My first experience with Python...

In the week of December 18, I attended a course at University of Texas at Dallas (UTD) to learn Data Science with the programming language, Python. This was a wonderful opportunity for me to learn this fast-growing language along with its functionality and use cases

Python is a very easy coding language to learn due to the simplicity of its syntax. Compared to other languages such as Java and C++, Python takes a lot less typing to perform the same action. One of the most fundamental advantages of using this language is, its ability to easily work with ‘text’, or in programming terms, ‘Strings’. Python provides several libraries which facilitate tokenizing and concatenation of Strings. Due to this feature, it is being used extensively in industries which require processing of millions of example, various companies use the different functions in Python to analyze their product reviews from customers to get suggestions.

Along with Python, I also learned its use in Data Science and Machine Learning. Artificial Intelligence (A.I.) in the past couple of decades has grown rapidly due to the vast array of opportunities brought about by the ‘cloud storage’ facility. Now, there is a major focus placed by companies upon the incorporation of A.I. into their business model. Some of the tasks it could perform include analysis of sales, customer satisfaction, and even give recommendations. Another major catchphrase being thrown around nowadays is ‘machine learning’. Machine learning is basically when programmers allow the system to learn and make changes to the source code without human supervision based on the data it collects. Around 70% of the source code written for A.I. today is in Python. This is because of the language’s processing speed, efficiency, and also the several libraries that allow the system to analyze large amounts of data and understand trends.

Using Python classes such as ‘numpy’, ‘pandas’, and ‘matplot’, I was able to read in a ‘.csv’ file and get the output of graphs which showed the cross-analysis of categories in the file. To the left is a ‘ ’ graph that was created by the system after analyzing the file with information about the petals and sepals of iris flowers.

Finally, I would really like to thank Professor Kamran Khan and Mr. Prabhat Jha for their exceptional teaching and patience during the sessions. Also, special thanks to Mr. Jay Veerasamy for his time and support at the sessions. Something really interesting that the professor stated was, that “in order to be a data scientist the last thing you have to worry about is how to code, but rather, you should know what to code.” When working with machine learning, having this mindset is very important, since it can open up potential areas for incorporation of A.I.