Your Ph.D. degree is a good opportunity for introspection and equally, your past experience or failures also gives you a good lesson that how you can improve. Learnings involve our all discoveries, mistakes, skills, and projects to make our future bright. We should not regard our past as an immature period but as an unfolding story.
A good tutorial detailed an article or well-designed a library, it all comes down to personal interest when you learn a new tool or the language for Data Science. We should aware with our this personal trait that shapes our receptive filters so that one person's favorite language is another person's nightmare?
An account of my attempts in my past days to use MATLAB, Weka, R, C++, and Python in my data science career. Data science is a broad term, employing people from a huge variety of backgrounds, like economics, biology, and many more. Although data science emerged from a pure statistical background, that the way it soon impacts the field of computer science and is today a tool as versatile and essential as a calculator.
As a result, the palette of programming languages for data science kind of feels like the universe a lifetime is not enough to explore it, and it is constantly expanding. We know that there are trade-offs involved with the generality, power, and complexity of a language. Therefore, the popularity of a language should serve only as an indication of current trends, not a factor for determining your own choice. Ultimately, it's a matter of application, experience, and taste.
At starting of my career I was introduced into the world of machine learning by a famous personality, Andrew Ng through an online course. I recommend it to this day to people looking for a smooth introduction into the admittedly scarily vast world of machine learning.
Although Python and R were much more popular at that time, Andrew chose MATLAB for the course's assignments. Data science courses focus more on how to use a language to do data analysis than how to do data analysis using a language.
My experience with Weka was short-lived. We were introduced to it as an optional tool for an assignment for the Pattern Recognition course. Finding automated tools and using them to derive off-the-self solutions is a current research area, termed as AutoML, but it took us some years, and failures, to realize that we need a human in the loop.
I delved into the mysteries and wonders of R during my diploma thesis. You've probably heard that R is a special child in the family of data analysis languages. But a steep learning curve is an understatement for the feelings of self-doubt and utter disorientation I experienced at the beginning of the deployment.
Our goal was to create a software tool for the automated execution of machine learning experiments. R was more of a purpose than a means, as we wanted to conduct extensive research on machine learning techniques by using the rich repository of R libraries.
Now, why would you do data analysis in C++? Why would anyone do it? Since a summer internship is my only experience in a non-academic workplace, I am not a guru of the psychology of a big company when choosing the tools of its employees. I suspect it was out of a combination of tradition and need for commercial, efficient-in-execution-time code.
The very initial discussion with my supervisor was that which language will you use for your future experiments? I replied that according to my understanding Python language will go. The question came from him was that are you experienced with this programming language. I replied simply no, but I have a very good hunch about it. Happy that my arguments persuaded him, I now enjoy the benefits of doing data analysis in Python. The ease of setting up experiments, appending functionality, and benefiting from rich libraries have really set my work forward. algorithms.
Nevertheless, I won't argue in favor of an unquestionable superiority of Python, as that would defeat my purpose. Programmers tend to solidify their beliefs into strong statements about languages. Probably forgetting that there can't be one language to rule them all. If there were, it would have to be so general that it couldn't be that effective. So, next time you are in front of a new dataset, don't be afraid to add another software arrow into your data science quiver.