Decoding the Confusion: Data Scientist vs. Data Engineer
Just imagine you are at the helm of your career and navigating through the data-driven landscapes of unprecedented opportunities. You are in such an era where businesses consider the data as the north star of success. Here, choosing the right path through data analytics is not only a success but also a future-proof transition. But data science vs data engineering- which one is the more significant!!
Businesses adopt a data-driven culture to cope with changing market landscapes and improve their yields. In the midst of this ever-evolving technological landscape, where the very pulse of innovation quickens, market reports resoundingly proclaim the potential for data-driven strategies to revolutionize businesses and skyrocket their yields. Companies have already started experiencing at least of a 20% surge in EBIT because of AI contribution. Work optimization (a process of improving work efficiency and yield in a cost-effective form) is all set to enter the corporate world lifting it’s growth by 2025. No doubt, The journey of learning data management opens up limitless possibilities, but it still presents an individualistic question- Which path should you tread?
This blog is going to guide you in deciphering the enigma of data science vs Data engineering.
Welcome to the crossroads of Data Science and Data Engineering—an argument where data shapes destinies and decisions create futures.
Data Science & Data Engineering in Brief!!
Data Science is a holistic field that consists of everything – from sourcing data to making informed decisions. Firms find this field helpful for tackling issues with facts in hand and making insightful moves. Data scientist's jobs expect to grow by 35% by 2032. Thus, the best degree for data science concentrates more on skills upgrades to meet the increasing demands.
Data Engineering deals with cleaning and arranging the raw data sets to help seniors make the right move. Data engineers help other data experts find insights, make decisions, and plan business growth. The data engineering market expects a rise of 36.7% by 2027, welcoming data experts in diverse fields. Thus, experts eager to start their careers as data engineers must opt for an industry-paced course.
Uncovering Data Science vs. Data Engineering
Data scientists and data engineers are essential for business success, yet inherent distinctions bring diverse results for experts. So, let us review the major differences between these two roles that will help you make the right career decision.
1. Based on Educational Requirements
Learners eager to pursue data science or data engineering must hold a bachelor’s degree in any discipline with good academic records. A basic knowledge of mathematics or statistics makes them good at making analytic decisions. A knowledge of programming languages is an extra gain but not mandatory. In short, both fields welcome learners from diverse domains.
2. Based on Roles and Responsibilities
Learners opting for the data science field aim to drive their careers as data scientists. Their role is to extract insights from raw data sets and help decision-makers make the right move. Their knowledge of statistics helps in experiments, visualize, identify trends, analyze patterns, and make predictions.
Data engineering crafts a successful career for data engineers with the primary role of designing data frameworks for analysis. Pursuing a proven course program helps learners deal with raw data sets, data storage, and security. They are helping hand to data scientists. They provide structured data sets to data scientists for further analysis and decision-making. Their skill in designing seamless data pipelines leads to quality data analysis.
3. Based on Skill Sets
Upskilling from the best online data science masters program equips a learner with basic knowledge of programming languages (Python, SQL). Plus, it enriches their careers with skill sets in – Statistical models, AI & ML algorithms, data visualization, big data tools (Spark, Hadoop), and forecasting models (Time-series).
A learner eager to become a skilled data engineer can learn basic programming (SQL), database management, data warehouse, and big data (Spark, Hadoop) skills via upskilling.
4. Based on Earning Potential & Job Opportunities
A review of a data scientist vs. data engineer salary in India interprets the earning potential and job offers available for these roles.
Pursuing the best degree for data science unlocks job offers in India and overseas with exponential growth. The average salary for a data scientist is ₹13,50,000 pa. Data scientists can rebuild global careers with quality learning and hands-on practice with trending tools. A notable degree enriches their careers with job offers from overseas (US, Canada, Europe, and 60+ Western countries).
Data engineers earn ₹10,50,000 a year in India. Yet, their roles are limited, and they do not see much growth. Some firms hire data scientists, which includes the duties of data engineers. The increasing demand for decision-making skills of data experts may slow the pace of the growth of data engineers in the next years.
5. Based on Career Path
Data scientist's career advancement ranges from entry-level to senior managing positions. It's an open platform that helps in the skills upgrade of experts for better growth & success.
Data engineering crafts a career path that has become limited and does not have much scope at entry levels. Also, many MNCs see the data engineer’s role as a data architect, developer, or any other software engineer. In short, more privilege for tech domain experts lessens the growth scope for non-tech pros.
I am summing up the comparison in a table for ease of understanding.
|- Bachelor’s degree
- Basic knowledge of mathematics/statistics
|- Bachelor’s degree
- Basic knowledge of mathematics/statistics/computer science (preferable)
|Roles & Responsibilities
|- Data handling
- Extract insights
- Track and visualize trends
- Assist decision-makers in business decisions
|- Simplify complex data sets
- Design data framework
- Data storage
- Data security
|Programming, statistics, data visualization, big data, AI & ML, forecasting tools
|Database management, data warehouse, basic programming, & big data
|Average salary – ₹13,50,000 pa
|Average salary – ₹10,50,000 pa
|Entry-level – Senior Management
|Limited scope at entry levels. Mostly placed in tech domains (data architect, developer, & software engineer)
Concluding, “Which career is best for you, data scientist or data engineer?”
A query of Data Scientist vs Data Engineer – which is better?, stops here.
The discussion over Data Scientist vs. Data Engineer reflects better career growth chances that data science offers to work experts. Yet, both fields deal with data handling. Their future growth depends on an expert’s choice of roles, interests, and skill sets. Data scientists carry more skills and proven expertise in trending tools that bring lucrative offers. Working experts eager to redefine their careers in the data field must consider these features before making the final move.
Enrolling in a Master's in CS: Data Science and AI program opens the door toward alluring pay scales and global job offers. A degree from Woolf University welcomes job offers from the US, Canada, Europe, and 60+ Western countries with effortless visa facilities. A proven mentorship eases practical learning of Generative AI trends followed by live projects. Plus, dual certification from IBM & Microsoft lifts the career success options.
1. Is a data engineer better than a data scientist?
A data engineer and a data scientist have different roles and duties to perform. Concerning duties, data scientists help decision-makers and management teams to make a business move, unlike data engineers who assist data scientists. Following the salary range, data scientists earn more than data engineers. Data scientists may seem better than data engineers, but at present, data engineers are more in demand.
2. Can I switch from data scientist to data engineer?
Yes, you can switch a data scientist’s position with a data engineer and vice-versa. Yet, it needs to learn extra skills and tools to drive better success. A proper analysis of existing skill sets will help find the lagging ones and upskilling.
3. Is data science harder than engineering?
Data science requires more skills as it helps managers and leaders in decision-making. Data science is not harder than engineering, yet it needs quality learning of extra skills (technical & soft) to lift career growth options. Thus, upskilling with the best online data science masters program will drive success.
4. Is data engineering a future-proof career?
Data engineering is a future-proof career as the demand for quality data analysis and insightful decision-making keeps on increasing. A data engineer has all the promising skills that dive deeper into a data-driven culture of business decisions. Many MNCs are opening data engineer positions to support data scientists and other experts. Hence, building a data engineering career will be useful in the upcoming years.
5. What are Data Engineer skills?
Data engineers deal with designing data architectures and data warehousing. Data engineers help data scientists read data sets, analyze them, and make good decisions. Data engineers must have skills like –
- Programming skills
- AI & ML algorithms
- Data Analysis
- Data Modeling
- Database Management
- Data Security
- Data Mining