What is a Data Analyst and What Is a Data Scientist?

You’re a freshly minted MBA graduate with aspirations of working in your field as a data professional, but the LinkedIn Jobs portal offers so many distinct job openings for data analysts, data scientists, business analysts, and data engineer that you’re unsure which one to choose.

You wonder if any distinctions exist between these vocations or whether they all belong to the same category.

This article may just be what you need to understand the key distinctions between these jobs. The differences between a data analyst and a data scientist will be our focus.

A word of caution, however: this material does not address all duties that come with each title or describe every difference between them.

The fact is that these responsibilities will vary from company to business and industry, so the greatest method to find a good fit is to read through the full employment description.

The duties of a data analyst

The data analysis method is made up of numerous activities, many of which are utilized to exploit data for a variety of business purposes requested by a number of stakeholders.

As part of the procedure, you will frequently be called on to do other tasks. After it has been less structured, many data analysts participate in sourcing and cleaning raw data from primary and secondary sources.

In some cases, you’ll be expected to collaborate with stakeholders to identify informational demands, which will necessitate the design and upkeep of data systems and databases.

A data analyst may also be required to be creative in solving business issues that don’t have a clear equivalent of data.

This might include going through several sets of data and combining them in a way that provides useful consumer insights.

On the data analysis side, the data analyst job is more consulting-focused than that of a data scientist. As a result, because the intricacies of analysis may become more technical, data analysts are frequently in contact with business unit stakeholders and act as a go-between for data scientists.

Data analysts are frequently involved with the client-facing aspects of the organization, so they may be expected to assist with client pitches by providing analytical components, or in developing dashboards to track and improve company performance.

It’s also critical for data analysts to be able to produce actionable insights from data that may assist solve genuine business problems.

For instance, as a data analyst, you might be asked to explain why the number of new users fell last month, or why a certain marketing campaign performed better in specific areas.

More significantly, data analysts must be able to express these findings effectively in various situations, which frequently entails generating reports so that audiences can understand them and changes based on existing information.

Many data analysts spend much time and energy trying to figure out how to make these statistical findings useful in terms of business action.

More generally, one distinctive feature of being a data analyst is that you will come to have an expert understanding of both the company and the broader industry.

This is frequently required for a data analyst to produce meaningful insights that are relevant to various stakeholders.

Technical skill and knowledge of data analysis

Many data analyst job descriptions will include skills such as data mining, data warehousing, and database management.

For future analysis that may be accomplished on similar information sets, setting up data collection methods is critical.

SQL abilities and database management expertise are especially important for data analysts because they contribute to the insight generation process.

Data analysts, on the other hand, will be required to use a variety of skills including SQL, Excel, R, or Python for a wide range of purposes including statistical analysis, data modeling, and data visualization.

Data analysts, unlike data scientists, are not primarily concerned with sophisticated data modeling methods.

Rather than focusing on complicated supervised learning algorithms such as regression, data analysts will mostly need to be knowledgeable in basic supervised learning models like regression.

What are the duties of a data scientist?

Data scientists, much like data analysts, are individuals who use data to solve a certain business problem that requires data-driven insight.

Data scientists, on the other hand, are mostly concerned with estimating variables and employing algorithmic and statistical modeling to answer these issues.

As a result of this distinction, one can observe that the amount of coding done in data scientist jobs is significantly higher.

In this regard, data scientist positions may be tough because they need a combination of technical skills and an awareness of business issues in context.

A data scientist will frequently have to experiment with different algorithms to solve a particular issue, and he or she may even have to be familiar with pipeline automation.

Data scientists, on the other hand, must have abilities to investigate and model much larger sets of unstructured data than analysts do, which necessitates them to be fluent in parallel processing languages like Scala.

Many data scientists eventually discover that a large portion of their job is simply cleaning and processing raw data from a variety of sources before ensuring that the process may be repeated for real-world deployment and prediction.

While data analysts are more consultant-oriented, data scientists are more product-oriented, with the goal of developing efficient prediction models in actual product environments with high levels of accuracy.

The ability to code and understand data science is important.

To be a data scientist, you must know how to use cloud-based technologies such as Scala, Spark, Hadoop, AWS, and Databricks.

To name a few skills required by data scientists, they will also need to be experienced with object-oriented programming, machine learning libraries, software development techniques, and so on because they may have to deal with legacy scripts and algorithms that might require updating as data sets evolve over time.

Data Scientists, on the other hand, work more with prediction issues and need more sophisticated data methods to generate predictions that accommodate both structured and unstructured data.

As a result, not only is a solid understanding of mathematics and statistics required, but also comprehensive technical skills in data gathering, processing, visualization, and most importantly, familiarity with Machine Learning algorithms.

Data scientists may have the opportunity to learn a wide range of algorithms in domains such as Natural Language Processing, Computer Vision, and Deep Learning at different organizations. As a result, data scientists are frequently expected to have strong statistical and framework knowledge such as TensorFlow.