We often hear people tossing around terms like Data Analysis, Data Mining, and Big Data without really understanding what they mean. Data Analyst, Data Engineer, and Data scientists are some of the job roles often mentioned together. People often get confused about these job roles and feel they are the same.
There are several such job titles in the data science field, but here we will talk about two primary roles: data analyst and data scientist. To make things easy for you, we have discussed the difference between data analysts and data scientists in detail.
Who is a Data Analyst?
A data analyst job title is similar to a business intelligence analyst or business analyst. The focus of data analytics is on describing and visualizing the current landscape of data. The data analyst creates reports, so it is easy for non-technical users to understand.
The data analyst also plays a crucial role in data science. A data analyst who does prognostic analytics is more like a data scientist. The basic difference between a data analyst and data scientist is that the former does the analytics without using the algorithms to get the output.
The Data Analyst needs to have skills like SQL, Excel, Tableau, and other visualization tools. Unlike a data scientist who also needs SQL skills, the data analyst role requires a strong focus on the SQL field. Their work involves performing queries like window functions, pivot tables, common table expressions, and subqueries.
A data analyst needs to maintain databases and data systems using interpreted data sets and statistical tools. They have to prepare reports that effectively communicate patterns, predictions, and trends based on their relevant findings.
Who is a Data Scientist?
A data scientist is nothing but a professional responsible for understanding the data from a business perspective. He also shoulders the responsibility of making predictions from the data to make accurate business decisions. The main functions of data scientists include
- Meet stakeholders to discuss the business problem
- Pull up relevant data for analysis.
- Perform exploratory data analysis, which involves prediction and model building and engineering
- Depending on the workplace- create a pickled model for production and/or compile code to.py format.
The Data Scientist needs to have the following skills – SQL, Python, Jupyter Notebook, and Modeling/Algorithms.
Many people think only data analysts require SQL skills, which is wrong. In business settings, you don’t get most data sets; the data scientist has to create them using SQL. Python is the primary programming language in data science. For the role of data scientist, you need to have good knowledge of Python. A data scientist has to build models and perform the coding. The Jupyter Notebook is the data scientist’s playground allowing them to develop and test models using libraries like NumPy, pandas, and sklearn.
Data Scientist has the responsibility to design data modeling processes. They have to create algorithms and predictive models to extract data needed by companies to solve complex issues.
Data Scientist Versus Data Analyst – Differences
- A data scientist needs to have strong business insightfulness and suitable data visualization skills to convert data insights into a business story. A data analyst does not require any of these.
- A data scientist is required to examine and explore data from various disconnected sources. On the other hand, the data analyst only needs to look at data from one source that is the CRM system.
- Data analyst solves questions given by the business, whereas the data scientist has to make questions from the data. The solutions are intended to benefit the company.
- Data scientists need to be well versed with machine learning and need to build statistical models. It is ok for a data analyst not to have a hands-on machine learning experience.
To sum up, though there is some overlap in data scientists and data analysts’ skills, there is a considerable difference between the two.