Guaranteed internship!!!
1,500 hours
Optional internship
Online
Dual degree
60 ECTS
Employability
plan
This Master’s degree in Biostatistics and Bioinformatics prepares you to apply and develop new computational techniques in biomedical research and to work for companies in the biotech sector and hospital environments.
Our online Master’s degree in Biostatistics and Bioinformatics will teach you how to use computer tools to store, organise, analyse and interpret great amounts of data in order to extract knowledge that can be applied to solving biological and biomedical problems.
With this master’s degree you will enter a booming sector with a high demand for professionals.
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.
Approximately twice a week you will carry out practical activities that will be reviewed and evaluated by your specialized teachers.
Class summaries, articles to stay up to date... You will have everything you need to keep on learning.
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.
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..
Your teachers will open the doors to training in Master’s degree in Bioinformatics and Biostatistics with a focus on employability through specialisation:
From the first stage of your journey to the last, we will be by your side to help you get the most out of your training.
As soon as you enrol, you will have access to the virtual platform and you will receive the support book. We will also start your employability plan. Let's get going!
Videos, PDF summaries and live classes from your teachers.
You will regularly test your knowledge to move steadily towards your destination.
You will carry out a bibliographic research project on a topic of your interest.
Let's get to work! From 60 to 300 hours of optional internships in companies.
You have reached your destination! You now have your CEMP degree and university accreditation from UCAM*. It's time to face new challenges... and new adventures!
*see accreditation conditions.
I want my CEMP degree! I want my CEMP degree!As your journey progresses, you will discover different modules which will help you, step by step, to reach your final destination.
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
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
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
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
29.Basic immunology
30.Viruses: structure and functio
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
1. Introduction
2. Basic data types, operators and input/output
3. Types of advanced data
4. Flow control
5. Function
6. Errors and objects Oriented Programming
7. Data manipulation
1. Introduction to omics: application
2. Databases useful for the analysis and interpretation of omics data
3. What is massive sequencing? From DNA to NGS data (Big Data)
4. General bioinformatics analysis of massive sequencing data
5. Genomic variants
6. Bioconductor: repository of bioinformatics tools
7. Variant detection through the use of bioinformatics methods
8. Integrative Genome Viewer
9. DNA sequencing
10. Transcriptomics I: RNA-seq
11. Transcriptomics II: Microarrays
12. Characterisation and functional enrichment
13. Other omics
Why enrol in our Master’s degree in Biostatistics and Bioinformatics? Because in addition to having prestigious professors and a curriculum aimed at preventing dropouts, we guide our students to their professional goals.
Learn more about the employability plan you'll benefit from the moment you sign up.
