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Avoid these 5 Mistakes as a new Data Scientist

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In this article at OpenGenus, we have explored 5 common mistakes one make at their first job as a Data Scientist fresh out of School. We wrap up with 5 tips as well.

Table of contents:

  • 5 Mistakes to avoid
  • 5 tips to follow

5 Mistakes to avoid

As a new Data Scientist who just completed his or her education at Graduate School and joined the Industry, you are prone to make mistake and learn along the way. These are top 5 mistakes that you should avoid at all cost:

  1. Using Complex Models without Justification: New data scientists get excited about using the latest models, regardless of their level of complexity. It is important to justify the use of a model based on the business problem and data available. If there is no clear reason to use a complex model, then it is better to use simpler models.

For example, if you can use a Regression Model like Logistic Regression, then use this instead of going for a complicated and general purpose model like ResNet50 or other CNN models.

Large Language Models (LLM) are in hype but avoid these if your problem can be solved using simpler models. Complicated models can be hard to debug and consume a lot of energy.

  1. Overcomplicating Data: While data preparation is an integral part of the job, one overcomplicate and waste time on things that did not matter. Learn to simplify the process by prioritizing important features to save time and deliver insights quickly.

If possible, use standard datasets like ImageNet or COCO dataset for Image based tasks.

  1. Not Testing Assumptions: One make assumptions about my data without testing them. This can lead to incorrect conclusions and incorrect solutions to business problems. It is important to test assumptions and make sound inferences based on the data.

Note that even basic assumptions that may seem to be correct can be wrong in a given context. This requires expertise and experience.

  1. Not Communicating Effectively: At a company, you need to work in a team and your work should be easily accessible to other Data scientists. For this, communication in form of daily conversation, email or documentation is necessary. Effective communication is key to gaining credibility and earning buy-in from decision-makers.

  2. Ignoring Business Context: One of the most significant mistakes beginners make is to ignore the business context of the organization. Beginners get focused on the technical aspects of the job that they forgot about the business goals and objectives. Approaching a problem with a value delivery focused mindset is a gamechanger.

Discuss with your manager to understand the business goals and work accordingly.

5 tips to follow

These are 5 tips that you should follow while avoiding the avoid mistakes:

  1. Define the problem: Start by defining the problem and why it matters to the business. This will help you stay focused on the end goal and develop solutions that align with business objectives.

  2. Continuously learn: The field of data science is continually evolving, so it's essential to stay up to date on the latest tools and techniques. Take online courses, attend conferences, and participate in local meetups to expand your knowledge base continually.

  3. Collaborate: Data science is a collaborative field, so seek out opportunities to work with others. Collaborating with other professionals with different skill sets and perspectives can help you see problems from different angles and arrive at better solutions. Discuss with fellow engineers and your manager daily.

  4. Tell a Story with Data: Visualizing data can help tell a story, making complex data more accessible to stakeholders. Developing skills in data visualization can help you communicate your findings effectively.

See this to learn about Data visualization.

  1. Focus on Impact: Always keep in mind the impact of your work on the business and end-users. Understanding the impact of your work can help you make better decisions and prioritize your time and resources.

Best of luck becoming a great Data Scientist.

Avoid these 5 Mistakes as a new Data Scientist
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