Data Scientist vs Data Analyst: Key Differences And Duties

Data plays a significant role in numerous industries and firms today. With the growing industries, the amount of data is also excessive. It becomes almost impossible to keep a tab on the gigantic data collection if we follow the traditional approach. Over time, numerous industries are switching from traditional to digital approaches. So which one is better? If you compare Data scientist VS Data analyst, you can easily conclude.

Let’s compare the roles of Data scientist VS Data analyst. Data scientists are responsible for examining the nature of the data and regulating the related answers in the specific data category.

In contrast, Data analysts act like a mediator between data scientists and business analysts. Both data scientists and data analysts are in-demand jobs today. We will discuss the job of Data scientist VS Data analyst in detail so that you can compare the two and find the best-suited career path.  

What Does A Data Scientist Do?

A data scientist performs ad-hoc data mining and collects numerous structured and unstructured data from various sources. The role of a data scientist is to use several Statistical methods and data visualization procedures to design and evaluate advanced statistical models from extensive volumes of data. 

Data scientists build AI models with the help of several in-built libraries and algorithms. A data scientist can direct an enormous amount of unstructured data and helps to resolve the core problem, and advise ways to make changes around the business by consulting the manager or the stakeholders.

What Does A Data Analyst Do?

A data analyst collects data from several databases and warehouses, then filters and cleans it. The role of a data analyst includes writing complex SQL queries and scripts to gather, stock, recover and manipulate data from RDBMS such as Oracle DB, MS SQL Server, and MySQL.Data analysts create different reports through graphs and charts with the help of  Excel and BI tools. 

Potential Paths for Emerging Data Scientists

Now, let’s discuss the paths for emerging data scientists, starting with junior to mid-level and then the senior data scientist.

Junior Data Scientists

Let’s begin with the data scientist interns. The path to becoming a data scientist begins with becoming an intern, and at this stage, the data scientists mostly work on expanding their core technical skills, including Python, SQL, and so on. A data science intern will contribute value by building scripts or prototyping projects with data visualizations and models. 

At the beginning of their career, data scientists work with specific goals. The role of a junior data scientist will be writing a query to calculate customer churn rates or creating a dashboard to look at purchases by marketing channel if the entry-level data scientists are given challenging analytics problems like the task of building an entire model and deploying it into production then they will be under the guidance of a senior data scientists.

An entry-level data scientist focuses on brainstorming strategies and architectures and producing work for managers and other stakeholders. The salary of an entry-level data scientist ranges anywhere from NRS 20-30K. However, it also depends on the company. 

Mid-level

So, after working for about a year or two, your career transitions from entry-level to mid-level data scientist. Data scientists are ready to take on a more challenging role at this level. Mid-level data scientists should be prepared to take up larger project scopes and more challenging business problems. 

For instance, if an entry-level data scientist creates the SQL queries for an ETL pipeline, a mid-level data scientist can architect the entire ETL pipeline from scratch and implement it in their machine-learning model.

A mid-level data scientists shouldn’t need as many check-ins and can unblock themselves without asking for help from the other data scientists. In addition, also from a product perspective, a mid-level data scientist has a higher understanding of business problems and implementing data science to resolve these issues.

Improving skills and experience means more autonomy regarding the project’s choice and management. There are always plenty of projects for a data scientist to work on. To level up your career as a data scientist, it is crucial to prioritize projects, so no one has to assign new work to you.

The average salary of a mid-level data scientist ranges from NPR 122,000. However, it depends on the company. 

The Senior Data Scientists’

The final step is upgrading to a senior data scientist. So what sets the senior data scientist apart from the others? Someone with an experience of about 5-7 years will be referred to as a senior data scientist. While experience might still matter, there are several other defining factors regarding the acquired skills. And some of these include; having high data accuracy and quality, Excellent communication regarding technical concepts, High-level code quality, a decent understanding of project scope, and prioritizing applications of data science.

The above-mentioned factors play a key role in determining the role of a data scientist. A senior-level data scientist can take on challenging problems and develop the best solution possible. Their value is determined by the efficiency with which they finish the task.

Now, let’s get into the salary.  The average salary of a data scientist is about NRS 193,000 per month. The pay scale varies from company to company as well.

Masters In Data Science 

Upon graduating with an MSc Data Science and Computational Intelligence, you will be able to:

• Gather, clean, stock, and query data from several public and private data sources.
• Estimate, acknowledge, and respond to decision-making needs and requirements.
• Apply appropriate analytic techniques to provide estimates that support decision-making and action.
• Communicate actionable information and findings in easy-to-understand written, oral, and visual formats.

Masters In Business Data Analytics

A master’s in business analytics degree can help prepare you for new career opportunities working with data-focused businesses. There are many programs to choose from, and to help get you started; we’ve listed several considerations that can help you choose the right program. We view the three below factors as key to evaluating a business analytics program.

• Flexible GRE requirement
• Curriculum that incorporates real-world projects
• Dedicated career support

Data Scientist VS Data Analyst- Differences

The main differences between the role of a Data Scientist Vs Data Analyst are discussed below in detail.

These are the key differences between a Data Scientist Vs Data Analyst

Conclusion

So, we’ve drawn some comparisons between Data Scientist Vs Data Analyst in this article. If you are interested in the master’s in data science, you can refer to this article and connect with Softwarica college MSC data science.