5 Common Data Science Mistakes and How to Avoid Them
By Aditi Bhat
Many a times, mistakes have led to some fantastic discoveries – penicillin, an antibiotic that saves millions of lives, is one such example. This holds true in case of data science as well. Data scientists sometimes end up finding new patterns and trends when their calculations and data arrangements refuse to run smoothly or don’t exactly add up. But not all mistakes lead to new discoveries; in fact, most of them lead to a dead end – which translates into an insight-less or misleading study for a data scientist. The number of variables is high and the final insight expected out of the study can have huge long-lasting implications; hence, a data scientist needs to try and minimise errors in order to maximise the odds of a fruitful study/analysis.
This famous quote by Sherlock Holmes fits perfectly for the role of a data scientist as well
“My name is Sherlock Holmes. It is my business to know what other people don’t know.”
The margin of error for a data scientist is minuscule. Organizations around the globe invest hefty amounts on data scientists and their work and therefore, data scientists can’t possibly afford to make mistakes.
Here are five common data science mistakes and ways to avoid them.
1. Choosing the wrong tool to visualize: The experts of data science focus more on learning technical aspects of the analysis rather than using different visualization tools that can deliver faster results.
As the popular saying goes “A picture is worth a 1000 words.” So it is crucial to choose the right visualization tool in order to monitor exploratory data analysis or to represent the result. Hence, it is advised to get an insight of what the data is all about. Representing data with rich visuals makes it easier to study it and even spot trends.
2. Analysis without a Query: If there is no query, question, concern or objective in mind before the analysis of the data, it's equivalent to running around like a headless chicken and the whole purpose of data science invalidates. Therefore, it is essential for any data scientist to have the project goal before collecting data and a perfect model goal during its analysis.
3. Sampling Bias: Sampling Bias is perhaps the biggest data science mistake that could lead to an incorrect result. Most of the data scientists choose to believe that their selected sample is a good representation of the entire population and conduct the analysis on it. In order to avoid such a mistake, it is important to bifurcate the population into clusters and take the sample from every cluster formed. This method eliminates poorly informed decisions and skewed data models.
4. Ignoring the probabilities: It is a fact that numbers don’t lie but in no fashion does it mean that there is only one possible conclusion after analyzing the numbers and data. After analyzing, data scientists often conclude that in order to achieve Y result, Z action has to be taken. Before concluding anything, the data scientist should shoulder the responsibility of scenario planning and probability theory. More than one possibility almost always exists for a particular query. Data scientists have to keep various possibilities in mind and make informed choices accordingly. These aspects help in delivering correct odds.
5. Ignoring the Historical or Secondary Data: Ignore historical data at your own peril. When a data scientist is involved in collecting primary data, he/she sometimes ignores the secondary or other historical data. Also, in organizations where there is no warehousing in place, the data scientists have to rely completely on new data. However, a lot can be learnt from analysing historical data. It is suggested that you refer and understand previous data too before modelling newly collected data.
James Joyce, the renowned Irish novelist, once said, “Mistakes are the portals of discovery.” If some mistakes could make a business, then there are those mistakes, especially related to Data Science, which hold the potential to break the business.
Lastly, don’t be afraid to try new things due to the risk of failure. It’s our mistakes that we learn from and become better. But it’s important not to make obvious and inconsequential one like the ones mentioned above. As a data scientist, it’s imperative to you track your mistakes, learn from them and strive to avoid them in future. Good luck analysing!