Data Science vs Data Engineering: Decoding the Difference

Data is leading the whole world, wherein most of the decisions and innovations are driven by data, and professions in data science and data engineering are quickly gaining precedence. Finding insights in data was the main focus a few years ago. But as the sector developed, the importance of strong data management and getting the information out of it has become more important.

The change in viewpoint has highlighted the symbiotic relationship between data engineers and data scientists, bringing the function of data engineers to the fore. In this article, we will have an in-depth look at what data science and data engineering are, revealing the differences in education, responsibilities, salaries, required skill sets, and even career paths.

What is Data Science?

Data science is a multidisciplinary field that employs scientific techniques, procedures, algorithms, and systems to derive something useful. To achieve business intelligence, it fundamentally concerns the comprehension and interpretation of data to achieve meaningful and productive conclusions. Other fields that participate in the research constitute mathematics, statistics, and computer science, and additional domain knowledge is decisive in interpreting data.

Organizations nowadays rely more and more on data science to be able to predict trends and sustain competitive advantage while improving customer service and innovating new capabilities. Data scientists utilize these patterns to carry out actions and develop models, including predictive and prescriptive.

Core Components of Data Science

• Data Collection and Data Cleaning

At the onset of each data science project, raw data is acquired from numerous sources such as databases, APIs, IoT devices, etc. As the raw data can’t be used for analysis, the cleaning and preprocessing of the data is done to replace inaccurate information, impute missing values, and harmonize the data consumes most of the time of a data scientist.

• Exploratory Data Analysis

It involves looking at datasets to determine patterns, anomalies, and relationships that might exist within them, and doing the statistical analysis and visualization using tools like Matplotlib in Python. The objective is to get a feel for the structure of the dataset, to see if there are any trends, and to define the problem space as a basis for further modelling.

• Building Machine Learning Models

Predictive models are developed using ML algorithms by data science. Depending on the problem, this could belong to classification, regression, clustering, or recommendation tasks.

• Deployment & Monitoring

Models are put into production settings after they have been constructed and verified. This stage entails incorporating the model into processes and keeping an eye on its operation to make sure it keeps producing accurate results.

Data engineering involves designing, building, and maintenance of infrastructures and systems that store process, and rearrange data. While data science is more focused on analyzing insights, data engineering is more concerned with the development of extremely sturdy systems that ensure accessibility, reliability, and usability for analyses.

All in all, the main focus of data engineering is laying down the architecture at the base level so that it is possible to realize data-driven applications. The work of a data engineer is very different from data scientist, where it involves designing pipelines to move data efficiently from sources to storage and ultimately to analytical systems where data scientists and analysts can use it.

Core Components of Data Engineering

• Data Pipeline Development

The flow of data from its source to a destination is referred to as a data pipeline. Data engineers design and maintain these pipelines to collect, transform, and load data into data storage systems, such as warehouses or lakes.

• Database Design and Management

These professionals design and optimize databases to efficiently store large volumes of data. They use relational databases such as MySQL or PostgreSQL and non-relational databases that could be MongoDB or Cassandra.

• Big Data Processing

In many cases, organizations have datasets that are too large for traditional databases to handle. Data engineers might use big data frameworks such as Hadoop or Spark for processing and analyzing large-scale datasets. Such frameworks support distributed processing by the same data engineers who can now scale their operations.

• Data Integration and Synchronization

Data for an organization typically resides in varied stores. Data engineers make it possible for seamless integration and synchronization of the data across systems to present a unified view that can be exploited.

• Data Quality and Security

Data engineers put in place measures that data quality, integrity, and security. They monitor the pipelines for loss of data and handling errors, as well as the protection of sensitive information through encryption and access controls.

Who is a Data Scientist?

A Data Scientist is someone who has a handle on all the skills outlined above. Data scientists clean and organize (big) data. Usually, they have a pretty broad role that allows them to handle the data from start to finish. Often, they work quite closely with the business stakeholders in understanding the specific goals and questions to be analyzed for trends and models created about future outcomes. In other words, they decide what questions their team should be asking and work out how to answer them using data.

Roles & Responsibilities of a Data Scientist

1. Data Collection and Preprocessing

The first stage deals with obtaining data, where data scientists engage in the task of gathering raw data from different sources such as databases, APIs, and web scraping, as well as gathering user data.

2. Exploratory Data Analysis

Exploratory Data Analysis deals with the examination of the data sets to recognize the patterns of the data, relationships, and anomalies within the datasets. Data scientists usually rely upon statistical approaches and include using visualization (Matplotlib, Seaborn, Tableau) in their data explorations, providing direction on problem statements, and helping in analysis tasks.

3. Developing Predictive Models

Another task that is critical to the success of data scientists is the building of machine learning models for forecasting or enabling forward-thinking decisions. This phase also entails the selection of matching algorithms (regression, classification, and clustering) as well as setting up, training, and evaluating datasets on models.

4. Data Visualization and Communication

Although data scientists spend countless weeks sifting through datasets, at the end of the day, they must present their research to clients and sometimes other experts who might not have a technical background. This also means creating dashboards, visualizations, and reports, translating data science activities and metrics into useful, actionable business.

5. Collaboration

It is important to mention that data engineers are some of the first people data scientists go to to confirm the quality of the data. They also cooperate with the business teams to capture the need and verify the insights. Therefore, collaboration is one of the major responsibilities of data scientists working in an organization.

Educational Qualifications of a Data Scientist

A data scientist must have relevant expertise in Mathematics and Statistics, Computer Science, and domain-specific knowledge.

