The majority of IAA students go on to work in data analyst or data scientist-type roles. Although we receive training to be qualified for both, the actual job responsibilities and experiences have notable differences. Knowing how these roles are different can help students figure out what appeals to them more.
Having worked as a data analyst for the past three years, I combine insights from my own experience with research from industry articles and job descriptions to provide an overview of each role and how they differ. I also want to provide a disclaimer that data analyst and data scientist are broad terms that can be defined differently by different employers. There are cases where what I will define as a data analyst’s responsibilities will fall under a data scientist role, and vice versa. The goal of this blog post is just to provide a general picture of typical differences, and it will not apply to each specific case.
First, we’ll start with similarities:
- Cleaning data
- Identifying patterns and trends in data
- Using statistical methods
- Creating reports and visualizations
- Communicating findings to stakeholders
Now let’s dive deeper into each role.
Data Analyst
This role may appeal to you if you:
- Like to be closer to the business
- Enjoy more structured deliverables
- Want to solve a variety of different problems
Focus: understanding the past
Typical job responsibilities:
- Find patterns in data and translate them to actionable insights
- Work with business stakeholders to define metrics
- Create and maintain dashboards for tracking business performance
- Handle ad-hoc requests to pull numbers or create a quick deliverable
Skills/Knowledge:
- Basic statistics
- Foundational math and logic
- Business concepts
Work with:
- Structured data, typically in databases
Typical tools/languages used:
- Programming languages
- SQL
- R
- Python
- SAS
- Data visualization / dashboarding software
- Microsoft Excel
- Tableau
- Microsoft PowerBI
Similar role names:
- Business analyst
- Business intelligence analyst
- Marketing analyst
- Product analyst
- Quantitative analyst
Data Scientist
This role may appeal to you if you:
- Enjoy working on complex problems
- Have an experimental mindset
Focus: predicting the future
Typical job responsibilities:
- Create statistical models and use machine learning algorithms to predict the future
- Work with big data to derive insights
- Run A/B test experiments
- Develop artificial intelligence
- Create algorithms to automate data processes
Skills/Knowledge:
- Advanced statistics
- Machine learning
- Predictive analytics
- Model building
Work with:
- Structured and unstructured data
- Big data
Typical tools/languages used:
All of what was listed for data analyst, plus:
- Python packages such as numpy, scikit-learn
- AWS (for deployment)
- Hadoop
- TensorFlow
- Spark
Similar role names:
- Machine learning engineer
- Machine learning specialist
Although similar in several ways, data analysts and data scientists perform different functions in an organization. Data analysts help inform decisions by studying the past, while data scientists focus on predicting the future. These functions inform the types of methods and tools used in these roles. By identifying which appeals to you, you can better tailor your job search and prepare for the next step in your career.
Sources:
- https://www.coursera.org/articles/data-analyst-vs-data-scientist-whats-the-difference
- https://www.linkedin.com/pulse/data-scientist-vs-analyst-engineer-whats-difference-minhazul-abedin/
- https://www.codecademy.com/resources/blog/data-analyst-vs-data-scientist
Images source: LinkedIn
Columnist: Sasha Volodin