Catchy Designs

1. Earn a bachelor’s degree — and a master’s degree. According to the Bureau of Labor Statistics, most computer and information research scientists — including data scientists — “need a master’s degree in computer science or related field, [like] computer engineering.” A master’s program will take you two years, after earning a four-year bachelor’s degree. 2. Get an entry-level job. You may want to pursue an advanced job, but for now, it’s likely you’ll need to start in an entry-level position, such as a data analyst or junior data scientist. You may want to consider system-specific training or certifications — such as data visualization, business intelligence applications or relational database management — to help get your first job. 3. Earn a master’s degree or a Ph.D. Data science is a field where your opportunities will be greater with a master’s or doctoral degree, so you may want to consider getting one! You can choose to pursue a master’s in computer science, information technology, math and statistics. 4. Get a promotion. With a higher degree, more career opportunities have been opened to you, and it may be time to go after a promotion — and earn a higher salary, too.

The things that people like most about Python:

• Syntax - it feels like a Lisp with syntax; I don't know why this is, since it has very little to do with Lisp, but it's somehow... light, I guess? Or maybe this is just because it prefers words instead of symbols? But I've heard other people call it "executable pseudo-code", so at least I'm not the only one that sees it that way.
• Significant whitespace - this means people that usually wouldn't be careful with their indentation have to get it right. I cannot read code that is not properly indented, the misleading structure makes my brain hurt.
• Expressiveness - no, I'm not talking about short programs or anything like that (this is covered in the above points). I'm talking about the ability to express different things without much effort. Decorators, metaclasses, multiple inheritance, monkey patching, that kind of stuff.
• Consistency - the philosophy of "there should be one obvious way to do it" is very effective; you will never see programs written in wildly different styles like you would in JavaScript or C++, for example.
• Error messages and stack traces - 'nuff said.
• Not afraid to break compatibility - mistakes in the language design get fixed, they don't just hang around forever, like they do in many other languages.
• Implementation - the source code of CPython is very readable, any questions you might have are easily answered by just going to its GitHub mirror.

The things that people don't like:

• Performance - if you want to fully utilize the hardware, you have to use PyPy or Cython or manually write a C module. Of course, the last two will only help if there's a hot spot in your program, they won't help with overall optimization.
• Poor support for functional programming.