Skill Sets

The following skill sets are required to become a data scientist:

• Programming: Python, R, SQL, etc.
• Maths: Statistics, Calculus, Linear Algebra, etc.
• Libraries: TensorFlow (Machine learning), scikit-learn, pandas.
• AI and Deep Learning: Artificial neural networks, image recognition, etc.
• Tools: Jupyter Notebook, Tableau, Power BI.
• Soft Skills: Communication, problem-solving, and storytelling.

Who is a Data Engineer?

A data engineer is a person who designs, constructs tests, and maintains architectures such as databases and large-scale processing systems. Data engineers are the ones who are responsible for the design of the systems involved in collecting the data. They ensure the availability of the data for Data Scientists and Analysts. They may create specific data collection infrastructures and processes as well. It is more about technology than about business.

Roles & Responsibilities of a Data Engineer

1. Data Pipeline Development and Management

Data engineers create and manage pipelines that move data from a range of sources to a storage site and analytic tools. This entails sourcing API, IoT hardware, or other types of data, transforming it into a usable configuration, and loading it into data warehousing or lakes ( ETL-ELT).

2. Application of Big Data Frameworks

For large data sets, data engineers rely on big data technologies such as Apache Hadoop, Apache Spark, and Kafka. These frameworks facilitate the ability to leverage distributed computing, which includes data engineers conducting extensive data processing and autonomous analytical operations.

3. Data Consolidation and Synchronization

Frequently, organizations have data redundantly distributed over siloed systems. Data engineers are tasked with integrating and synchronizing the data from multiple sources to achieve this.

4. Maintaining Data Accuracy and Privacy

Data engineers take steps to protect data sent and stored from loss. They implement controls on data and ensure compliance with relevant policies and laws.

5. Data Streaming

Data engineers design applications that work in areas such as fraud detection or IoT, where real-time data ingestion and processing are crucial.

Educational Qualifications of a Data Engineer

A data engineer has more education in areas like computer science and software engineering.

Skill Sets

The following skill sets are required to become a data engineer:

• Programming: Java, Scala, Python.
• Tools: Spark, Kafka, Airflow, Nifi, Talend.
• Cloud Platforms: AWS, Azure, GCP.
• Big data: Hadoop, Hive, etc.
• Database Management: MySQL, NoSQL, MongoDB, PostgreSQL.

Career Path: Data Scientist and Data Engineer

Data Scientist

Entry-Level Roles: Starting in this field, they can start as:

1. Data Analyst: Pay close attention to data exploration, cleaning, and interpretation.
2. Business Analysts: They place a strong emphasis on applying data insights to operations and corporate strategy.
3. Junior Data Scientist: Develops models and analyzes data while working under more experienced professionals.

Mid-Level Roles:  The mid-level roles may include:

1. Data Scientist: Handle independent activities such as predictive modelling, data visualization, and machine learning.
2. Specialized Roles: Hold positions in specialized domains such as natural language processing, computer vision, or recommendation systems.

Senior-Level Roles: Everything about data comes from the senior role,s such as goals, strategies, and actions across the organization are centered around data.

1. Lead Data Scientist: Oversees major initiatives, leads teams, and guides more junior data scientists.

Data Engineer

Entry-Level Roles: Data engineers usually begin in entry-level positions like

1. Software Engineer: Acquire expertise in system architecture and backend development.
2. ETL Developer: Put your attention on creating pipelines for data extraction, transformation, and loading.
3. Junior Data Engineer: Help with the development and upkeep of data pipelines.

Mid-Level Roles: Data engineers can rise into more complex positions with expertise, such as:

1. Data Engineer: Create and oversee scalable data systems on your own.
2. Big Data Engineer: Utilize technologies such as Hadoop or Spark to process large datasets.

Senior-Level Roles: Senior roles frequently entail leadership and strategic duties:

1. Senior Data Engineer: Manages massively parallel data processing and oversees the design of next-generation data architecture.
2. Chief Data Architect: The chief data architect is in charge of the organization’s whole infrastructure and data architecture plan.

Salary Comparison

Based on the level of proficiency, the state, and the industry of work, a data scientist is paid between $115,000 and $130,000 in the US. This is an estimate for mid-career professionals; people with more than ten years of career history can earn up to $200,000 or above.

Data engineers in the USA earn annually a salary that ranges between $110,000 and $125,000, less than the average earnings that a data scientist earns but still reasonable. The base salary for senior data engineers who have cloud-level platform expertise or big data tools can also exceed $200,000 and total payments may reach similar levels or above.

For a job as a data scientist in India, beginning salaries range from ₹6,00,000 to ₹10,00,000 per year for freshers (1-3 years experience), which can increase to about ₹35,00,000 per year by the time their experience is 8 to10 years. A data engineer’s salary in India can range from as low as ₹5,00,000 per annum to as high as ₹18 LPA to ₹42 LPA. A data engineer’s salary is relatively lower than a data scientist’s and has variances depending on the area one works, the skills one possesses, level of experience, and so on.

Comparison of Data Science and Data Engineering

Also Read: Top Data Science Interview Questions and Answers

Conclusion

Data science and data engineering are the two fundamental aspects of the modern data ecosystem. These aspects are distinctly different from one another but are incomplete without the other. In this article, we have decoded the difference between data science and data engineering. We have covered the core components, applications, and challenges of both fields, along with the individuals- Data Scientist and Data Engineer, responsible for implementing the practices of both.

Data scientists perform the analytical and predictive role in the lifecycle of data. In contrast, data engineers create the proper sturdy structures and systems that store the data and make it available and usable. Both work together for a symbiotic relationship in which organizations have all the capabilities required to base their activities on the strategic use of innovation and decision-making with data. Learn more about data science and find out the differences in depth. Choose the Accelerator Program in Business Analytics and Data Science With EdX and Harvard University Aligned with Nasscom by Hero Vired and get certified.