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Jul 04, 2023

10 Deadly Mistakes to Avoid That May Cost Your Data Science Career

Divyanshi kulkarni Follow DataDrivenInvestor -- Listen Share Data is to the business what blood is to the body. And what you make of data is all that matters in today’s hustling world. It is just

Divyanshi kulkarni

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Data is to the business what blood is to the body. And what you make of data is all that matters in today’s hustling world. It is just data, but together with Artificial intelligence; you make it something more empowering and impactful. With the vast variety in data, comes mass anonymity that seldom begets inappropriateness and mistakes.

Beginning to understand recent statistics from ExplodingTopics.com reveal approximately 328.77 million terabytes of data being generated every day. The year 2025 is projected to experience a data generation of around 181 zettabytes. With so much happening on the data generation front, it is quite possible that miscalculations and mistakes happen. It is essential to counter these at the very beginning as these can cost heavily for any business ahead.

Mistake 1: Incompetence to define the exact business problem

Defining the business data problem at hand is of utmost importance. For that is going to decide the direction of business outlook. Novice data science professionals, in the initial years of their data science careers, forget to place any importance on this critical aspect of data science. In order to have a clear vision of what data science will bring at each step, it is highly crucial to scope the full potential of a project right from the beginning. This is why it is essential to always understand the business requirements.

Mistake 2: Lack of research and planning

Another mistake that data scientists must try to avoid is not gathering enough data, not researching enough, and lacking a chalked-out plan for the business problem. Possessing sufficient data to accurately answer the research questions is the way to go. If you do not have sufficient data, you will not be able to infer and draw reliable conclusions from your analysis. Addressing the questions such as: What are the questions that we are trying to answer and how will we go about answering them? Why does the data behave in a certain way? What story it is trying to tell us? is highly suggested. Directly landing into a problem without having an antidote plan might cost dearly to the business.

Mistake 3: Choice of inappropriate Data Visualization methods

Cleaning and preprocessing are the foremost steps as you plan to dig deeper into the problem. Making the right choice of data visualization techniques and other tools is critical to its success. These are deemed essential at all stages of the project development. Bad or misguided visualization may lead you astray; deviating from the ultimate business goal.

Mistake 4: Failing at Efficient Model Fitting

Failing to deploy and utilize the correct machine learning model fit is a big red flag. Optimizing the model for the data you have is of utmost importance. As the data changes and evolves over time, it calls for timely alterations and optimization to be done in the values of hyperparameters in order to reach peak performance.

Mistake 5: Excessive Focus on Theory than Performance

Excessive reliance on theory than the actual model performance is a huge backlash for the entire project at hand. The accuracy of your solution depends heavily on the algorithm you chose, the data you are working with, and the parameters you set. Looking at the practicality of it all will surely make a positive impact on the result.

Mistake 6: Failure at Customizing Solution

It is highly advised not to reuse program implementations for more than one project. Data science is not a one-size-fits-all kind of stream. One solution designed for one project may or may not be fully applied to another project. No two business problems are the same; hence it calls for rigorous indulgence in making custom-made solutions.

Mistake 7: Wrong Tools Choice for the Problem

A commonplace mistake committed by most data scientists; it becomes a daunting task for them to select from an infinite number of tools that can help in different stages of project implementation.

Mistake 8: Fading Essence of the Business

All data science trickles down to one single point of making informed business decisions. This is why it is essential to take time to understand the how and why of the data problem; instead of landing in a tight spot for business.

Mistake 9: Amature at Data Science Basics

This goes to the deepest corners of the data scientists’ qualifications and needs no mention that it is imperative to gain mastery at possessing a solid knowledge of probability, statistics, and machine learning. This is surely going to impact your data science problem resolution as well as leveraging big benefits for your data science career.

Mistake 10: Failing to Comprehend the Data Biases

Model biases occur when it becomes evident that we have not sufficiently trained the models in order to harness their full potential while making machine learning predictions. This usually happens primarily due to the incapability of hyperparameters to tune, not giving enough data, and not adding any further features that would make a significant impact on the model.

Tips to Avoid Mistakes as a Data Scientist:

· Make sure you understand the context of the problem and why it is important

· Clearly articulate the research question you are trying to answer

· Identify the key variables and their relationships

· Delegate tasks to other team members

· Seek clarification on the business problem from the stakeholders

· Break down the project into smaller manageable tasks

· Determine the desired outcome of the analysis

· Determine the magnitude of data that is needed to answer the research question accurately

· Identify the data sources and ensure their reliability

· Find a mentor or join an online community or forum to seek advice

· Train the model with the available data while ensuring that the model reaches the global minimum error mark

· Get certified on credible data science certification in order to master the requisite skillsets to encounter such business problems efficiently

· Check the quality of the data and clean it as needed

· Be aware of any potential biases in the data

· Strengthen your data science foundation

· Research well before selecting the data science tool for the business problem

Conclusion:

No doubt, this blog has taken you deep into the critical aspects of finding the right solutions for business data problems. This would clearly set a path for a comprehensible data science resolution with the right decision at the right time. Make the most of this blog and thrive in your career!