1. Learn from My Data Science Rookie Mistakes! 2. Mistakes to Avoid in Your First Year of Data Science

  • Data science job requires more than just coding algorithms, it’s about manipulating and analyzing data. πŸ€”
  • Learning everything in data science is nearly impossible, focus on one specialty to make real progress. πŸ“Š
  • Best data scientists are great at explaining their findings in a clear way, not just knowing everything about everything. 🧠
  • Always triple-check your data, mistakes can have real-world consequences. πŸ“ˆ
  • Don’t rush to deliver work, take your time to ensure high quality. πŸ•’

The Importance of Version Control πŸ“

Chances are, when learning data science, you’re working by yourself. The need for Version Control in your code may seem unlikely, but every data scientist in the industry uses git or GitHub. It’s essential for working in cross-functional teams and managing the same code base, making it paramount to learn Git basics and set up your own GitHub profile.

Key Takeaways

  • Learning Git shouldn’t take more than a week
  • Hands-on experience with GitHub is recommended
  • Use of Version Control is essential in the industry

Utilizing Developer and Software Engineering Focused IDEs πŸ”§

While most beginner data scientists use notebooks as their primary ID, industry uses software developed toolkits like PyCharm and VS Code. Upscaling software engineering basics and principles before landing a first role is highly recommended.

Key Takeaways

  • Implement solutions in developer-focused IDEs
  • Dive into software engineering principles early on
  • Medium articles offer valuable insights

Focus on Specialization in Data Science 🎯

In an attempt to learn everything in the field, it’s easy to lose sight of the importance of specialization. Focusing on one domain or specialty that aligns with your work is how you make true progress and growth.

Key Takeaways

  • You can pivot later in your career to expand your expertise
  • Avoid trying to learn everything at once
  • Focus on continuous study in a specific area
Domain SpecializationBenefits
Focus on ProgressGrowth

Understanding the Role of a Data Scientist 🧠

The role of a data scientist is more than just coding algorithms. It involves a significant amount of time in gathering and analyzing data, emphasizing the importance of data analysis in driving business decisions.

"Deploying algorithms have their time and place and do generate value; however, most of your time is spent simply just looking and analyzing data."

Articulating Findings in a Clear Manner πŸ’¬

Being a data scientist isn’t just about technical skills; it’s also about effectively articulating findings. Clear and articulate communication of your work is crucial in driving influence and progression within a company.

Key Takeaways

  • Articulate findings for better influence
  • Clear communication drives progression
  • Understand the impact of your work on the business

Framing Business Problems in a Data Science Way πŸ“Š

In the real world, you often won’t find neatly formatted CSV files awaiting analysis. The responsibility lies in understanding business requirements, framing them in a data science context, and finding solutions to the problem.

Framing Business ProblemsKeys to Success
Identify SolutionsUnderstand Data

Accuracy and Validation in Delivering Work 🎯

From University to the real job, the speed and accuracy in delivering work have to be adjusted. It’s crucial to take the time to triple-check data and findings, ensuring that errors are avoided and work is delivered to a high standard.


Adapting to your first data science job will always be challenging, but taking the necessary steps can ease the process and lead to a successful career in the field.

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Remember, no one will be annoyed if you deliver your work on time to a high standard. It’s better to take your time and triple-check your work than to risk mistakes.

The guide is a survey of 7 regrets from the first year in Data Science. It offers explicit insights on the challenges and important aspects of the field, presenting key takeaways and actionable strategies for success.

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