Overview
Due to the rapid evolution of data, more businesses are attempting to have a thorough understanding of their clients or rivals. Indeed's January survey indicates a 29% year-over-year growth in demand for data scientists and a 344% increase since 2013. Companies are always on the lookout for data scientists who can address operational issues. The duties might involve reducing operational expenses, extracting useful information from data, enhancing the user experience for customers while using applications or websites, and more.
Are you considering a career in data science given how prominent this sector is becoming? We hope you would read this article through to the conclusion before considering making a career change or entering the data science sector. As a result, you will be able to determine if you like it or whether it is appropriate for you.
1. The definitions of job titles and the necessary abilities are not yet clear
Startups began renaming existing job offerings from data analyst/statistician to data scientist or anything similar after they realized artificial intelligence was now a potent keyword to get financed. They receive more applications for job advertisements since the job title seems sexier.
However, if you read the job description, you'll see that some of the jobs are entirely different. Some desire Business Analysts who can use SAS and SPSS to answer problems. While they can all be referred to as data scientists, some want data engineers developing Big Data Hadoop systems and others want deep learning researchers utilizing TensorFlow and neural networks. These varieties are all quite distinct from one another and demand various talents. These categories—Data Scientist (Advanced Analytics), Machine Learning Engineers, Data Engineers, and Applied Scientists/Researchers—have arisen recently. Concentrate on only one.
2. Graduates are in high demand
As was already indicated, many people want to be data magicians. Not just mathematicians, physicists, or computer scientists, but also other natural sciences with a quantitative foundation like economists and psychologists. The issue is that the majority of employers do not seek out recent grads, and some do not even know what they are looking for. Some people might think that hiring one data scientist will take care of all their issues. Additionally, they recruit recent graduates of boot camps or college programs that have all the keywords on their CVs since they don't fully understand the needs. A contributing factor to the failure of 85% of data endeavors might be this. Additionally, according to TechRepublic, in 2019 the need for data scientists had already begun to decline. Young data scientists nowadays are quite frustrated and have a difficult time obtaining work, in part because of COVID.
3. Without a college degree, it is challenging
It's risky to think you could work with data without any formal schooling. If you are smart or lucky, it could be conceivable, but in most cases, you won't be called in for an interview. Math and statistics are at the heart of artificial intelligence, and these two subjects are typically the most challenging to study. You might not need it all, but in most cases, there are other applicants and Ph.D. holders in your competition. You need more time to learn the foundations than you would get from any of these MOOCs or boot camps. If you read the job listings, you will see that, depending on the jobs, a master's or even a Ph.D. is a bonus. In light of it, it is challenging but not impossible.
4. Have fun while coding
This has major significance. You might be able to code, but you can not want to spend all day staring at a screen filled with codes. Data science might not be the best choice for you in such a case. Whether you are resolving problems or coding, you will spend most of your time gazing at the codes. Say you are now employed at a business intelligence company. Your everyday task is to carry out requests or demands from the firm. Business and technical needs are mixed. Here, a technical requirement requires writing code to be fulfilled. As a result, if you often move to business responsibilities when you are tired of your technical work, you should think twice before taking the plunge. Start some projects that you find intriguing if you wish to love programming more. Make sure you have what you need to stay inspired and accomplish the assignment. A program is nothing more than a tool; study it to provide value rather than just for the sake of learning it.
5. Building datasets for applied machine learning
There is one thing that both Kaggle challenges and academic courses have in common that is not true in business: ready-made data collection. It makes perfect sense to study exploration, preprocessing, and modeling, but getting there requires a lot of work. Machine learning is gratifying if it adds value, but getting decent results requires a lot of observation and experimenting, and getting clean data takes much longer. Avoid applied machine learning if you are a perfectionist and have a poor tolerance for frustration; it will drive you crazy.
6. Deep Learning isn't extensively used.
Artificial intelligence has been increasingly popular in recent years, yet neural networks have many disadvantages. They take a lot of time to build and train, are prone to over-fitting, and require a lot of processing power. Although it is improving, infrastructure is still not up to par. Avoid pursuing a profession in the industry as a data scientist if you wish to employ neural networks. Because neural networks need too much magic and are frequently adequate using standard approaches, very few businesses employ them. Focus on academics and research, or to some extent businesses that specialize in ANNs, if you wish to leverage deep learning.
7. Act independently
You'll have to be self-reliant. Not that you can't seek assistance from your coworkers, but you'll need to figure things out most of the time. All day long, you will deal with a range of problems. For instance, you can have written software that has been working properly for a while. However, the software abruptly terminates. Or perhaps your current machine learning model for production suddenly performs poorly. You must ascertain the causes and come up with solutions on your own. In addition, it might be challenging to apply an author's notion in code when reading academic papers since you don't fully grasp the concept the author is attempting to convey. Although they might not have as much time as you would want for an explanation, you might still decide to contact your coworkers. They can provide you with some sample problems, but you are still responsible for solving them.
