learn/bioinformatics
Python for Genomic Data Science - Johns Hopkins University
Situated in Baltimore, Maryland, Johns Hopkins University is a private research university founded in 1876. The mission of the Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world. With more than 46,470 students enrolled, the course has an overall rating of 4.3/5.
The Python for Genomic Data Science is designed for learners who are not computer scientists but who want to analyze genomic data. It will teach the participants basic concepts of bioinformatics programming, and introduce them to computational tools that deal with large amounts of data to write basic programs in Python, adapt existing programs to their needs, and to interface Python programs with various bioinformatics packages through the use of Biopython. The topics covered in the course include data structures, Ifs, loops, functions, modules, packages and biopython.
To know more about the course structure and syllabus, visit the link - //www.coursera.org/learn/python-genomics?specialization=genomic-data-science
Quantitative Biology Workshop - Massachusetts Institute of Technology
Established in 1861, the Massachusetts Institute of Technology is a research university dedicated to advancing knowledge and educating students in science, technology, and other areas of scholarship that will best serve the world in the 21st century. The Massachusetts Institute of Technology MOOCs offer numerous online courses in a variety of subjects which can be audited for free by the students or they can choose to receive a verified certificate after payment.
The Quantitative Biology Workshop is designed to give learners an introduction to the application of quantitative tools to analyze biological data. The tools and programming languages covered during the course include MATLAB, PyMOL, Python, and R. The content will not teach any one language in a comprehensive manner as the course aims to give learners an introduction to multiple languages and tools to find a topic that they would want to explore more. The course will teach you how to apply quantitative methods to biological problems, define computational vocabulary, write Python, MATLAB, and R code to analyze biological data, examine any protein structure in PyMOL and analyze how to answer a scientific question through a step-by-step thought process.
To know more about the course structure and syllabus, visit the link - //www.edx.org/course/quantitative-biology-workshop-3#:~:text=QBWx%3A%20Quantitative%20Biology%20Workshop%20is,for%20students%20from%20other%20universities.
Python Course 1: Getting Started with Bioinformatics - OmicsLogic
OmicsLogic is a training program developed by Pine Biotech in collaboration with the Tauber Bioinformatics Research Center from University of Haifa, Israel. OmicsLogic programs are developed using project-based content that is enriched with multimedia content. The programs are divided into 5-10 minute modules with videos, quizzes and assignments that can be completed independently. With more than 302 students enrolled, the course has an overall rating of 4.0/5.
The Python Course 1: Getting Started with Bioinformatics is designed for undergraduate and graduate students from life science backgrounds with little or no prior knowledge of Python coding for bioinformatics. The topics covered in the course include loading data into Python and checking what type of data it contains; basic visualization packages including boxplots, scatterplots, histograms and bar plots; basics of DNA and how it can be translated to be read in Python for analysis; create your own complementary strands of DNA using Python. and finally you will learn about data science and the resources to get started.
To know more about the course structure and syllabus, visit the link - //learn.omicslogic.com/courses/course/python-course-1-getting-started-with-bioinformatics
Big Data, Genes, and Medicine - The State University of New York
Established in 1948, the State University of New York is the largest comprehensive system of higher education in the United States, educating nearly 468,000 students in more than 7,500 degree and certificate programs both on-campus and online. With over 28,670 students enrolled, the course has an overall rating of 4.2/5.
The Big Data, Genes, and Medicine is designed for students from a diverse range of biological backgrounds The course will teach you build and develop R scripts for — preprocessing biomedical data, replace missing values, normalize data, discretize data, and sample data; evaluating the performance of feature selection methods, selecting features from datasets involving gene expressions, evaluating the performance of classification and prediction methods, classifying and predicting diseases from gene expressions, determining gene alterations and their types and finally finding clusters and pathway analysis.
To know more about the course structure and syllabus, visit the link - //www.coursera.org/learn/data-genes-medicine
- Statistics and R for the Life Sciences - Harvard University
Established in 1636, Harvard is one of the oldest institutions of higher education devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. The Harvard University MOOCs offer numerous online courses in a variety of subjects which can be audited for free by the students or they can choose to receive a verified certificate after payment.
With over 381,510 students enrolled, the Statistics and R for the Life Sciences course is designed to teach students R programming language in the context of statistical data and statistical analysis in the life sciences. The topics covered in the course include introducing the learners to basic exploratory data analysis tools such as histogram, the Q-Q plot, scatter plots, boxplot, stratification, log transforms, and several summary statistics; random variables, the central limit theorem, probability distributions, and p-values. You will also use R to explore advanced topics such as Monte Carlo simulations.
