Opinion
Everything in the natural world has its merits and demerits. Data Science is no exception to this golden rule.
There is a mind-blowing amount of hype generated for the future of Data Science in the modern generation. There are tons of speculation of how Data Science will change the future of the world and how data is the biggest talking point of today’s age.
The importance of data is held in high regard by most tech giants, bigger or smaller companies, as the main aspect to revolutionize the state of affairs in the world today. Facebook, Google, Amazon, Microsoft, IBM, etc., are some among many other companies escalating the data hype train.
While data is more popular now than ever and achieving the status of a data scientist is considered a pinnacle of glory, what are the benefits and struggles to attain this position?
In this article, we will aim to answer this question that exists among most enthusiasts and aspirants of Data Science. We will analyze a concise list for the appropriate merits and demerits that are featured and related to Data Science.
Firstly, we will list out the five Pros to clearly understand some of the best advantages and merits of Data Science. Our focus will be on why they are so beneficial and popular in the modern era.
Then, we will discuss the cons or demerits of Data Science that could sometimes be a hindrance causing aspirants and enthusiasts to second-guessing their choices before picking up the subject.
We will analyze both these aspects of merits and demerits as neutrally as possible. The points mentioned in the article are opinionated by it should give the viewers a solid standpoint.
Ultimately, it is your personal choice to weigh in all the possible options and choose what is the best decision for you to achieve your path to success. So, without further ado, let us get started!
1. Numerous Career Opportunities
This merit should not be a surprise to anyone as Data Science is considered the sexiest job of the 21st Century. The field provides numerous options, both from a student and professional standpoint.
While students can choose from a wide range of options like Data Science, Artificial Intelligence, robotics, computer vision, natural language processing, and so much more, professional career opportunities are also broad.
These opportunities for careers in Data Science range from Data Scientist, machine learning engineer or machine learning scientist, applications or enterprise architect, Data or infrastructure architect, Data Engineer, Statistician, business intelligence developer, and data analyst, among many others.
The numerous career opportunities from the field of Data Science and the eventual prosperity you can achieve as a successful Data Scientist are limitless. And it turns out to be one of the intriguing advantages of this subject.
I would highly recommend checking out one of my previous articles on the five best ways to earn income from Data Science from the following link provided below. It should be a good starting point to analyze your career choices.
2. Develop Essential Qualities For A Revolutionary Future
Data Science is a fresh subject, and there are tons of new discoveries to be achieved in this field.
The best part about learning Data Science is it also automates your skill-level for most of the essential topics of the realistic future.
Most of the components of Data Science, such as data, object storage, programming, AI, computer vision, natural language processing, etc., are some of the aspects in Data Science that will remain in high demand for the upcoming decades.
The essential qualities you develop for Data Science are going to be helpful and everlasting. They will help you for any future tasks or assignments you decide to persuade.
The pace of revolutionary technologies is not slowing down any time soon. And Data Science offers the best tools to master for the glorious future years to grace upon humanity in the next few years.
3. Weightage To Technical And Practical Abilities
Data Science is one of the subjects that allows you to utilize your brain capacity to the fullest.
It is not a desk job or a copy-paste job, but a position where you have to constantly keep thinking to produce the best and most effective results possible.
The best part about Data Science is as you keep learning more, you keep developing new abilities.
If you are working on an end to end Data Science project which includes deployment, you will learn some of the most essential skills required.
These skills range from technical aspects like data cleansing, data analysis, construction of Machine Learning and deep learning models, using cloud technologies.
The practical aspects involved are communication skills, researching, effective interactions, self-confidence, and other soft skills that you are forced to develop to succeed in Data Science.
4. Gain Intriguing And Fascinating Knowledge
Data Science is not a dry field!
It is a consistently and continuously developing subject. Various new performance measures and features are developed each day.
These advancements can be noticed by the research papers that are continuously published throughout the year.
There is so much to learn and gain from the subject of Data Science. Some of the concepts of machine learning, deep learning, and neural networks are extremely intriguing and fascinating.
Since the field of Data Science is continuously developing, there is something for you to learn each day. The subject is vast and offers enthusiasts so much to explore and consider.
To mention a few helpful resources, my main recommendations are 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.
So, make sure you keep learning and having fun with Data Science. This point is one of the best merits of picking up a continuously advancing subject. You can factor in a lot of resources and learn a lot.
5. Create And Develop Unique And Innovative Projects
With the boundless and immense knowledge gained from Data Science, you can extend these technicalities for the development of unique and innovative projects to change the landscape of the future.
The best part of Data Science is the outstanding projects you can create with the help of data, visualizations, and analytical techniques, construction of models, training these models and running them, and finally deploying them to reach a wide range of audiences.
From developing a single end to end Data Science project with machine learning or deep learning models, you gain lots of knowledge. It provides you great insight and overview into the beauty surrounding the field.
Most of the projects you create can create positive implications for the future of Data Science and the world. Developing projects and working on them is easily the best part of Data Science.
Disclaimer: Some of these cons can be actually considered as pros depending and varying according to each individual’s perspective of the subject of Data Science. The terms stated are tending towards a more general audience and their approaches and outlooks.
Before moving on to looking at the five cons mentioned in this article, I would highly recommend you guys to check out one of my previous articles on ten wrong reasons to pursue Data Science. These cons apply to enthusiasts who are interested in the field but do not share any of the ten mentioned points in the following article.
