Now is the time to get certified for Python!

Python Institute PCAP-31-03: Certified Associate in Python Programming

There are five Practice Tests with preparation questions from all knowledge areas.

Exam Syllabus

Modules and Packages

• import variants; advanced qualifiying for nested modules

• dir(); sys.path variable

• math: ceil(), floor(), trunc(), factorial(), hypot(), sqrt(); random: random(), seed(), choice(), sample()

• platform: platform(), machine(), processor(), system(), version(), python_implementation(), python_version_tuple()

• idea, __pycache__, __name__, public variables, __init__ py

Exceptions

• except, except:-except; except:-else:, except (e1,e2)

• the hierarchy of exceptions

• raise, raise ex, assert

• event classes, except E as e, arg property

• self-defined exceptions, defining and using

Strings

• ASCII, UNICODE, UTF-8, codepoints, escape sequences

• chr/ord(), literals

• indexing, slicing, immutability

• iterating through,

• concatenating, multiplying, comparing (against strings and numbers)

• in, not in

• .isxxx(), .join(), .split()

• .sort(), sorted(), .index(), .find(), .rfind()

Object-Oriented Programming

• ideas: class, object, property, method, encapsulation, inheritance, grammar vs class, superclass, subclass

• instance vs class variables: declaring, initializing

• __dict__ property (objects vs classes)
  private components (instance vs classes), name mangling

• methods: declaring, using, self parameter

• instrospection: hasattr() (objects vs classes), __name__, __module__, __bases__ properties

• inheritance: single, multiple, isinstance(), overriding, not is and is operators

• inheritance: single, multiple, isinstance(), overriding, not is and is operators

• constructors: declaring and invoking

• polymorphism

• __name__, __module__, __bases__ properties, __str__() method
  multiple inheritance, diamonds

Miscellaneous (List Comprehensions, Lambdas, Closures, and I/O Operations)

• list comprehension: if operator, using list comprehensions

• lambdas: defining and using lambdas, self-defined functions taking lambda as as arguments; map(), filter();

• closures: meaning, defining, and using closures

• I/O Operations: I/O modes, predefined streams, handles; text/binary modes, open(), errno and its values; close(), .read(), .write(), .readline(); readlines() (along with bytearray())

Why learn Python?

Python is easy to learn. The syntax is simple and the code is very readable. With Python, you can write programs in fewer lines of code than with most other programming languages. The popularity of Python is growing rapidly. It is now one of the most popular programming languages. Python has a wide variety of applications. It is used for automation, web application development, artificial intelligence, data science and so on:

Automation

Python can make life easier by automating many tasks, such as scraping a website to collect data, automating test cases in software development, or automating everyday office tasks. Python can easily access and read all kinds of files, which opens up the possibility of saving a lot of time by automating repetitive tasks.

Web Development

Python is a good choice for rapid web application development. With many frameworks like Django, Pyramid, and Flask, you can develop web applications with great speed using Python. Python is used on the server side of web development. You can use Python to interact with database and create RESTful API services.

Artificial Intelligence

The near future will be the era of artificial intelligence. In the past, computers and machines were used to perform mathematical calculations at very high speeds, but now many large organizations and researchers are working to develop intelligent systems that can perform tasks like a human. To some extent, machines are able to understand human emotions and their natural language. They can mimic certain human actions that were not possible before. Again, Python is very popular for developing AI systems.

Data Science

Earlier, Python was mainly used to build applications and write scripts to automate tasks, but now a brand new trend of data science has given Python an even bigger boost. Data scientists are heavily dependent on Python because it is so simple, has a large community, and can perform huge calculations with ease.

Python is being used in a wide variety of fields, and there are no signs that this trend is coming to a halt. It's safe to say that Python is here to stay for the long haul in this ever-changing and evolving IT industry.