Mathematical methods of data analysis

Specialties continued

As I mentioned before (Specialties) I'm studying math and I will need to choose a specialty. I will get all the regulated math units regardless of my choice, the difference is only in the additional units.

 Mathematical methods of data analysis

 I've got almost no clue about this specialty. I'm quite new to this and I  haven't got any experience with stuff like this. So I'll just write about the stuff I read on my uni's website. So it contains such units:

1. Discrete transformations  - Absorb the knowledge of  mathematical description of the one-dimensional and multidimensional discrete transformations (Fourier, Vols, cosine, wavelet,etc.) as well as their fast computing technologies, learn to apply discrete transformations in digital and signal analysis at spectral area.
2. Cryptology -Learn about cryptology and its mathematical foundations. Absorb basic mathematical techniques used in cryptographic systems used in cryptographic systems, which are the components of the IT society and used in e-government and e-service facilities.
3. Mathematical methods for digital image processing - Learn about mathematical modeling of real-world images also creating their computer analogs, how to use mathematical discrete transformations, means of regular expressions and fractal techniques in solving one-dimensional and multidimensional efficient coding of digital images, analysis and synthesis problems.
4. Risk and uncertainty analysis - Learn to analyze the risks and uncertainties through probabilistic methods.
5. Database management systems - Gain knowledge about the existing database management systems (DBMS), DBMS structure and its basic functions.Learn to create databases based on Visual FoxPro DBMS and develop effective use of their information retrieval and manipulation tools.
6. Combinatorial optimization - Be introduced to the most important combinatorial optimization problems, get the knowledge about the basic methods and algorithms for solving such problems and learn to programmatically implement and experimentally investigate specific algorithms for practical optimization problem solving.
7. Software agents for knowledge engineering  - Learn about understanding knowledge, its imaging techniques and decision-making systems in a distributed information sphere.  Learn to use alternative solutions sampling strategies and methods of argumentation. Absorb the design principles of Expert Systems, learn to work with ES shells: Jess (with Java), Instant Tea(Online). Familiarize yourself with the software module design  technology and develop the skills to apply them in practice.

That's about it. Again feedback is greatly appreciated from people in the related fields. I'd love to hear more about this stuff.