1. Humungous Field
Data Science is a humungous field.
It includes massive sub-fields including topics of big data, data mining, machine learning, and so much more. It uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data.
Sometimes these massive subjects and fields are extremely strenuous. If you have a time limit to cover these concepts in a particular period of a short time, it can feel burdensome due to the high amount of technical and theoretical involvement required.
If you are stuck and confused, and you don’t have a plan to start preparing, I would recommend checking out the following article. It is a 12 step guide to master Data Science in 12 months.
2. Steep Learning Curve
We already understood that Data Science is a particularly humungous field with widespread information across numerous aspects and sub-fields of Data Science.
Some concepts of Data Science require more time and effort than others. Understanding the intricate details, math, and programming behind each individual topic is time-consuming and grueling in some cases, especially if you have a busy schedule.
Particular aspects of Data Science of Data Science have a steep learning curve. They require dedication and practice to master over periods of time. Similar to programming, Data Science is something you need to keep in touch with as time progress and keep brushing up your knowledge.
With so much content and study material out there for Data Science, it might be hard for beginners to interpret the endless possibilities, which always creates a doubt if it’s too late to start.
Good News is its never too late to start and you can always grasp these concepts and learn more. You can definitely learn and study Data Science to achieve desired results with a perfect plan.
3. Its Not Always Fun And Games
Data Science is not always fun and games.
It is cool to say that the learning process is fun and entertaining and stuff like that. But, it is important to note that Data Science has its qualities to be potentially stressful and difficult at times.
While working on a specific project in your casual time, you will probably realize the depth in the nature of the essential concepts you have to dwell into to fully master the subject.
Even in an industrial background, you are expected to complete the given work assigned to you within the particular time duration. The steps involved in the entire process are not to be taken lightly.
The steps involved include data collection, data visualizations, data analytics, cleaning up of data, which can sometimes be really annoying and hard to declare the best step to improve a model’s performance.
After these steps, you are immediately working on your data and start constructing your model. You need to ensure that your model is perfect, and you need to try numerous models and preparation processes.
Once you have your model built, it goes through tons of testing and validation processes to ensure that the model is suitable for deployment. Finally, after the desired expectation and results are acquired, your model is deployed.
The work procedure and stress involved in this cycle may not be suitable for everyone who is planning to work on Data Science in the future. However, some enthusiasts will love this entire workflow.
So, ultimately it is a matter of choice and what you are happy doing!
4. Endless Learning Cycle
After spending days understanding a particular concept to solve a complex task, you are able to successfully complete it.
Then, you start working on the project with your newly acquired knowledge, and mid way through the project, you take a break and read a new research paper.
After reading this new research paper, you end up finding out that the concept you spent hours studying is no longer relevant enough, and there is a new topic of greater significance.
Although the virtual story might be a bit exaggerated, most enthusiasts will share a similar feeling when they constantly have to keep up with the various evolutions taking place in the field of Data Science.
The learning process in Data Science is never stagnant. You need to keep updating yourself with the latest and emerging trends. However, this process might not be everyone’s cup of tea.
5. Data Science Can Potentially Be Demotivating Sometimes
Imagine you are working on a complicated Data Science project. Initially, you are very excited about the various things you can learn and objectively create.
As you start working on the project, you realize it’s not as simple as you thought. You end up getting stuck on an error, and you are totally lost. You Google, and you do everything you can, but the solution does not arise.
Firstly, this leads to frustration and then proceeds to create a feeling of demotivation. You start to doubt yourself and conclude that Data Science is not suitable for you.
Don’t worry, though! You are not alone, and tons of Data Science enthusiasts, aspirants, and now experts all have encountered these issues. The best solution is to overcome them and do the best of your abilities.
Data Science has an enormous and wonderful community with tons of well-articulated and descriptive resources. Make Sure you utilize the following resources to the fullest to benefit from them.
Conclusion:
When confronted with two courses of action I jot down on a piece of paper all the arguments in favor of each one, then on the opposite side I write the arguments against each one. Then by weighing the arguments pro and con and cancelling them out, one against the other, I take the course indicated by what remains.
In this article, we confronted the five pros and five cons to establish if Data Science is the perfect career option for you to choose in 2021. Depending on how well the article ages, I am pretty sure that these points will remain valid for years to come.
To end the article on a more positive note, Data Science has a bright future. If you are a beginner Data Science enthusiast, I would highly suggest you look into the numerous pros and cons of this spectacular field of study.
After analyzing the pros and cons, it is ultimately your choice to decide if Data Science is the best option available for you. And do you want to continue to kickstart and embark on your remarkable Data Science journey?
Either way, life is a long journey with variable paths and adventures. Make sure your decisions suit your personal interests. At the end of the day, being happy with yourself and what you do is the most important thing in life!
Data Science has valuable elements and tons of resources to develop into a blossoming data scientist. The subject may sometimes be complicated, but it is undeniable that you can achieve so many beautiful tasks and create an array of exciting projects in this field.
If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
Check out some of my other articles that you might enjoy reading!
11 Crucial Mistakes To Avoid As A Data Scientist!
8 Revolutionary Artificial Intelligence Technologies Of The Modern Era!
Mastering Dictionaries And Sets In Python!
10 Best Tools And Technologies For Data Science!
15 Awesome Python And Data Science Projects For 2021 And Beyond!
Thank you all for sticking on till the end. I hope you guys enjoyed reading this article. I wish you all have a wonderful day ahead!
