In today’s world where data rules, the need for experts who can use its power has grown fast. Two jobs have become key players in this data shift: Data Scientists and Data Engineers. As data shapes more of our world, knowing the differences between these two fields is key for people who want to start a good career. 

In this article, we will discuss whether you should become a data scientist or data engineer and learn all the important aspects of both of these interesting fields and hopefully, you’ll find the right career path for you!

Explaining Data Science

Data Science involves getting useful insights from huge amounts of data. It uses a mix of computer science, math, machine learning, and data management methods to find hidden patterns, trends, and links. These findings can help make vital choices, boost research and new ideas, and even lead to new products and services.

Data Science Life Cycle

The data science life cycle is a systematic process used to extract knowledge and insights from data. Here are the key steps involved:

1. Problem Definition: Identify and understand the problem or question to be addressed.
2. Data Collection: Gather relevant data from various sources.
3. Data Cleaning: Preprocess and clean the data to ensure accuracy and completeness.
4. Exploratory Data Analysis (EDA): Analyze the data to discover patterns, trends, and relationships.
5. Feature Engineering: Create and select relevant features that will be used in modeling.
6. Model Building: Develop machine learning models to make predictions or classify data.
7. Model Evaluation: Assess the performance of the models using appropriate metrics.
8. Model Deployment: Implement the model in a real-world environment.
9. Monitoring and Maintenance: Continuously monitor the model’s performance and make necessary updates.

The Key Skills a Data Scientist Needs

To succeed as a Data Scientist, you need a wide range of abilities:

• Computer Science: To excel in data science, you must know how to code in SQL, Python, and R. You should also understand software design and engineering principles.
• Mathematics: Data analysis relies on a strong foundation in applied math. This includes knowledge of stats, probability, and linear algebra.
• Machine Learning: You must be familiar with different machine learning methods. These include unsupervised learning, supervised learning, reinforcement learning, and deep learning with neural networks. It’s also crucial to know how to use machine learning libraries like Pandas.
• Managing Data: A Data Scientist’s toolkit must include abilities to wrangle and mine data, keep databases in good shape, query and manipulate data, clean it up, and handle big data.
• Data Visualization: A key skill is a knack for turning complex data insights into eye-catching visuals using tools like Matplotlib and Tableau.

A Day in the Life of a Data Scientist

Being a Data Scientist means having a wide range of daily tasks just like the key skills you need. These pros develop and push forward analytics projects, write code, train machine learning models, make sense of analysis results, and share insights with key people. It’s a job that mixes tech enthusiasts with good communication.

Take, for example, a Data Scientist working on sales analytics for Reality Labs at a company like Meta (Facebook). Their job would include:

• Partnering with Product, Engineering, and other teams to lead analytics projects from start to finish. These projects have an influence on product strategy and investment choices.
• Shaping product direction through clear persuasive talks to leadership.
• Tackling a wide range of tough problems using various analytical and statistical methods, while handling big complex datasets.
• Using technical know-how in number crunching, testing, data mining, and data showing to create strategies that help billions of people and millions of businesses.
• Setting goals, making forecasts, and keeping an eye on key product numbers to spot trends. This helps to measure how well product efforts are doing.
• Spotting and testing ways to make the product better, and guiding roadmaps with insights and advice.

The Field of Data Engineering

Data Engineering, in contrast, aims to put into action, assess, and keep up data structures, like data pipelines, databases, and other systems to process data. It serves as the foundation to ensure easy access to data for various uses such as machine learning projects and automated factory production methods.

The Key Skills a Data Engineer Needs

While some skills overlap with those needed for Data Science, Data Engineers rely more on know-how in computer science and how to manage data:

• Computer Science: Deep knowledge of SQL, Python, R, C, C++, and Java programming languages, along with a thorough grasp of operating systems (Linux, Unix, Windows macOS), and software development principles.
• Data Management: Skill in tools to manage databases, transform data, mine data, create data pipelines, and use cloud computing is key.
• Analytical Acumen: A basic grasp of data analytics, including statistical analysis and the basics of artificial intelligence and machine learning, is needed.
• Visualization: Knowledge of tools like Matplotlib and Tableau is crucial to visualize data well.

A Look into the Life of a Data Engineer

A Data Engineer spends most of their day writing code. They create data pipelines, run tests, and fix problems in systems that are already in use. But being good at talking to others matters just as much. Data Engineers need to work with the people who use their pipelines to make sure what they build does the job right.

