Data Engineering vs. Data Science Explained

Data Engineering vs. Data Science explained: How do they differ?

Data Engineering is a broad term that refers to the design and maintenance of infrastructures that enable Data Scientists to perform their work. It involves analyzing and building systems that support data collection, analysis, and dissemination. The systems architecture of an organization is closely related to the infrastructure, which includes the various platforms used for processing and storing large volumes of data.

• Some vital parts of Data Engineering include:
• Data pipelines
• ETL (Extract, Transform, and Load)
• Data storage and processing

Data science is extracting valuable insights from the vast amount of data collected by various sources. It involves analyzing data and using it to train machine-learning and statistical models. One of the most critical factors that Data Scientists should consider when developing their skills is domain knowledge. This is because they will not understand how everything fits together without this. Aside from analyzing data, Data Scientists also play a vital role in communicating the value of their findings to non-technical stakeholders, therefore it is necessary for Data Scientists to have knowledge about various tools such as slide decks and dashboards. Some crucial facets of Data Science Jobs include:

• Statistics and linear algebra
• Machine learning and algorithms
• Computer programming

Data Engineer vs. Data Scientist: Their roles and skills in an organization

Both these experts in Data Science and Data Engineering are required to have basic knowledge about programming languages like SQL and Python. They may have similar educational backgrounds in Computer Science or Computer Engineering. However, Data scientists are needed to have more analytical skills, while data engineer jobs require logical and complex problem solving skills.

While the daily tasks of Data Engineers and Data Scientists may seem to meet along the line of work, their roles and expectations are different. Even in job posting descriptions, similar skills may be required for these two distinct but similar roles; however, their duties and special skills should not be confused. Below are the required skills, competencies, and responsibilities of both Data Engineers and Data Scientists.

Data Engineer
Roles:

• Arrangement of data problems into a programmed system
• Design and preparation of Big Data awaiting implementation and analysis
• Developing complex queries for data pipelines and ETL operations
• Recommending ways to improve data
• Finding out opportunities for data collection
• Preparing data for predictive modeling using machine learning and statistical methods
• Deployment of Machine Learning and statistical models

Skills and competencies:

• Data Warehousing & ETL
• Advanced programming knowledge (JAVA, Python or Scala)
• Machine learning concept knowledge
• Data architecture & pipelining
• Scripting, reporting & data visualization
• In-depth knowledge of SQL/ database
• Knowledge of MySQL, Hive, Oracle, Cassandra, and Redis
• Interpersonal skills
• Organizational and managerial skills

Data scientist
Roles:

• Integration of data for analysis
• Development of operational models
• Bridge the space between consumers and stakeholders
• Strategic planning for data analytics
• Using machine learning and deep learning for data optimization

Skills and competencies required from a Data Scientist include:

• Data mining
• Optimization of data
• Good decision-making skills
• Hadoop-based analysis
• Understanding of deep learning frameworks like TensorFlow, PyTorch, Django, Flask
• Knowledge of Python and R programming languages
• Understanding of packages. For example- Scikit-Learn, NumPy, Matplotlib

Data Engineering vs. Data Science: The job outlook

The creation of roles and titles is a process that is designed to reflect the changing needs of our time. There is an increasing interest in data management in this technological age, as many companies are looking for flexible and cost-effective solutions to manage and store their data. To do this, they have to move their data to the Cloud and also build 'data lakes’. These complement their existing data warehouses and are designed to store and access their data.

Due to the need to replace the data flows soon, the number of job postings for Data Engineers has increased. Similarly, the role of Data Scientists has been in demand since the beginning of the hype. Still, now, companies are starting to hire individuals with more specialized skills such as creativity, technical expertise, communication skills, and what have you.
However, it is hard for recruiters to find candidates with the right skills and qualities for the job as demand overshadows supply. Conversely, some may argue that the hype for data-relation jobs is dying off, but one thing is sure: the need for data scientists will always be there. According to research carried out by McKinsey & Company in the US, there could be a shortage of up to 190,000 individuals with deep analytic skills in the next couple of years. This shortage would allow companies to hire 1.5 million individuals who have the skills needed to analyze and make effective decisions.

Interestingly, the career paths of Data Engineers and Data Scientists are very similar. Data Engineers may start off as Software Engineers, Data analysts, or have similar engineering backgrounds. Data Scientists may as well start off as Computer Science entry-workers, and then venture into Data Analysis and then Data Science.
According to Payscale, the average annual salary of a Data Engineer is $93,000 and that of Data Scientists is $97,000.

Conclusion

Both Data Engineering and Data Science are complementary to each other, and they are as essential to the organization as the other. Without one, the other will be left handicapped in operations and effectiveness. Therefore, their combined use cannot be overstated in any organization.