A common phenomenon in the work of a data scientist is solving problems. As a beginner, its always better to start learning how to solve basic programming problems, as these skills come in handy during your journey into the field of data science. They are also equally necessary when undertaking projects to boost your experience and portfolio.
A notable way of developing great problem solving skills as a beginner in the world of data science, is by regularly practicing and learning from already existing programming problems and their solutions.
''Think of it this Way '' : Data Scientists or Machine learning Engineers tend to create models to solve issues or perform specific tasks based on information or data provided. But how can these models perform these tasks?
- Yes, you guessed right ! The model is trained to solve similar problems related to the task, which in turn greatly helps the model to work well when provided with totally new data.
Thus as a programmer or data science novice, isn't it great bringing this exact same concept into our journey in the field of data science ? It surely is. Hence, as mentioned earlier in the beginning of this article, a great way to continuously grow your problem solving skills is by first learning how existing problems are solved.
This helps train your mind and focus to develop great solutions when handling new problems in the field of data science as well as programming.
Now, let's look at a basic practical illustration.
The lines of code below display a Python Program that takes in strings as input from the user and checks whether it is a palindrome or not.
- A palindrome is a number or string that is the same when read forward or backward. In other words, it is the same when reversed. Examples: 1, 121, malayalam, radar, etc.
# Python program that checks if a string is a palindrome
string = input('Enter the string: ')
if string == string[::-1] :
print('The string is a palindrome')
else:
print('The string is not a palindrome')
Such a program can be solved using different approaches. But as you keep on learning from examples on how such problems are solved, it helps build up your familiarity with similar examples in future. This gradually improves how you will solve more complex problems as you move on, in the field of data science.
In conclusion, as you progress in your journey in the field of data science, always remember that an essential way to develop your problem solving skills is to consistently keep on tackling more and more already existing problems or projects. This improves your familiarity with such problems and provides you with a higher chance of handling new and more complex real world data science projects easily and comfortably.