Directorio Masters en Salud y Gestión Sanitaria Masters en Laboratorio Clínico
June 2026
The MSc Bioinformatics and Biostatistics unlocks diverse career avenues in biology, healthcare, and data science. Graduates can thrive in biomedical research, clinical trials, healthcare data analysis, genomics, personalized medicine, pharmaceuticals, data analytics, and beyond. Equipped with versatile skills, our graduates are in high demand across industries where data analysis and biological expertise are paramount.
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This MSc/ PGDip Bioinformatics and Biostatistics prepares you to apply and develop new computational techniques in biomedical research, working both in hospital environments and for companies across the biotech sector.
Our online MSc / PGDip Biostatistics and Bioinformatics will teach you how to use computer tools to store, organise, analyse and interpret vast amounts of data to extract knowledge that can be applied to solving biological and biomedical problems. This course will fully equip you with the skills you need to kickstart your career in this rapidly evolving sector.
What is bioinformatics?
Bioinformatics is the application of computational and statistical techniques to study biological data, including DNA, RNA and protein sequences. It involves the development of algorithms and software tools to analyse and interpret large data sets in order to understand biological systems and processes. Bioinformatics has applications in several fields, including genomics, proteomics, drug discovery and personalised medicine.
What are the uses of biostatistics?
Biostatistics is the application of statistical methods to study biological data and improve public health. It has many applications, including the design of clinical trials, the analysis of health care data, the identification of risk factors for disease, and the evaluation of public health programmes. Biostatistics also plays a crucial role in developing and testing new medical treatments, and in the analysis and interpretation of epidemiological data to inform public health policy.
THE ROUTE TO YOUR FUTURE
Why enrol in our MSc / PGDip Bioinformatics and Biostatistics online training programme? Because in addition to having prestigious professors and a curriculum aimed at preventing student drop-out, we guide our students towards achieving their professional goals.
Learn more about the employability plan you'll benefit from the moment you sign up.
Characteristics of the MSc Medical Laboratory Science
Audio visual study material
- You will have access to many hours of audio visual materials, which are essential teaching materials. Thus, you can study wherever and whenever you want.
Practical activities
- Approximately twice a week you will carry out practical activities that will be reviewed and evaluated by your specialized teachers.
Complementary material
- Class summaries, articles to stay up-to-date... You will have everything you need to keep on learning.
Master class
- You will learn from well-known experts in the medical sector thanks to our master classes, which you can watch as many times as you want.
MSc / PGDip final project
- At the end of the course you will conduct a research project on a topic of your interest. One of our teachers will supervise your project.
The MSc Bioinformatics and Biostatistics unlocks diverse career avenues in biology, healthcare, and data science. Graduates can thrive in biomedical research, clinical trials, healthcare data analysis, genomics, personalized medicine, pharmaceuticals, data analytics, and beyond. Equipped with versatile skills, our graduates are in high demand across industries where data analysis and biological expertise are paramount.
As your journey progresses, you will discover different modules which will help you, step by step, to reach your final goal.
Module 1. Biochemistry and Molecular Biology I
1. The cell: structure.
2. Cell components and carbohydrates
3. Lipids
4. Peptides
5. DNA
6. ARN
7. Chromosomes
8. Genes and genomes
9. Study of the chromosomes
10. Mutations and polymorphisms
11. Cell division
12. Central dogma of molecular biology
13. DNA replication and repair
14. Transcription
15. Translation
Module 2. Biochemistry and Molecular Biology II
16. Control of gene expression in prokaryotes
17. Control of gene expression in eukaryotes I
18. Control of gene expression in eukaryotes II
19. Epigenetics
20. PCR
21. Recombinant DNA technology
22. Sequencing
23. Nucleic acid hybridisation: arrays
24. Cell mobility and transport
25. Membrane proteins
26. Mass spectrometry
27. X-ray crystallography
28. Protein structure prediction
29. Basic immunology
30. Viruses: structure and function
Module 3. Biostatistics and R I
1. Fundamentals of descriptive analysis of one-dimensional data
2. Introduction to R and RSTUDIO
3. Fundamentals of Probability Calculus I
4. Fundamentals of Probability Calculus II
5. Discrete random variables
6. Continuous random variables
7. Discrete notable distributions
8. Practice of R. Main objects of R
9. Continuous notable distributions
10. Basic elements of a random vector
11. R practice. Representation and simulation of random variables with R
12. Media vector and covariance matrix
13. Estimation of the parameters of a population
14. Confidence range for a proportion
15. Confidence range in normal distributions
Module 4. Biostatistics and R II
16. Hypothesis contrast for a proportion
17. Practice of R. Bias, variance and confidence range for an estimator
18. Hypothesis contrast for a normal population
19. Comparison of populations
20. Practical R. Hypothesis contrast in R
21. The maximum plausibility method
22. The method of linear regression simple I
23. The method of linear regression simple II
24. The model of multiple linear regression
25. Practical R. Linear regression adjustments
26. The model of analysis of variance
27. The method of analysis of covariance
28. Logistic regression
29. Neural networks for regression
30. Variable selection and extraction techniques for regression
31. Variable selection and extraction methods
32. Evaluation of regression models
33. Comparison of regression models
Module 5. Bioinformatics
Part I. Python
1. Python, the new unknown
2. Basic data types, operators and input/output
3. Types of advanced data.4. Flow control
5. Function
6. Errors and Object-Oriented Programming
7. Data manipulation
Part II. Omics database and data analysis
1. Introduction to bioinformatics I: Operating System Requirements
2. Introduction to bioinformatics II: How to use the terminal
3. Introduction to omics: application
4. What is massive sequencing? From DNA to NGS data (Big Data)
5. General bioinformatics analysis of mass sequencing data
6. DNA sequencing
7. Integrative Genome Viewer
8. Variant detection through the use of bioinformatics tools
9. Transcriptomics I: RNA-seq
10. Transcriptomics II: Microarrays
11. Characterisation and functional enrichment
12. Other omics
13. Databases: Repositories, data analysis and interpretation of results
14. Bioconductor: repository of bioinformatics tools
15. Practical I: Data analysis using Galaxy
16. Practical II: Designing a pipeline for variant calling
17. Practical III: Designing a transcriptomics pipeline
18. The future of bioinformatics
Major Project
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