Companies hire for various data science positions, and each of these roles demands a specific set of skills. This article outlines what each data professional does and how their contributions to a company vary depending on their job description.

Types of Data Science Roles

• Data Strategist

In a perfect world, a business would recruit a data strategist before gathering any data. This senior expert would be familiar with the value that data can add to organizations.

Using data science may be done in four primary ways, according to renowned data strategist Bernard Marr, for businesses across a variety of industries:

• They can make decisions based on data.
• Data may be used to make products and services smarter.
• Data can be used by businesses to enhance operational procedures.
• They could use data monetization to develop a new source of income.

Businesses frequently outsource such data jobs. They employ outside consultants to create a strategy that fits the business's goals. It's time to guarantee data availability once a company has a data strategy. When this happens, a data architect is needed.

• Data Architect

A high-level database structure is planned out by a data architect (or data modeler). Planning, organizing, and managing information within a company to ensure its accuracy and accessibility are all involved. Additionally, they need to evaluate the requirements of business stakeholders and optimize schemas to meet their demands.

These data jobs are pretty important. Due to the lack of coherence across several database tables without an appropriate data architecture, it may be impossible to answer critical business questions.

• Data Engineer

Particularly in smaller firms, the roles of data engineers and data architects sometimes overlap. But there are significant variations.

To fit the use cases specified by the architect, data engineers construct the infrastructure, arrange tables, and set up the data. The so-called ETL process, which stands for Extract, Transform, and Load, is also handled by them. In order to do this, data must be retrieved, processed into a usable format, and then sent to a repository (the company's database). 

Simply put, they correctly stream data into tables.

Over the course of their work, they frequently receive ad hoc ETL-related assignments, but they hardly ever directly communicate with business stakeholders. With good cause, this position is among the highest-paid ones for data scientists. The ability to use software engineering is just one of several abilities required for this job.

Well, let's review.

As you can see, although each role in data science has slightly distinct qualifications, they are all related to and complement one another. Strategists for data use come first since they outline how data might advance corporate objectives. The architect then designs the database schemas required to accomplish the goals. The engineers create the infrastructure, piping the data into tables.

We may distinguish between three data roles—data analyst, BI analyst, and data scientist—when it comes to gaining insights. These are distinct locations, even if there is considerable overlap.

The Learnbay data science certification course in Dubai offers a thorough yet straightforward explanation of these roles and you can learn the in-demand skills with this course.  Here is a quick summary of the key distinctions between the job titles of data science, business intelligence, and analytics.

• Data Analyst

Data analysts investigate, purify, examine, visualize, and present data, offering insightful information to the organization. In order to access the database, they commonly use SQL.

In order to clean and analyze the data, they then use an object-oriented programming language, such as Python or R, and depend on visualization software, such as Power BI or Tableau, to present the results.

• Business Intelligence (BI) Analyst

The duties of a data analyst and a BI analyst are somewhat overlapping. However, the latter's function is primarily focused on reporting. Their primary goal is to create useful reports and dashboards and to update them regularly. More crucially, they need to meet the informational requirements of various organizational levels of stakeholders.

• Data Scientist

A data scientist possesses the same abilities as a data analyst but has access to the machine and deep learning to build models and forecast the future using historical data.

We can categorize data scientists into three groups:

• Traditional data scientists

A traditional data scientist performs various duties, such as data exploration, advanced statistical modeling, A/B testing experimentation, and developing and optimizing machine learning models.

• Research scientists:

For big businesses, the focus is on creating new machine-learning models.

• Applied scientists

Boast is one of the highest-paying professions in data science and is usually hired in large corporations and big tech. To productionize models, these experts combine their data science and software engineering expertise.

Because it enables one person to control the whole ML deployment process—from model construction to productionization—and results in faster outcomes, more prestigious firms want this combined skill set. An applied scientist is able to manipulate data, model it for machine learning, pick the best method, train the model, adjust the hyperparameters, and then deploy the model.

As you can see, there is a lot of common ground between data scientists, data analysts, and business intelligence analysts. If you are getting started in the field, you can either apply for data scientist, data analyst or BI analyst positions. However, a data analyst is the best choice for beginners. Also, you can sign up for an IBM-accredited data science course in Dubai, to master the essential skills needed to succeed as a data science professional in top MNC.