Right Questions Make Good Data Scientists! Prepare Yourself

The best data scientists begin by asking the right questions.

You're probably right if you believe your employer doesn't appreciate you, but perhaps not for the reasons you think. Your worth to your employer depends on the value you produce. It doesn't turn them into villains. It's just how business is done. Data scientists, however, have an issue with it. Data scientists are not taught to provide value to businesses. They are taught to use data to fit models. Because it's a relatively young field, employers don't often realize what value data scientists might add if they just asked. In a typical case of "don't know what you don't know," the outcome is a disappointment for everyone.

Data science is now perceived by employers as a failed venture. In the meantime, dissatisfied data scientists leave their jobs in search of ones where they will be genuinely valued. When it occurs, it is terrible, but it doesn't have to be this way. In reality, it's not as difficult to change the situation as you would think. The key is to concentrate on providing value. You may start by altering the questions you ask yourself and other people. Small changes in the questions you ask may have a huge impact on how much people regard you as a data scientist.

Questions to ask:

1. Exactly what question are you aiming to answer?

It is critical to at least have a broad understanding of the topic you are seeking to answer before querying any data collection. It is not desirable to test a number of hypotheses before deciding which are important. If you did that, you would run into the problem of multiple hypothesis testing.

2. Are you certain of the specific goal of your measurement?

Once you are aware of the issue you wish to address, you must decide how to do it. Choosing what to measure is a necessary step in this process. The same question may frequently be approached in a variety of ways. However, if you select to measure the wrong impact or variable, then you may not be able to properly address your problem. Therefore, it is crucial to carefully assess if what you are seeking to measure is the best method to respond to your interest-piqued inquiry.

3. Do you have the relevant information to address your query?

Therefore, you need to have the data to conduct that measurement once you have an issue to solve and know how to assess the effect you are interested in. The greatest and most intriguing topic and variable to study are useless if you lack the resources to do it. It is quite unusual to have the ideal dataset to address the precise query and gauge the particular effect of interest. As a data scientist, having incorrect data may be quite frustrating.

4. Do you know how your data was properly collected?

Just because you know to determine your effect of interest and deal with the problem, you're thinking about it doesn't mean you should relax. Datasets do not just magically materialize, complete with all the essential details. There are many different methods for gathering data. They usually involve either humans or machines, which leaves room for error. Try to account for as many potential reasons for making a mistake as you can while doing analysis, or at the absolute least, be aware of them.

5. Are there any moral questions to be answered?

This is a crucial query that is frequently disregarded. Are the conclusions you've reached based on your analysis ethical? You could be perplexed by the idea of ethical issues in analysis. It seems impossible that data that has been properly gathered, examined, and analyzed could be immoral. But in actuality, prejudices like sexism and racism occasionally find their way in unintentionally. Particularly when the reality we are using statistics to represent has elements like injustice and bias.

6. Who will read your analysis and how well-versed in statistics are they?

Who will read your analysis or be the recipient of it? Is the person using a product or a website? a team in data science? The sales team, business development team, marketing team, etc. There will be a range of statistical knowledge among these groups. Therefore, you might need to adjust your analysis to fit your audience, in particular, the techniques you use, the way you present your findings, and any limitations they may have. As a data scientist, you must make sure that you correctly communicate your conclusions to the target audience. You must consider how much statistical knowledge your audience possesses.

7. Do you need to be prepared to defend or challenge the strategies you choose to respond to your query?

It's critical to consider how much knowledge you will need to be able to critically examine the analysis or model you develop before choosing the technique for your data science project. You might occasionally need to be able to describe every action you made in great detail. This details all the factors that were considered and their relevance. Sometimes, precision is the key to success. In such instances, as long as the model makes predictions with a minimal amount of inaccuracy, it doesn't matter what is put into it.