Frequent mistakes Data Science freshers should avoid

Published on:

24 Jun 2022, 6:30 am

Majority of data science resources do not teach you what the industry expects from a data scientist.

As the world becomes more linked and more organisations become data-driven, it appears that every business will require a data science practise. Therefore, there is a high demand for data scientists. Even better, everyone recognises the industry's skill shortage.

To be truly effective, a combination of problem-solving, systematic thinking, coding and various technical skills are required. If you come from a non-technical and non-mathematical environment, chances are you learned a lot through books and video courses. The majority of these resources do not teach you what the industry expects from a data scientist.

Here are some mistakes you should avoid in this field as a beginner:

Directly pursuing Machine Learning Techniques without First Learning the Fundamentals

The majority of people who want to become data scientists are motivated by movies of robots or amazing prediction models, as well as, in some cases, large incomes. Unfortunately, there is a long road ahead of you before you get there.

Before you implement a technique to an issue, you need to learn how it works. Learning this will assist you in understanding how an algorithm works, what you can do to improve it, and how you can build on existing strategies. Because mathematics is vital in this situation, knowing certain concepts is always beneficial.

Using Only Certifications and Degrees

Certifications and degrees have popped up almost everywhere since data science became so popular. A quick peek through my LinkedIn feed reveals at least 5 certification photos proudly displayed. While obtaining that accreditation is a difficult task, relying only on it is a formula for catastrophe.

Believing that what you see in ML Competitions is Representative of Real-life Jobs

This is one of the most common fallacies among aspiring data scientists today. Clean and pristine datasets are provided by competitions and hackathons. Even datasets with incomplete data don't need you to exhaust your brain cells simply work out an imputation approach and fill in the gaps.

Unfortunately, real-world enterprises do not operate in this manner. There is an end-to-end pipeline that requires collaboration with a large number of people. Almost usually, you will have to work with sloppy and filthy data.

Prioritizing Model Accuracy over Domain Applicability and Interpretability

As previously stated, accuracy is not necessarily what the business seeks. Sure, a model that detects loan default with 95% accuracy is wonderful, but your customer will dismiss it if you can't describe how the model reached there, which features drove it there, and what your thought was when constructing the model.

Many Data Science Terms on Your Resume

If you've done this previously, you'll understand what I mean. If your resume presently has this issue, fix it right away! You may be familiar with a variety of approaches and technologies, but merely stating them will turn off potential hiring managers.