5 Biggest Mistakes New Data Scientist Make And How To Avoid Them
As emerging technology is growing in an exponential manner, the organisations of all sizes are in the rat race and all are seeking to blend in the technology arena. According to this report, India has more than 50,000 jobs in both data science and machine learning lying vacant apparently because there is not enough talent to fill them. It is high time for those who really want to pursue a career in data science. In this article, we list 5 major mistakes that amateur data scientists make on the job and how to avoid them. 1| Focusing Less On Data This can be said as one of the major issues faced by an amateur data scientist of which they are unaware most of the time. Since you have started your career as a data scientist, you have grasped all the fancy algorithms of machine learning as well as deep learning. One thing to keep in mind that data plays a very crucial role and ignoring it will just be doing “Garbage in-Garbage out”. Also, after you have done analysing the data, runs the algorithm and got fair metrics, you think that the work is accomplished. That is where you ignored all the possible risks that may b seen in the future. Your eagerness for completing the task may lead you to risks such as data leakage, overfitting, and other biases. How To Avoid Them When building a model, most of the work is done at the data and features level. It is better to concentrate a little more on data than on the algorithm because your data and its features will shape your model at the last. The quality of your final model will completely depend on the data than the algorithm. 2| Assuming The Algorithm Is More Important Than Domain Knowledge Many budding data scientists rely on the theoretical knowledge of algorithms while trying to build a model. Knowing all the complex algorithms will not help you to build a fair working model. If you want to build a good model, it is crucial to know and understand the data you will be using, the purpose behind your model, and basically the domain knowledge. How To Avoid Them Pushing data randomly without exploring it much will show biases in the results of your model. Hence, it is needed much to do some exploratory data analysis which will help you to make some hypothesis of the model and you can be informed about what you are doing. 3| Making The Algorithm Complex This mistake may cost you a little more. Most of the time, budding data scientists forget that a simple machine learning model with good data can beat the complex algorithms in a model. It is not always needed to use fancy complex algorithms to build a robust model. The junior data scientist often gets lured by the fancy algorithms of deep learning and start thinking that everything can be solved using those algorithms with a little knowledge of exploring data and thus failed to meet the goal in the long run. How To Avoid Them Techniques like logistics regression, linear regression, etc. can sometimes outperform complex algorithms. Firstly, you should think what your main purpose is and then start with building a simple model with a less fancy algorithm. It is better not to make it complex which is already complex. 4| Spending Less Time On Exploring And Visualising Data Most of the time the budding data scientists prefer to build a model than to visualise and explore the data. They lack the idea that by spending more time on understanding the data can gain you deeper insights on what the outcome of your model will be. Being curious and eager to finish building a model and complete the task while ignoring the exploring and visualisation part can cause serious damage to the model. How To Avoid Them The basic and most important tools of a data scientist are to explore and visualise the data. Understanding your dataset is the foremost task that an aspiring data scientist should do as it will later reflect in your model. 5| Less Communication This usually happens to amateur data scientists that they hesitate to question about the difficulties and when you are not ensured about certain issues. They often shy away from putting their views forward as afraid of being criticised, forgetting that without drawing any feedback one cannot improve much further. How To Avoid Them To be a keen data scientist, you have to be a good communicator. It really helps a lot. One should always keep in mind that data scientists are meant to solve other people’s issues and without communicating whether it be inside the organisation or some outside business clients, it is merely a difficult as well as unsolvable task.
As emerging technology is growing in an exponential manner, the organisations of all sizes are in the rat race and all are seeking to blend in the technology arena. According to this report, India has more than 50,000 jobs in both data science and machine learning lying vacant apparently because there is not enough talent […]
As emerging technology is growing in an exponential manner, the organisations of all sizes are in the rat race and all are seeking to blend
["Data Science", "Data Scientist"]
["data science", "Go", "machine learning", "programming_languages:R", "AI", "ML", "programming_languages:Go", "deep learning", "GAN", "Data Science", "Data Scientist", "R"]
["AI", "machine learning", "ML", "deep learning", "data science", "R", "Go", "GAN", "programming_languages:R", "programming_languages:Go"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.