As part of this development track kids will work on challenges that introduces them to some of the advanced concepts while programming using Python.
This learning track is based on a set of youtube videos by Chris Bradfield. Here’s what Chris Bradfield has got to say, “Learning to Code with Python is a video series intended for kids ages 11-14 (or grades 5-8). Younger kids are welcome to give it a try, but be aware there will be a lot of typing! We’re adding more, so subscribe to get all the updates. These videos are based on lessons developed by Chris Bradfield at KidsCanCode and have already been taught to hundreds of students in after school programs and workshops in Southern California.”
Some of the benefits of learning Python –
For aspiring Data Scientists, Python is probably the most important language to learn because of its rich ecosystem. Python’s major advantage is its breadth.
For example, R can run Machine Learning algorithms on a pre-processed dataset, but Python is much better at processing the data in an efficient manner.
What is Python –
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn’t catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python’s introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective.
Read more at – www.python.org