What is the difference between Data Scientist and Data Analyst?
Currently, there is confusion about what data scientists and data analysts are supposed to do in daily work and how they differ from each other.
Data scientists are usually given tasks related to departments of the company (e.g., marketing) where they are required to develop methods to maximize the benefits of the company, e.g., by increasing the revenue. These tasks vary from developing algorithms to develop machine/deep models.
There is a common misunderstanding that a data scientist develops only machine/deep learning models. In fact, most of the data scientists time is spent on cleaning and preparing huge amounts of data (usually stored on Big Storage like Hadoop) in order to be used for data science tasks, e.g., training machine learning models.
Data Analysts are usually given tasks related to departments of the company (e.g., marketing) where they are required to analyze, visualize and communicate huge amounts of data in an easy and interpretable manner to the decision-makers. The data analyst role is an important role and without it, decision-makers cannot make easy their decisions.
What are the role requirements for the Data Scientist and Data Analyst?
Data scientists are usually required to have solid knowledge in:
- math background and particularly in probability and discrete and continue math.
- scripting languages such as Python or R
- data analysis tools such as SQL
- machine/deep learning algorithms on theoretical and practical levels
Data Analysts are usually required to have:
- A solid background in statistics
- Solid knowledge in the data analysis tools such as SQL
- Solid knowledge in dealing with big data e.g., Hadoop data by using Hive and Impala
- Knowledgeable in data visualization tools e.g., looker, D3.js to be able to visualize your data and results, e.g., by creating dashboards
- Some knowledge in scripting languages such as Python or R
The roles of data scientists and data analysts have similar requirements. However, since data scientists usually develop a machine learning model, they are required to know the math behind those models which are mostly about probability theory and discrete math. On the other hand, data analysts are required to apply many statistical methods to interpret the data (e.g., obtain P-value for A/B tests).