5 Myths of becoming a Data Scientist — The Sexiest Job of the 21st Century
We often hear the terms data scientist or machine learning engineer in many job postings. Moreover, industries are transitioning to the field of artificial intelligence, trying to establish their leadership in the industry demanding predictions and outcomes. Though there are similarities between data scientists and machine learning engineers, there are quite a few subtle differences that one must consider when applying for jobs.
When we randomly browse the term data scientist in job search platforms such as Indeed and try to find the average salary of a data scientist, the amount would be a staggering 1,19,690 $ per annum respectively. On top of that, people who have knowledge of the implementation of R and python along with SQL, they become highly sought after in many industries.
Though there has been a lot of demand in the field of data science, there are a few myths that people who are entering the field would have during the course of becoming a data scientist. In this article, I’m going to highlight common myths that people have about becoming a data scientist.
Myth#1 Data Scientists are PhD or Masters holders
Though it might be useful for a data scientist to have a Masters’s or a Ph.D., I would say that it is passion towards the field and various methods and strategies one learns that would help him/her become a data scientist. There are so many machine learning engineers or data scientists who don’t have masters or PhDs for that matter. One has to focus on the most important skills needed in the industry along with business acumen to apply the knowledge so that problems could be solved easily using machine learning. Apart from the machine learning models, one has to learn to use the python programming frameworks that would ensure that they learn the skills needed for a data scientist.
Myth#2 Domain Knowledge is not very important for Data Scientists
During the process of completing a few courses and understanding programming languages needed for data science, one might assume that domain knowledge is not as important as learning to code. With domain knowledge, however, one would be able to give advanced features and important tools to the machine learning models to predict more accurately. To elaborate, machine learning and deep learning models require data that would help them to classify the points into different categories. It is important to give the right features in our data so that the models could use them and make predictions on the training and testing set respectively. This is where domain expertise and knowledge come into play. Since the data that a machine learning engineer or data scientist work with might not contain very useful features, steps must be taken to give important features to the machine learning models for prediction. These features would be created well if there is good domain knowledge for a data scientist. Therefore, having good domain knowledge would ensure that better features are given in data that is fed to machine learning models for prediction.
Myth#3 Learning to Code is enough to become a great data scientist
Learning to code using either python or R could be really handy when performing visualizations from data and generating predictions. Furthermore, learning a few useful libraries and applying them using Python or R could be really helpful for machine learning applications. Nonetheless, it is not the only requirement to become a data scientist. One must also learn to think like a problem-solver, knowing the proper application of different tools so that it ensures that they are generating the right predictions. All the data points that we are going to be discussing are laid here and we are going to get a good understanding of them. Therefore, one must also learn to use different machine learning models for predictions.
Myth#4 Giving more data would lead to better accuracy in the models
In order to test the performance of machine learning models, one would have to consider either the accuracy in the case of classification problems or the mean squared error in regression problems. People might sometimes assume that the more data that is given to the machine learning models, the better would their performance be on the test set. But sometimes data that is given to the models might be erroneous and not really useful. Additional data might sometimes have a distribution that is quite different from already existing data that gives good predictions. As a result, this could lead to getting lower levels of accuracy or mean squared error in the case of the performance of the machine learning models.
Myth#5 Data science is only for bigger organizations
Since there is massive data available in the form of images, text, videos, and audio, it would make sense to make use of this data and ensure that proper machine learning mechanisms are followed to ensure that we are getting the best results on the test set. Since data science has often been talked about by Tech Giants such as Google, Microsoft, and Apple, it is common to misinterpret that only those organizations could use data science to scale and augment their business. Nevertheless, there could be many smaller firms and organizations that would take into account the data and get predictions for different use cases respectively. Therefore, it should be taken into account that smaller firms and entities could also use data science and machine learning to get predictions and improve their business outcomes.
All-in-all, these are some of the myths about becoming a data scientist. It is often common for newbies in machine learning and data science to consider these myths and interpret them as real. As a result, this could slow down their progress towards learning new things and experimenting with the latest machine learning algorithms. Hope you found this article helpful. Feel free to share your thoughts and feedback in the comments section. Thanks!