Why Starting as a Data Analyst Can Benefit Aspiring Data Scientists
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Chapter 1: The Case for Data Analysts
You may be asking why it would be advisable for someone training as a data scientist to first take on the role of a data analyst. Drawing from my own experiences in both fields, I want to outline the advantages of starting as a data analyst, even if your ultimate goal is to become a data scientist.
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Section 1.1: Gain Valuable Experience
A report from Interview Query highlights that “data science interviews plateaued in 2020, with only a 10% increase after previously experiencing an 80% annual growth. Conversely, the second fastest growing position in data science is that of business and data analysts, which saw a 20% rise.”
The growth rate for data analyst roles surpasses that of data scientists, leading to increased competition for the latter positions. If you're lacking practical experience in data science, pursuing a data analyst role might be a more strategic choice, as it tends to be less competitive. The qualifications required for data analysts are generally lower than for data scientists, which means a candidate with data science training and a graduate degree will likely stand out in applications. Starting as a data analyst can help you build essential domain experience, enhancing your profile for future data scientist positions.
Section 1.2: Develop Soft Skills
In my role as a data analyst, I frequently engage with various stakeholders and present findings more regularly, as the turnaround time for requests is significantly shorter compared to the lengthy development cycles of data science projects. This frequent interaction allows me to hone my presentation and storytelling abilities using data. Reflecting on my time as a data scientist, I realize that had I been more adept in my soft skills back then, I might have been able to persuade more stakeholders to implement my models; unfortunately, only a third of my models were ever utilized.
Chapter 2: Expectations vs. Reality
In 2012, the Harvard Business Review dubbed data scientists as the “sexiest job of the 21st century.” However, many data scientist positions can be far from glamorous, often failing to meet expectations due to limited opportunities to make a significant impact on business outcomes.
As a data scientist, I found myself building models that were often quite similar, using the same algorithms repeatedly, which became monotonous. The scope of business problems that could be addressed through machine learning varies significantly depending on the company’s structure and leadership support.
While my experience supporting marketing initiatives as a data scientist felt tedious, my time as a data analyst was refreshing. I collaborated on diverse projects and engaged with various departments, such as product, customer success, and finance. In just one year as a data analyst, I gained a deeper understanding of the company’s overall operations than I did in three years as a data scientist. My responsibilities ranged from A/B testing to marketing attribution, and I even had the chance to develop machine learning models when needed. The role of a data analyst allows for broader project involvement that can still profoundly affect business success.
Understanding the Growth of Data Science
While there’s a growing demand for data analysts fueled by increased data generation and accessibility, companies may not be fully prepared to embrace data science initiatives. According to Harvard Business Review, “77% of executives view the business adoption of Big Data/AI as a significant challenge, an increase from 65% last year.”
My experience as a data scientist revealed that data science is often a nebulous concept; many people are aware of it but lack a clear understanding of its applications. This disconnect can hinder support from leadership for data science projects, as they struggle to see how it can solve specific business issues. As a data scientist, your skills may go underutilized without an analytics leader who can effectively communicate the benefits of data science.
On the other hand, a data analyst who generates insights and actionable recommendations is much easier for leadership to understand. As a data analyst, you can still find opportunities to build models while increasing the likelihood of their adoption by framing them as solutions to identifiable business challenges.
Section 2.1: Progressing Within a Company
In smaller organizations, there may not be sufficient work or the necessary infrastructure to justify hiring a data scientist, making data analyst roles more common. These positions offer excellent opportunities to engage in diverse projects that influence business outcomes while paving the way for future advancement into senior roles as the company expands.
As data scientist roles become increasingly competitive, reassessing your career strategy may be essential for achieving success. Starting as a data analyst can help you build the foundational skills necessary for a data scientist. Once you’ve mastered the basics, transitioning into model development will be a smoother process than attempting to excel in all areas from the outset.
The Future of Your Data Career
Consider the potential for growth and variety in your career path by starting as a data analyst. This approach not only enhances your skill set but also prepares you for a successful transition into data science when the time is right.