DATA SCIENTIST OR DATA ENGINEERING: A QUICK GUIDE TO PICK THE BEST CAREER CHOICE

Advancements in data have paved the way for interesting career opportunities in the field. Applying targeted data scientist knowledge to resolving big data problems calls for an in-depth understanding of the industry core. Today, the global data science industry stands at USD 133.70 billion (Mordor Intelligence). There is no denying the fact that the data analytics landscape is expecting significant transformations over the years to follow.

With the diverse data science job roles on offer, this industry is inviting and gaining traction from skilled data aspirants ready to take the plunge. IBM forecasts the demand for data scientists to expand by 28% through 2025. This indicates the astounding opportunities you are set to experience ahead! Let us delve into the riot of diverse career opportunities in data science and starting upfront we have- Data engineering and Data science.

Which is more popular- data scientist or data engineer?

Data scientists are analytical data experts who have the technical skills to solve complex business problems and the curiosity to explore what problems need to be solved. They are experienced in mathematics, and computer science, and are capable of deducing future trends.

Data engineers are equipped to work in a diversified set-up to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their core job involves making data accessible so that businesses can utilize it to evaluate and optimize their performance over time.

However, the demand for both roles is high; with data scientists gaining more popularity. Companies across continents are eager to hire professional data scientists who are capable of collating teams and yielding business results by deploying futuristic data scientist skills.

Are these two same or different job roles?

Where data engineers focus on managing and organizing data, and building, and maintaining databases and data pipelines. Data scientists focus on analyzing and interpreting data to find insights and patterns. Let us delve deeper into the diversities the two career opportunities offer.

DATA SCIENCE

• Cleans and organizes data, performs descriptive statistics and analysis to develop insights, build models, and solve a business requirement.
• Conducts research with large amounts of data from various sources, and helps find unseen patterns to help decision-making.

DATA ENGINEER

• Develops, constructs, tests, and maintains architectures. Such as databases and large-scale processing systems.
• Manage this data by implementing various strategies to increase efficiency, reliability, and data quality

Uses different programming languages-Python and R; with data visualization and manipulation libraries to create decision-making models Uses various tools such as MySQL, Hive, Oracle, Cassandra, Riak, MongoDB, etc. Yet, a data engineer is independent of everyone

Uses a variety of machine learning tools and statistical models to prepare data for the purpose Examine the clean data in more detail; by dealing with data extraction, collection, and integration from multiple sources

Which is more rewarding- data science or data engineering?

Building a data engineering career or designing a career trajectory as a professional data scientist; the choice rests in you. This guide shall bring you clarity on your career choice. Looking at the rewarding career choice; it can be stated that the average salary for a data scientist sits at USD 124,124 annually (Indeed); whereas the same for a data engineer sits at USD 106,921 annually (Glassdoor). However; these figures will vary depending on the educational background, years of expertise, certifications, and skillsets, geographical location, current market demand, and many other parameters. This can be clearly said that becoming a seasoned data scientist with the best data scientist skills on offer; is the career of the future, depending on the stated statistics.

What are the common skills required to become a data scientist and data engineer?

Getting started with any of the career choices requires a great deal of resource experimentation and a strong comprehension of core skills. Starting with a bachelor’s or a master’s degree in STEM or equivalent is all that you need to pivot into strengthening skills. Let us look at the common skills needed for these fields of work.

Popular Data Scientist Skills that work with Data Engineering as well are:

• Machine learning
• Reporting and Data visualization
• Computing
• Big data
• Data warehouse

• SQL/ NoSQL
• Problem-solving
• Database systems
• Data warehousing
• Programming languages

• Interpersonal communication
• Model Deployment
• Statistics
• Data Intuition
• Data Analytics

Looking at these popular skills, it is obvious to comprehend that these data-highlighted roles are heavily dependent on one’s capability to comprehend and deduce big numbers and business data for the greater good.

Job Outlook- Data Scientists vs Data Engineers

With the volume of data exploding over the coming years, it is advised for any data aspirant to think it through while picking the career of their choice. The US Bureau of Labor Statistics reveals that 11.5 million data science jobs will be generated by 2026. Where data engineers can run big jobs quickly by utilizing the computing power of BigQuery, SnowFlake, FireBolt, DataBricks, and other cloud warehousing technologies. Make your way up the ladder with the most nuanced skills on display and a wide spectrum of career achievements ahead.