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Navigating Common Pitfalls in Your First Year of Data Science

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Chapter 1: Introduction to Data Science Challenges

Entering the field of data science as a self-taught individual can be an adventure filled with valuable lessons. Before embarking on my journey toward a Master’s in Data Science, I dove into this realm without formal education. While I won't delve into the specifics of my entry into the field—there are articles available for those interested—this piece focuses on the significant mistakes I encountered during my inaugural year. Trust me, there were quite a few.

The challenge of self-education is that your knowledge is often limited to what you’ve independently learned, which can lead to considerable gaps. Whether you are self-taught or have formal education, reviewing these pitfalls can help you avoid similar errors.

Section 1.1: Understanding SQL Views

One concept that initially eluded me was SQL views. Although I had heard of them, none of my projects incorporated them, and my self-directed learning didn’t cover this area. Consequently, I found myself in a meeting, somewhat embarrassed, asking what a view was.

So, what exactly are SQL views? Essentially, they are representations of SQL tables—a sort of mirror image. When the underlying SQL table updates, the view reflects those changes. It's important to note that views are not actual tables; they cannot be modified or have their values changed directly.

Understanding SQL views in data science

Why not just utilize a table? Views are incredibly useful in various scenarios, especially when you want to save or reuse a query. They can be created using a syntax similar to the following:

CREATE VIEW vTest AS (

SELECT *

FROM myTable

);

By saving a query as a view, you can easily access it as if it were any other table, making your workflow much more efficient, particularly when developing reports or front-end applications.

Section 1.2: Best Practices for Creating Views

One crucial lesson I learned is the importance of naming conventions. When you create a view, always prefix its name with "v" or "vm." This practice helps you avoid mistakenly trying to write to a view rather than a table.

Chapter 2: Realities of Data Pulling

The first video titled "7 Regrets From My First Year In Data Science" dives deep into the common missteps many encounter, providing insights to help you navigate your own journey.

As you begin your career, you may receive requests for “quick data pulls.” However, it’s essential to understand that there is no such thing as a simple data request.

  • Avoid Quick Turnarounds: Never agree to unrealistic deadlines. Instead of rushing, focus on delivering quality work. If you’re new to pulling data, expect unforeseen challenges.
  • Check Your Work: Always validate your data before sharing it. Triple-checking ensures accuracy and builds trust in your analysis.
Importance of validating data

Additionally, when sharing findings, always include disclaimers that clarify:

  1. The source of the data
  2. Any logic applied during the data querying process, such as filtering out certain values or dates

This practice protects you in case instructions were misinterpreted or incomplete. Users may forget to communicate critical filters, and having this disclaimer can prevent misunderstandings.

Chapter 3: Continuous Learning and Confidence

The second video, "The 7 Biggest Data Science Beginner Mistakes," highlights the common errors newcomers face, offering strategies to overcome them.

You will inevitably encounter new concepts throughout your career. When faced with these challenges, remain calm and conduct thorough research.

Learning is a universal aspect of the job, affecting both novices and seasoned professionals alike. Embrace your self-taught background with pride and confidence; many people value diverse experiences over formal education.

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