Opinion
Data Science and Artificial Intelligence are stupendous fields that I enjoy working on to improve each day.
In the modern era, they are most hyped and talked about entities. The world is in awe of the various opportunities that are being presented each day with consistent development and constant accomplishments in data science and AI.
However, to become successful as a data scientist or a machine learning practitioner, there are some golden rules one must follow to improve productivity and overall effectiveness of their desired tasks or projects.
I have been constructing machine learning and deep learning models for quite some time and have been working on various projects. From my personal experience, this is the list of the three Do’s and three Don’ts that I would like to share with all of you so that you can kickstart and embark on your epic journey or improve the trajectory of your success to astronomical levels.
Let’s get started and learn the most significant aspects to consider for your Data Science future.
1. Do: Extensive Research An Continuous Study
The best part about Artificial Intelligence and Data Science is the continuous evolution of these subjects each day. The improvements in technologies are rapidly increasing. It becomes significantly more important to stay updated on the latest trends and emerging developments that occur in the field of data science.
Researching is an integral part of any Data Science Project. It is crucial to have some knowledge or at least a brief idea of what are expansions occurring in the AI field.
Researching on a project or any particular task or even just a simple data science terminology is enormously essential. Appreciate everyone, especially expert’s views on the subject as each of them have their own vision, which you can adapt and learn.
Hence, research and critical thinking are one of the few ways you can transform your skills to a whole different level.
My Suggestion: I would highly recommend watching lots of YouTube videos from quality data science, AI, math, or programming channels. Reading books and most importantly, research papers are necessary for absolute knowledge and understanding of any specific topic or aspect of data science.
1. Don’ts: Giving Up!
Data Science can sometimes be difficult, especially for a beginner trying to get started. You look at the potential topics in this field, and it could intimidate quite a few people.
The interesting part about data science, similar to programming, is with each mistake you make, you learn something new and what you did wrong, provided you find a solution by looking it up on the internet or cracking it by yourself. This feeling makes the overall experience even more satisfactory.
Don’t worry if you are not able to solve a Machine Learning or data science problem on your first try. That is completely fine as long as you remain persistent, find a solution, and understand the concepts better.
Also, if it makes you feel better, even experts in this field make mistakes and have to look up stuff for solving certain questions. This field is probably one of the only ones where you don’t have to mug up a lot of things as you can use Google for things you forget.
My Suggestion: I would highly encourage you to change your outlook on challenging tasks and enjoy them more. Each failure should just be considered as a stepping stone to Success and nothing more. In data science, with each mistake you make the more you learn. So, cheer up and keep learning!
2. Do’s: Practical Implementations
To appreciate the true beauty of data science, you need to try out lots of projects. The tasks that can be achieved and the problems you can solve are absolutely fantastic.
Theoretically understanding the intuition of machine learning concepts and math behind these concepts of data science is crucial.
However, you also need to know how you can implement the following projects in a real-life practical scenario. Don’t be afraid to get your hands dirty with some code and implement these projects on your own.
An example of this can be any machine learning or deep learning concept. Let us consider an example of a multilayer perceptron or backpropagation of a neural network. You probably know how these concepts work mathematically and theoretically.
That is awesome! But is equally essential to implement these practically and achieve the solution for such tasks with your executions. This helps you to improve as well as help you to crack interviews.
My Suggestion: Even if it is just a simple Machine learning algorithm, I would highly recommend not to use the scikit-learn library or similar helpful tools for easier implementations. Instead, try to detect the best possibilities and execute them from scratch on your own. This helps to improve skills and gain an overall better understanding of these concepts.
2. Don’t: Hesitating to ask for help
It is not uncommon in data science to get stuck on a problem that you are working on for a long time. The best part is data science has a brilliant community with very helpful people and lots of resources at your disposal for your benefit.
Stack Overflow, discord channels, YouTube videos, free online code camps, GitHub, towards data science, etc. are all helpful resources that are available for all of us to utilize and improve our skills.
Communication with other people and experts while sharing ideas is a great way to learn more. Not effectively communicating can lead to quite a few issues like misleading understandings in queries you might have about a particular topic.
Also, talking to people is extremely helpful to share your views, as well as gain knowledge. By talking to more people, you develop better ideas and most importantly interactivity, which will be very useful while working in a company with a team on data science projects.
My Suggestion: I used to hesitate to ask for help when I initially started. I thought it was best to find all the solutions to these problems on my own. I considered this was the best practice for a long time, and that is only partially right. Sometimes you may have misunderstood a concept or aren’t doing something perfectly alright. After trying by yourself, if you still have confusion, it is a good practice to ask your friends or experts who can help you out!
3. Do: Keep exploring, building new projects, and participate actively!
The field of artificial intelligence and data science is humungous. There is so much out there to be curious about and explore. There are lots of mathematical functionalities, in-depth theory on multiple aspects of machine learning and deep learning.
Practice becomes significantly to keep yourself updated with all the latest trends and process the on-going techniques in this tremendous field. There is a lot of scope in every aspect with continuous developments. So, keep coding and keep working on practical implementations!
Try to actively participate in competitions on websites. Kaggle is one such site that hosts some of the best data science, related competitions. Don’t worry about which place you finish. It does not matter much as long as you learn something new.
There are a lot of websites to improve your coding as well as participate in competitions like HackerRank, which you should consider. Involving in the community is helpful to consistently learn more from fellow data science enthusiasts.
My Suggestion: My Recommendation for this Do is similar to my suggestion in the second Do of the article. There are tons of practical projects and ideas available to implement. Just pick one project of your choice and start working on it. Doing more projects is the best way to keep learning! Find more projects and continuously upgrade your skills!
3. Don’t: Never Stop looking for better solutions once you finish solving a problem!
Congratulations on finishing your data science projects!
But hey! There is so much more you can do to improve your projects. The beauty of the field of data science is the variety of options that it provides you. There is always something better than you can always try out and implement accordingly.
To give you guys a better understanding of what I mean by this point, then feel free to revisit my first two posts on Towards Data Science on the project of human emotion and gesture recognition. You can do so from the below links.
Human Emotion and Gesture Detector Using Deep Learning: Part-1
Human Emotion and Gesture Detector Using Deep Learning: Part-2
You can observe the emotion models that I have posted in part-1 and part-2 of these articles. The best custom deep learning model I built was after a lot of trial and error. I was happy with the result I was able to achieve. But with my second model on the same problem, after more tries and conceptual understanding, I was able to achieve even higher accuracy and overall better results.
Every model you construct and every project you complete in data science has a lot of room for improvement. It is always a good practice to consider alternatives and various other methods or improvements that you can make to achieve better results.
My Suggestion: In my initial days, I used to be so excited I finished a project that I would just chill and move on to the next task. That is again a partially fine thing to do, but often there are ways to improve your project to the next level. Before moving on towards your next objective, list out the things you can do to improve. 😃
Conclusion:
Summarizing everything we discussed in this article in a few lines, the most crucial takeaway, in my opinion, is to keep focusing on constant development in data science, building more machine learning or deep learning projects on a consistent basis, learning new things every single day, reading more research papers, and keep practicing.
Sometimes you may not get the ideas or projects on your first try, and that is completely fine as long as you learn something new with each failure. Don’t hesitate to ask for help when stuck and keep integrating your skills to reach your peak potential.
If you have any queries or suggestions pertaining to this article, then feel free to let me know, and I will get back to you guys with an answer as soon as possible.
Check out some of my other articles that you might enjoy reading!
Understanding Advanced Functions In Python With Codes And Examples!
Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!
