Top 10 Undesirable Data Science Skills That Will not Reflect on Your Salary

For data scientists, being aware of certain blind spots is equally important as having subject expertise

Data Science as a futuristic technology has become one of the go-to destinations to resolve all sorts of business problems. And not to mention, the salary it offers is a subject of envy. The average data scientist's salary increases with experience level, according to Payscale data from July 2022. With less than one year of experience, these professionals earn an average of $85,730. After 20 years or more, their salary increases to an average of $134,980. If you are an aspiring data scientist, you should keep in mind that it has some blind spots, you need to circumvent to ensure your efforts do not go waste. Here are the top 10 undesirable data science skills that would probably not reflect on your salary.

1.Collecting data the wrong way

Checking the quality and quantity of data is what most data scientists think is important focussing more on including as many parameters as possible. But, it is equally important to record the data correctly, which changes over time. At times including a large number of independent variables make it difficult to design predictive ML models.

2.Depending too much on intuition

Having theories and hunches about what a data set will show is very common with data scientists. But keep in mind, that not all the time they work the way you assume. Doing an exploratory analysis of the collected data to determine if the data has some story to tell is as important.

3.Depending on algorithms all the time

Algorithms detect patterns but not unfailingly. Looking at and depending on what the company has been doing will not yield profitable results either. Looking into the past as a sole predictor will prevent data scientists from exploring new avenues.

4.Not using control groups to test the new data model

To make the most of a model, you might want to use it everywhere. In reality, it doesn't work that way. It is highly important to identify groups who do not use the model but would want to trust it.

5.Considering targets more important than a hypothesis

Measuring performance in numbers is perhaps the reason why data scientists prefer targets to a hypothesis. But testing the hypothesis about what will improve things, either with a control group or by exploring the data is as important.

6.Not building additional data models:

Depending on old models, that might have worked for your problem will prevent you from keeping up with the market trends. With time, as the requirement changes, new models, a few of them for experimentation are an absolute must.

7.Optimizing for the wrong goal

It primarily happens because data scientist prefers automation to monitor the outcome. With control groups, it is equally important to measure the quality of the output and monitor it throughout the process.

8.Not knowing the right tools

Trying out advanced tools is indeed exciting, but they end up overkilling your model. A lot many times, using simple methods such as logistic regression, decision trees will work. Unless the simplest options are explored first, there is no point in adopting sophisticated algorithms.

9.Adopting unsuitable implementations

It is tempting to adopt implementations that worked for others. Given the large number of open-sourced algorithms that exist in the market, it is easy to prototype a model. However, not all implementations come to the rescue when it comes to finding a solution that fits your problem.

10.Misinterpreting what users understand

Business executives may not be able to do statistical analysis all by themselves, but of the vast experience they have, they understand certain crucial parameters well, sometimes better than you do. Ensure that the business partners are informed of how much trust they can put in the results.