It’s 2022, and the hype around data science is still on the rise. The field is so popular that people are switching careers to become data science professionals. Everyone knows that being a data scientist is not an easy task but the subject complexity hasn’t deterred the enthusiasts.
The buzz is not going to wane anytime soon, but there are some things that should. As data science trends as a preferred choice of career, there is an emergence of common mistakes that beginners in the field often make.
In this article, we will cover 9 mistakes that you should avoid making as a data scientist.
Here’s a list of topics we will be covering -
- Entering the data field for the hype
- Thinking tactically and not strategically – Not prepping enough
- Issues when working with dependencies
- Algorithms > Everything Else thinking
- Overfitting and Underfitting
- Ignoring the Business
- Getting satisfied with a mediocre solution
- Lacking mathematical & programming skills
- No tests for errors
1. Entering the data field for the hype
Data science is the internet’s hot topic right now. There is no doubt about that but that is not a good reason for you to choose data science over other options. As lucrative as it may sound, no work is going to do itself.
Honestly, data analysis, hypothesis testing, business decisions, etc. are not easy tasks. The processes behind them are grueling and need loads of experimenting, failing, and repetition. If you are pursuing studies in this field just for the sake of money or prospects, the job might discourage you soon enough.
That is why you should avoid this most common beginner mistake of pursuing something just because several others are doing the same. After experiencing data science from up close, the only suggestion one can give is to avoid choosing it for its popularity.
There will be another technology creating hype after a couple of years. You don’t want to keep switching and chasing the hype. So, before jumping in, ensure that you are genuinely interested and determined enough to follow through.
Do you know what a data scientist’s day looks like?
Are you interested in it?
Do you have the right skills for the job? Are you willing to learn them?
If there is a job X in the fisheries sector that pays 3X the amount of the data scientist job, which one will you pick?
2. Thinking tactically and not strategically – Not prepping enough
The easiest thing to do when given data is to jump right into it. That’s a rookie mistake and people at the start of their data science career often fall for that.
Work out a plan instead. Decide on the questions that the data must answer. Think about the strategy that you will follow to complete the data science project that is assigned to you. If you don’t do that, you might find yourself lost in a beehive of possibilities once you are inside.
Thinking as you go with no roadmap to monitor would cause you troubles with your project completion.
One essential step to making such strategies is analyzing data to understand its potential. Thus, EDA (exploratory data analysis) is one of the key skills for you to become a successful data science professional. It not only helps you visualize your data but also lays down the foundation for the future steps to come.
As a beginner, if you think you’ve done enough exploration and visualization, do some more. Keep at it until you have some interesting insights that you didn’t have before.
Once you are done with that, take your time to cleanse the data. Make sure that the data stands under the scrutiny of common sense and domain knowledge, that it doesn’t have too many missing values, and is good enough to be used. Then organize it in a structured format to be used by your machine learning model.
3. Issues when working with dependencies
If you are a data scientist without hands-on work in software engineering, it might come as a challenge to you. Even though your work in the software engineering part should not be that hard, beginners often fail to handle the dependencies well. The first rule of working with dependencies is to pin them in a requirements file.
# Inside requirements.txt
Tensorflow == 1.0.2
This ensures that the correct version of the dependency is used. In the example above, when pip is used to install a dependency, the program is aware of the version that it needs to install. This way, the program always works as expected.
pip install -r requirements.txt
Similarly, you would need to manage dependency clashes as well. It happens when one package needs one dependency while the other package needs another version of the dependency. If it is for separate programs, using different virtual environments can help.
You can discover dependency clashes either via pip check or pipenv package manager.
It is industry best practice to never use the latest version of dependency if it is not exclusively required. It’s tempting to have everything set to the latest but don’t do that. This is because it is more likely that some undiscovered bugs remain in the latest version that might cause you problems later.
4. Algorithms > Everything Else thinking
You have spent a good amount of time learning machine learning algorithms. It is possible to think that they are all that matter and once perfected, you can solve any problem using them. However, that’s not always the case as there are other important components of machine learning problems.
Data – If your data is lacking in either quality or quantity, no algorithm can help you out. If you are teaching the wrong things to your machine learning model, it is going to fail in the real world. Sometimes, it is the data that holds you back.
Domain Knowledge – Data Scientists use ML/DL models to solve problems in different industries like Healthcare, Biotech, Aerospace, Logistics, etc. In addition to understanding the algorithm you are going to use; it also helps to understand the context of the problem and to have crucial insights that might make the job of your model a lot easier.
It enables you to understand the context of the problem you are working on a lot easier.
