The difference between a data analyst and a data scientist
This article was originally posted on .cult by Nate Rosidi. .cult is a Berlin-based community platform for developers. We write about everything career-related, make original documentaries, and share loads of other unreleased developer stories from around the world.
You are a recent graduate and planning to start your career in a data related role, but on the LinkedIn Jobs portal you come across so many different job descriptions for data analyst, data scientist, business analyst, data engineer, engineer in machine learning, the list goes on and on. Are you wondering which of these roles might be the most appropriate for you, or if there is even a significant difference between these different roles?
This article just might be the thing that can help clarify some of the main differences in these roles. We will focus on the differences between a data analyst and a data scientist.
One caveat, however, what is covered in this article may not be fully relevant for every data analyst or data scientist role, nor an exhaustive list of responsibilities you might face.
The truth is, these roles differ across companies and industries, and at the end of the day, the best way to find a good job is to spend time reading the entire job description.
Responsibilities of the data analyst
As a Data Analyst, you will be heavily involved in using data to answer a variety of different business questions posed by various stakeholders in the business. To get these answers, you will often find yourself engaged in several other tasks as part of the process.
For example, many data analysts are involved in acquiring data from primary and secondary sources, as well as in the cleansing of data that results from less structured data sets. In some cases, you will also need to work with stakeholders to identify information needs, which in turn will require you to design and maintain data systems and databases.
A data analyst can also be heavily involved in A / B testing. Sometimes data analysts have to get creative to answer business problems that lack direct forms of data. This may involve going through different sets of data and merging them in such a way as to generate meaningful information about consumers.
From an analytical perspective, the role of a data analyst is much more advisory than that of a data scientist. As a result, data analysts are more directly connected with stakeholders in business units and often serve as a communication bridge for data scientists, given the complexities that can arise in the more technical elements of analysis.
In addition, data analysts are often more connected to the parts of the business in contact with the customer and therefore can sometimes be called upon to assist customers by providing analytical elements or creating dashboards to monitor and improve performance. of the company.
What’s more important for a data analyst is being able to pull actionable insights from datasets that help meet real business challenges. For example, as a data analyst, you may be asked to explain why the number of new users decreased in the past month, or why a particular marketing campaign performed better in certain regions. Most importantly, data analysts must be able to effectively communicate this information to various audiences, which often involves generating reports to communicate this information and trends based on existing data.
The key priority for many data analysts is the ability to translate this statistical information into immediate action for the business. More generally, a unique experience as a data analyst is that you will have a comprehensive understanding of the business as well as the industry at large. This is often necessary for the data analyst to generate meaningful information that is meaningful to different stakeholders.
Coding skills and technical knowledge of data analysts
You can expect many job descriptions for data analysts to include skills like data mining, data warehousing, and database management. Establishing data collection structures is also essential for future analyzes that can be conducted on similar sets of information commonly used to track the performance of business decisions made in the past. SQL skills and database management skills are especially crucial for data analysts as part of the information generation process.
In terms of the skills involved, data analysts can expect to use a lot of SQL, Excel, R, or Python, or SAS and BI software for a variety of purposes including statistical analysis, data modeling and visualization.
However, unlike data scientists, data analysts don’t primarily focus on advanced data modeling techniques. Instead, data analysts will mostly need to become familiar with basic supervised learning models like regression, with a good foundation in math and statistics.
Responsibilities of the data scientist
Much like data analysts, data scientists strive to answer a particular business question that requires data-driven insights. However, data scientists are primarily concerned with estimating unknowns, using algorithms and statistical models to answer these questions. As a result, a key difference is the extent of coding used in data scientist roles.
In this regard, data science roles can be difficult as they require a mix of technical skills and an understanding of business issues in context. A data scientist will often find himself trying out different algorithms to solve a particular problem and may even need to become familiar with pipeline automation.
Data scientists also get their hands dirty with much larger datasets than analysts and therefore must have the skills to explore and model huge amounts of unstructured data, often in parallel using languages. like Scala. Many data scientists are finally realizing that a large part of their job is simply to cleanse and process raw data from a multitude of sources and ensure that this process can be replicated for deployment and maintenance. real prediction.
Overall, while data analysts are more consulting-oriented, data scientists are often more product-oriented, with the goal of building data and modeling pipelines for efficient prediction in real product environments with a high level of precision.
Coding skills and technical knowledge of the data scientist
In addition to proficiency in SQL and Python or R, data scientists should be comfortable working in the cloud environment using software or languages such as Scala, Spark, Hadoop, AWS, Databricks, to name a few. only a few.
To complement these skills, data scientists will also need to be familiar with OOP, machine learning libraries, software development and more generally a thicker technology stack, as they may have to work with legacy scripts and algorithms. which may even need to be updated as you go. data sets change over time.
Since data scientists deal with many more prediction issues, they use more advanced data techniques to make predictions that respond to both structured and unstructured data. Thus, not only a solid foundation in mathematics and statistics is required, but also comprehensive skills in data collection, processing, visualization and, most importantly, familiarity with machine learning algorithms.
According to the company, data scientists may be exposed to a whole suite of algorithms in areas such as natural language processing, computer vision and deep learning. Therefore, data scientists often need to have very good experience with statistics and frameworks such as TensorFlow.