To know more about the course structure and syllabus, visit the link - //www.edx.org/course/statistics-and-r
- R-Coding Course 1: Getting Started with Bioinformatics - OmicsLogic
OmicsLogic is a training program developed by Pine Biotech in collaboration with the Tauber Bioinformatics Research Center from University of Haifa, Israel. OmicsLogic programs are developed using project-based content that is enriched with multimedia content. The programs are divided into 5-10 minute modules with videos, quizzes and assignments that can be completed independently. The training platform also offers several example projects that are sourced from high-impact peer-reviewed publications in the field of oncology, virology, agriculture and infectious disease. With more than 890 students enrolled, the course has an overall rating of 4.2/5.
The R-Coding Course 1: Getting Started with Bioinformatics is designed for undergraduate and graduate students from life science backgrounds with little or no prior knowledge of R coding for bioinformatics. The topics covered in the course include loading data in R and checking what type of information it contains; data processing and basic visualization packages including boxplots, scatterplots, histograms and bar plots; basics of DNA and how it can be translated to be read in R for analysis; and finally you will also learn how you can create your own complementary strands of DNA using R.
To know more about the course structure and syllabus, visit the link - //learn.omicslogic.com/courses/course/r-coding-course-1-getting-started-with-bioinformatics
Still facing trouble deciding which course to start with? Check out the table provided below to find which course would be suitable for you based on the prerequisites required before enrolling, duration, price, and the availability of a certificate upon completion of the course.
SL.NO | COURSE NAME | PREREQUISITE | DURATION | PRICE | CERTIFICATION |
1 | Biology Meets Programming: Bioinformatics for Beginners - University of California San Diego | No prior knowledge of biology or programming is required. A little technical know-how is preferred. | Approx. 19 hours | Free | Paid Certificate Available |
2 | Python for Genomic Data Science - Johns Hopkins University | No prior knowledge of Python is required. | Approx. 9 hours | Free | Paid Certificate Available |
3 | Quantitative Biology Workshop - Massachusetts Institute of Technology | Knowledge of biochemistry, molecular biology, and genetics. Programming experience is not required. | Approx. 36-72 hours | Free | Paid Certificate Available |
4 | Python Course 1: Getting Started with Bioinformatics - OmicsLogic | No prior knowledge of Python is required. | Approx. 6 hours | Subscription Based | Paid Certificate Available |
5 | Big Data, Genes, and Medicine - The State University of New York | Knowledge of biomedical science is required. No prior knowledge of R is required. | Approx. 40 hours | Free | Paid Certificate Available |
6 | Statistics and R for the Life Sciences - Harvard University | Basic programming and basic math knowledge is preferred. | Approx. 8-16 hours | Free | Paid Certificate Available |
7 | R-Coding Course 1: Getting Started with Bioinformatics | No prior knowledge of R is required. | Approx. 6 hours | Subscription Based | Paid Certificate Available |
If you are still facing trouble deciding on a course, consider enrolling for the upcoming 2-Week Training OmicsLogic Training Program on Getting Started with Bioinformatics in Python [Beginners] and Getting started with Bioinformatics in R [Beginners] that are designed for students and researchers from a life science background who wish to begin their journey in Python/R programming for analyzing biomedical datasets.
The program will introduce elements of data science in Python/R, such as data wrangling, visualization, statistical analysis, and machine learning. The methods will be reviewed in the context of biomedical and other scientific problems using -omics data. The exercises focus on importing and understanding various data types, transforming them into categorical variables, continuous data and extracting meaningful patterns for visualization. Then, the training continues to
include statistical analysis, complex data visualization, machine learning and an introduction to deep learning.
During the course of the program, students will gain the following important technical skills:
- Data wrangling and processing
- Data visualization using t-SNE & UMAP
- Dimensionality reduction Methods - PCA, MDS, NMDS
- Descriptive Statistics and Clustering: K-means & Hierarchical Clustering
- Dimensionality Reduction and Predictive Models with Deep Learning
After completion of the program, students and researchers can begin their career in biomedical data science as bioinformaticians, biomedical data scientists, biomedical data analysts and much more.
To enroll for the training program on Getting Started with Bioinformatics in Python [Beginners], visit the link - //edu.omicslogic.com/getting-started-with-bioinformatics-in-python
To enroll for the training program on Getting Started with Bioinformatics in R [Beginners], visit the link - //edu.omicslogic.com/en/getting-started-with-bioinformatics-in-r
To know more about the various courses and programs offered by OmicsLogic, head on to our main portal: //learn.omicslogic.com/ or write to us at . Happy learning!