8. Competitive Setting
Technology is ever-evolving. There will be regular releases of new machine-learning models and new programming languages. You will need to constantly study if you want to keep up with technology. Additionally, everyone in the field of data science is talented. They will have at least one really strong expertise, which might be web crawling, communication, software engineering, debugging, visualization, modeling, etc. Prepare yourself to adjust to the challenging yet exhilarating workplace.
Challenges could also be enjoyable. You'll be able to pick up a lot of knowledge quickly. The data science department will be a lot of fun for someone who enjoys difficulties.
9. A false assumption of AI exists.
Although artificial neural networks draw inspiration from brains, they are substantially different from them. I don't see any AI battling humans for supremacy. Public views of AI and scientific perception are very dissimilar. It is difficult to understand why AIs play DOTA 2, create complex fakes, or produce music but are still not "intelligent," which is the problem. The fact that AI still relies on pattern recognition and breaks down quickly if those patterns alter appears to be overlooked. It is incapable of comprehension, thought, or dreaming. You'll be asked why your AI system can't accomplish XYZ, and it's unlikely that you'll be able to provide a solution. Now clarify why AI can overcome GO world champions but is unable to foresee some ostensibly simple business problem.
10. A lot of AI isn't genuinely AI.
There was a study on European AI startups in 2019. In essence, they discovered that 40% of AI startups don't use any AI at all. Some even just hired people to pass for AI. That has a very simple cause. Building AI systems costs money since they need resources like data, time, and labor. Sometimes letting people do the task is simpler and more affordable. Be careful not to act like the guy who is only "labeling stuff" to demonstrate that your business has AI knowledge. Be wary of job advertisements for data science positions and enquire about their data before applying.
11. Perpetual learning
Tools like Spark, TensorFlow, PyTorch, Keras, Scikit-Learn, and Pandas make your life simpler. Who knows if these tools will ever evolve, be replaced by better tools, or last forever? However, they are only tools. Instead of concentrating too much on such tools, concentrate on problem-solving methods and strategies. If PyTorch solves a problem better than Keras, learn it even if you adore Keras. You'll observe that the concepts behind these tools and frameworks are frequently extremely similar, and they function similarly. Programming languages are the same. Don't be that person who uses C++ to prototype machine learning models because he is too proud to learn Python. Be receptive.
12. Domains are important.
The basis of machine learning is data. Data relates to a domain. To comprehend the data, one must first grasp the domain. The risky and unworkable assumption that a data team can address any problem with data and without domain knowledge. The data contains several indications that can only be comprehended if you are familiar with how the domain and, more specifically, the processes, operate. not just the technical view, but also the commercial view. Experimenting with different methods is insufficient. At the very least as a data scientist for advanced analytics, you need to have solid communication skills to grasp domains.
13. Results Take Longer to Appear
You shouldn't anticipate getting a lot of enjoyment every day. A research scientist's job is quite similar to that of a data scientist. Always testing out several approaches to find which ones are effective. Your day will be spent going over and comprehending your data. Once you have those features, your model will be able to function properly. The performance of your model is then improved by experimentation with other strategies or frameworks. While the process can appear to include only a few phases, it requires quite a number.
14. Critical-thinker
One of the most vital abilities is the ability to think critically. Many ventures only succeed when someone challenges the prevailing strategy or goal. Does the target variable reflect our desired prediction? Need machine learning in this situation? Do we put in an extra week to get an extra 1% of it? Can we rely on such information? Is it a prediction that will come true? It might be difficult to ask these questions since we frequently disagree with the responses, but it is vital.
15. Explain Complex Insights in Simple Terms
In any profession, effective communication is a necessary talent. Before beginning the project, you will need to coordinate with the operational team, product team, etc. to agree on the project's specifics. Imagine that after many conversations about the design of the service, your sophisticated model has produced the desired outcome. You must now describe it in high-level terms so that people can understand it or have faith in your service. Furthermore, you will need to provide a very high-level explanation when data is incorrectly forecasted.
Conclusion
Data is given meaning via data science. It transforms raw data into useful solutions that businesses can utilize to get insights and identify market trends.
A massive revenue bubble has made data science a profitable vocation due to a shortage of skilled data scientists and a quick surge in demand. Since data science is still in its early stages of development, it is doubtful that all data scientists will be traveling down the same road. Your career will not be made or broken by a data science degree.
We draw the conclusion from this that mastering data science is crucial at this time, and that in order to work in the future, we must be data literate.