Similarly, it is just as important to learn the basics first before rushing to implement algorithms. While it might serve some academic value to learn those algorithms right away, you will be lost in the real world without the basics.
5. Overfitting and Underfitting
Honestly, it is wrong to classify these as beginner mistakes as even the experts struggle with these issues. However, it is easier for beginners to ruin their models without being aware of the same.
Overfitting means that your model fits your training data so closely that it learns even irrelevant and useless patterns as well. It means that your model will fit your training data really well. However, it won’t generalize well and thus won’t do good on your test data.
Underfitting, on the other hand, means that your model hasn’t learned important patterns from your training data. Thus, it fits neither the training dataset nor the testing one.
Underfitting and Overfitting are among the most common issues that might be hurting your AI model. If your model has a very high accuracy for your training dataset but the accuracy drops drastically when trying to predict the testing data, you’re quite possibly overfitting your training data.
6. Ignoring the business
Pursuing your career as a data scientist might feel like an intellectual pursuit to you. That’s why most beginners lose sight of the business context of what they are doing. As a data scientist, you are often working for a company that has hired you to solve a business problem.
You are there to contribute to that organization’s success. If you get too focused on the tools to be used or refining parameters that are not relevant to the business, you are doing nothing more than wasting the time and resources of that organization.
Data science in an organization is not an academic pursuit. It is a completely practical role. So, it will serve you better if you focus on gaining domain knowledge of your company’s industry.
If you collate essential datasets and develop models that address issues faced by the organization, it will give you a better chance of making data science a contributing factor to the organization’s success. That is why you should never ignore the business aspect of what you are doing.
7. Getting satisfied with a mediocre solution
In the tech world, especially while competing against other big organizations, just solving a problem is never enough. You must also ensure that your solution is extremely top-notch.
Thus, you might be needed to experiment with different strategies, and solutions before letting one model be your final.
Additionally, once you have developed a solution, you need to tune its hyperparameters to ensure that its performance remains the absolute best. This would be especially required in scenarios in which new data is coming in regularly and the model needs to learn and adapt to the changing realities.
Thus, you cannot ever be satisfied with a model so much that you stop iterating on its improvement cycles. Instead, a large part of your data science career would be dedicated to improving models that have already been developed by your predecessors.
An essential factor to ensure that your machine learning model remains a top performer is your own knowledge. Technology is changing every day and if you do not keep tabs on the latest developments, it won’t be long before you and your model get left behind in the past. This necessitates that you keep up with how your peers in the data field are solving their problems. If there’s a new disruptive idea that could make them leap ahead, you need to be in on that information as well.
8. Lacking mathematical & programming skills
Sometimes, people get into data science thinking that pre-made tools and libraries are enough to help them through the job. However, while these tools are good for academic projects, you must understand the math behind the algorithms to have a real chance at creating something that works in the real world.
Beginner data scientists often lack the mathematical intuition behind the algorithm. This severely affects their ability to choose the right algorithm for the job. Not only that, they struggle with tweaking the algorithm/model to even a slightly changed requirement and cannot troubleshoot any problems the model faces.
Result? They are more inclined toward recreating models which are similar to the ones that they have seen before.
Similarly, there are certain data science professionals who are severely lacking in their programming skills. Data science is at the crossroads of many streams today and one of them is software engineering. So, it will serve you right if you have those skills.
Your lack of skills will often manifest in the troubles you will have while handling data. Importing modules & data, cleaning it, organizing it in a structured format that is suitable, deploying machine learning models, and a lot more require you to have good enough programming skills.
If you are venturing out in the field, it would be better if you are fluent in at least Python. Some positions will require you to know R, Scala, Excel, and SQL as well. That’s why it is essential that you plan ahead a roadmap to get you successfully through your data science career.
9. No tests for errors
Writing tests is a smart practice in the field of software development. It ensures that your code doesn’t end abruptly after running into runtime errors post-deployment. Not that you should wrap every statement with a try or if statement, but the main code logic should always contain a test that lets your program soft-land in case of a crash.
Beginners who have little to no experience in hands-on software development often fall prey to this issue. They learn their lessons, but the hard way.
The key is to keep reading and reviewing your seniors’ code snippets to identify patterns of clean and healthy coding styles. Otherwise, you can simply ask your peers for advice on how they incorporate tests in their production code.
To sum up:
This guide is not to deter you from becoming a data scientist. Rather, it is to ensure that you transcend your career rather smoothly. You don’t need to read and remember everything here. Just keep it handy for reference every time you start a new data science project. Before you know it, every recommendation in this article will become a ritual you will seldom miss.
