The computers keep getting smaller and faster and so do our codes and programs which run them lightning fast
Computers are getting faster every day. And this is because every day there are advances in the field of nanoscience and nano-materials which enable the invocation of Moore's Law. Moore's Law is an approximation statement started by Gordon Moore in the year 1965 which said that the number of transistors in an integrated circuit doubled every 2 years. Our integrated chips today have the power to handle intensely complicated algorithmic structures and do work with huge amounts of data.
And with good data, we need good algorithms.
If you are a beginner, it will be great to try out some pattern programs to build up a really good working knowledge of the most basic concepts of algorithms. Loops, conditional statements, and breaking conditions are some of the most basic algorithmic litanies one should obtain.
Apart from that, another important aspect one needs to address is the array. The manipulation of an array is extremely important in data science and can make the difference between doing the job easily and letting the job get you easily.
Much of the big data handling boils down to the handling of data structures the first of which is the array. List, tuple, array, and dictionary all have similar kinds of concepts that can be applied while coding out any program involving their structural addresses.
Arrays for example can be constructed beyond the regular one or two dimensions depending upon the amount and form of data present. And similarly, one can run through entire sets of data by correctly putting the codes to trace the entire structure and process it accordingly whether it’s a matter of accepting or working with an array.
Being on the lookout for recognizing patterns and then deeply thinking about how that pattern got naturally recognized by you is a good way to develop algorithmic thinking. Use patterns to understand how you think to understand how you would code to ensure the machine follows the same path to reach the conclusion you did. Always remember that AI is just a reflection of our Natural Intelligence.