At a company like Meta, a Data Engineer in the analytics program would have to:

• Design and oversee the data architecture for several big projects while weighing design and operational trade-offs in systems.
• Build and add to frameworks that boost the effectiveness of data logging, teaming up with data infrastructure to sort out and fix problems.
• Team up with engineers, product managers, and Data Scientists to get what data they need and show key data insights in a clear way.
• Set up and handle service-level agreements (SLAs) for all datasets in assigned areas of responsibility.
• Choose and put in place security models based on privacy needs, make sure safeguards are followed, tackle data quality issues, and improve governance processes in assigned areas of responsibility.
• Design, build, and launch advanced data models and visualizations that work for many uses across different products or fields.
• Tackle tricky data integration challenges using the best ETL patterns, frameworks, and query methods, and getting data from structured and unstructured sources.
• Help manage existing processes running in production, and make complex code better through cutting-edge algorithmic ideas.
• Improve pipelines, dashboards, frameworks, and systems to make it easier to create data artifacts.
• Have an impact on product and cross-functional teams to spot data changes that make a difference.
• Guide team members by giving and getting useful feedback.

Data Science vs. Data Engineering: What’s the Difference?

Data Science and Data Engineering have some things in common, but they have different main goals and jobs. Data Science tries to get insights from data and create models that predict things. Data Engineering makes sure a company’s data systems work well, including getting data ready for Data Scientists to use.

Things to Consider When Choosing Your Career

Choosing between Data Science and Data Engineering is up to you. You should think about what you like, what you’re good at, and what you want from your career. Here are some important things to consider:

1. What You Love and Find Interesting

• If you love to analyze data, create predictive models, and find useful insights, Data Science might be perfect for you.
• But if you enjoy designing efficient data systems, making pipelines better, and keeping data reliable, Data Engineering could be your thing.

2. Salary Comparison:

Here’s a salary comparison table between data engineers and data scientists in India:

Note: Salaries can vary significantly based on factors such as company size, industry, location, and individual qualifications.

3. Skill Alignment

• Look at your current skills and see where you excel. If you’re great with computer science, coding, and managing data, Data Engineering might suit you well.
• On the flip side, if you excel in applied math, stats, and machine learning, Data Science could be right up your alley.
• Go through the above sections where we discuss both data science and data engineering skill sets at length.

4. Career Goals and Opportunities

1. Data Science

Opportunities:

1. Data Analyst: Entry-level role responsible for collecting, analyzing, and interpreting data to provide actionable insights. Data Analysts work across various industries including finance, marketing, and healthcare.
2. Data Scientist: Engages in advanced data analysis, statistical modeling, and the development of machine learning algorithms. This role is prevalent in technology companies, research institutions, and innovative startups.
3. Machine Learning Engineer: Specializes in creating and deploying machine learning models. High demand exists in sectors such as autonomous systems, healthcare analytics, and financial technology.
4. Business Intelligence (BI) Analyst: Focuses on leveraging data to support business decisions and strategy development. BI Analysts are commonly employed in corporate environments and consulting firms.
5. AI Research Scientist: Works on developing cutting-edge artificial intelligence techniques and applications. Opportunities are primarily available in academic research, specialized research labs, and advanced technology firms.

Growth Path:

1. Junior Data Scientist: Start by working on data cleaning, exploratory data analysis, and basic model development.
2. Data Scientist: Progress to handling more complex data problems, leading projects, and refining predictive models.
3. Senior Data Scientist: Take on strategic responsibilities, mentor junior data scientists, and lead data-driven business initiatives.
4. Lead Data Scientist/Principal Data Scientist: Oversee large-scale projects, shape data strategy, and influence business decisions at a high level.
5. Chief Data Scientist: Define the data vision for the organization, guide data science teams, and drive innovation across the company.

B) Data Engineering

Opportunities:

1. Data Engineer: Focuses on designing, building, and maintaining the data infrastructure and pipelines required for data processing and analysis. Opportunities are found in tech companies, financial institutions, and large enterprises.
2. Big Data Engineer: Specializes in handling large-scale data processing frameworks and tools like Hadoop and Spark. Common in industries dealing with vast amounts of data, such as telecommunications and e-commerce.
3. Data Architect: Designs and manages data frameworks and systems that support data storage, processing, and retrieval. Data Architects are often employed in large organizations and IT consulting firms.
4. ETL Developer: Works on Extract, Transform, and Load (ETL) processes to integrate data from various sources. Opportunities exist in companies focusing on data warehousing and integration.
5. Database Administrator (DBA): Manages and optimizes databases to ensure data availability, performance, and security. DBAs are critical in sectors like finance, healthcare, and retail.

Growth Path:

1. Junior Data Engineer: Begin by learning data integration techniques, building data pipelines, and understanding database management.
2. Data Engineer: Advance in designing scalable data solutions, optimizing data pipelines, and working with big data technologies.
3. Senior Data Engineer: Lead complex data projects, mentor junior engineers, and ensure the efficiency and scalability of data systems.
4. Lead Data Engineer/Architect: Oversee data architecture design, manage data engineering teams, and drive improvements in data infrastructure.
5. Chief Data Officer (CDO): Shape the organization’s overall data strategy, manage data governance, and lead data-centric initiatives across the enterprise.

