A difficulty with any of these types of questions is that the answer is highly community-dependent.
To answer some of your questions in haphazard order:
MATLAB is used a lot both in academia and in industry. One of the reasons it's used quite a bit in industry is because it is taught in academia. I know for a fact that MATLAB is used at Lincoln Laboratory and in DuPont's research and development divisions.
There are software packages written in Python that are good at symbolic computation, such as sympy and SAGE. Depending on your particular interests, feature requirements, and personal preferences, Mathematica (or Maple, or other computer algebra systems) may be superior to these packages.
MATLAB has a Symbolic Math Toolbox that can be used for some symbolic computations, but its symbolic manipulation capabilities, in my experience, are weaker than Mathematica and Python. Some symbolic manipulation could theoretically be done in C++, but it is unwieldy. MATLAB is also not a good general purpose language. It does linear algebra and numerical mathematics well, but it does not have good input/output capabilities. It does not have good parallel capabilities (even though there are variants like parallel MATLAB, MATLAB Star-P, and the Parallel Computing Toolbox) compared to C++ or Python. Even its graphics capabilities could use some work. MATLAB is also expensive unless you're affiliated with an institution that has a license. Each toolbox is expensive to purchase, and generally costs on the order of hundreds to thousands of dollars.
Mathematica does numerical computation in addition to symbolic computation. I haven't seen people use it for numerical computation as much as I have seen people use Python and MATLAB for numerical work. It too has parallel capabilities, but won't scale to large supercomputers.
Python is a good general purpose language that is regarded as easy to learn and usable. It is used on large supercomputers (see, for instance, PyClaw, petsc4py, mpi4py, and others), and scales well. It also has highly regarded numerical packages (such as NumPy and SciPy); a large, active community; good input/output processing capabilities; and good graphics libraries, along with a large repository of libraries (check out PyPI). It is free, compared to the proprietary packages mentioned above. You can find most of the functionality of MATLAB or Mathematica in freely available Python packages. The main disadvantage of Python is that it tends to be slower than compiled languages like C++, although this disadvantage is diminishing with the continued development of Cython, Numba, and PyPy; it can also be mitigated by replacing slower Python code with C (or C++, or Fortran) code and appropriately written Python wrappers. Being interpreted, many people report higher productivity with Python than compiled languages. It is quite popular, and probably worth learning if you have time.
C++ is a complicated language, and its use in computational science is controversial. Its large feature set can make it easy to write software that is difficult to maintain and takes forever to compile. However, used judiciously, features such as templating and operator overloading can be employed to great effect, as it has been in projects like deal.II, Blaze, and Elemental (among others). C++ has a steep learning curve when it comes to its advanced features, and I've heard anecdotal reports of people taking years to feel like they've learned the full language. Nevertheless, it is also a popular language, despite the usability concerns and the complicated feature set. It is probably worth learning, if only to make yourself more employable; its main competitors in computational science are Fortran and C, which are also worth learning. C and C++ are separate languages, and I advise you to learn them separately, however often you see people write "C/C++".
Whatever you decide to learn will be based on what you actually need. Sure, it's nice to learn both Python and C++, but given time and resource constraints, you're probably only going to learn what you'll actually need to use, and that depends on the community you work in.