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Modeling and Data Analysis in Life Sciences: 2016
Syllabus
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Expanded course description:
- Power tools of the trade
- Representation of numbers
- Compact representation of numbers: vectors, matrices
- Introduction to MATLAB
- Statistical description of data
- Mean, variance, and so forth
- Correlation coefficients
- Statistical inference: null hypothesis, alternate hypotheses
- Modeling of data
- Linear fitting
- Non-linear problems
- Clustering techniques
- Fourier analyses
- Monte Carlo simulations
- Random numbers
- Computing integrals
- Simulating a process
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