Both data science and data engineering offer rewarding career paths with significant growth potential.

5. Learning Preferences

• Start your learning journey by enrolling in online certification programs that don’t just help you upskill but also add major value to your resume such as the GUVI Data Science Career Program.
• Data Science training focuses on stats, machine learning coding in Python or R, and showing data.
• Data Engineering training, on the flip side, covers databases (SQL NoSQL) big data tech coding languages (Python, C++, Scala), and cloud computing. For example, GUVI’s Data Engineering Program provides hands-on portfolio building through multiple case studies and projects.
• Think about how you want to learn, the traditional way by enrolling in a CS degree or upskilling with updated syllabi through online certifications and begin your journey, refer to our Blog Space for more questions you may have.

Steps to Reach Your Goal – Data Scientist or Data Engineer

No matter which path you pick – Data Science or Data Engineering – you can follow these steps to choose one career that aligns with your skill sets and interests.

• Bachelor’s Degree: High school students or those with time and money can get a four-year Bachelor’s degree to build a strong base for a data career by studying computer science or applied math.
• Bootcamps: People looking to start an entry-level job or switch careers can join data science bootcamps. These programs offer thorough training and help you find your ideal job through their career services.
• Projects: Engaging in hands-on projects is a vital step in both data science and data engineering. Here are examples tailored to each field:

• Data Science:
  • Predictive Modeling: Build a model to predict housing prices based on historical data. Use machine learning techniques to analyze factors like location, size, and amenities.
  • Customer Segmentation: Analyze customer data from an e-commerce platform to segment users into distinct groups based on purchasing behavior. This project can help businesses tailor marketing strategies.
• Data Engineering:
  • Data Pipeline Construction: Create a data pipeline to collect, clean, and store data from multiple sources. For example, build a pipeline that integrates data from APIs and databases, processes it using Apache Spark, and stores it in a data warehouse.
  • ETL Process Development: Design an ETL (Extract, Transform, Load) process to consolidate data from various business systems into a centralized data repository. Implement transformation rules and data validation checks.

• Master’s Degree: If you already have a bachelor’s degree and are looking to advance or transition your career, a master’s program in data analytics or data science can provide a significant boost in compensation and open doors to senior-level positions.
• Online Programs: For working professionals seeking flexibility, online master’s programs and data science certificate courses offer the opportunity to study at your own pace while continuing to work.

Exploring Alternative Paths: Data Architecture

Before you make your final choice, think about a related area: Data Architecture. Data Architects design and oversee the implementation of data architectures. They connect Data Engineers with Data Scientists.

What Data Architects Do

Data Architects need skills like those of Data Engineers. They must excel in computer science and data management. But they also need to think like designers. This means they spot problems, come up with solutions, test the best ideas, and keep improving their designs over time.

Could Data Architecture Be Your Path?

If you like to oversee engineering projects and apply design thinking, Data Architecture could be a good career choice for you. But it’s not easy to jump straight into Data Architecture without first working as a Data Engineer or Data Scientist. This is because Data Architects often come up with solutions that Data Engineers put into action.

Kickstart your Data Science journey by enrolling in GUVI’s Data Science Career Program where you will master technologies like MongoDB, Tableau, PowerBI, Pandas, etc., and build interesting real-life projects.

Alternatively, if you want to explore Python through a self-paced course, try GUVI’s Python Certification course.

Takeaways…

As the world becomes increasingly data-driven, the demand for skilled professionals in Data Science and Data Engineering will continue to soar. By carefully evaluating your interests, strengths, and career aspirations, you can make an informed decision that sets you on a rewarding path in this exciting and rapidly evolving field.

Remember, the journey to becoming a Data Scientist or Data Engineer is not a linear one. With the right education and experience, you can always pivot and explore alternative paths within the data ecosystem, such as Data Architecture, or even combine multiple disciplines.

The future belongs to those who can harness the power of data, and by choosing the path that resonates with you, you’ll be well-equipped to make a lasting impact in this data revolution.

FAQ’s

Is it better to be a data engineer or a data scientist?

It depends on your interests. Data engineers focus on building and maintaining data infrastructure, while data scientists analyze and interpret complex data to provide insights. Choose based on your skills and career goals.

Do data scientists earn more than data engineers?

Typically, data engineers(₹3.5–20.9 lakhs) earn slightly more than data scientists, (₹ 4.0 Lakhs to ₹ 23.0 Lakhs)though this can vary based on location, experience, and industry.

Is it easy to switch from data engineer to data scientist?

Switching from data engineer to data scientist is possible but requires gaining expertise in statistical analysis, machine learning, and domain-specific knowledge.

Can a data engineer become a data scientist?

Yes, a data engineer can become a data scientist. Transitioning involves acquiring skills in statistical analysis, machine learning, and data visualization, as well as gaining experience in data science projects.