With the current boom in big data analytics and raw data for solving business roadblocks, data science jobs have spread at a wide rapid pace. Data science and data engineers both belong to the same category and even help in aiding maximum performance. Although Data science remains at the forefront, data engineering is pacing fast at a steady rate with special system architecture skills. Data science and data analytics must be ready for analysis. Both these job profiles are similar, there are few differences in job outlook, qualifications, skills & salary. For becoming a successful data engineer, one must pursue a data engineer course.
Data scientists process raw data into an effective and meaningful pattern with the help of data mining techniques and various analytical skills. Data engineers are responsible for designing infrastructures that aid data scientists in performing their tasks by creating data pipelines and even sensible systems. Data scientists have higher pay than data engineers as per data from employment websites because of the increased demand for data scientists. Both these working professionals are offered abundant job opportunities due to a spike in data management.
Let us dive deep into the difference between Data Engineer and Data Scientist:
As data scientists perform statistical analysis and this helps in extracting meaningful patterns from large datasets whereas data engineers work for the practical use of data acquisition. Data scientists professionals even involve the use of advanced techniques such as collecting, neural networks, and decision making for deriving meaningful conclusions. This is an extensive field that combines maths, statistics, and computers science. In addition to this, data scientists have knowledge of business profiles that facilitate the interpretation. The main area of interest includes big data, machine learning, and data mining.
In brief about data engineers and data scientists:
Data engineer builds a framework and the structure of data analytics for a company. This holds a great significant role for the company as they change raw data into the structure on which data scientists work. Data engineers make sure that a continuous flow of data is required. These are responsible for building new data analysis tools and software and it this even have possible that ensures compliance with data security.
Data scientists help in diving deep into data and this even brings meaningful business insights which are essential for decision making. They work on creating and deploying AI-based algorithms in different aspects of the company to resolve company problems.
Data engineer: this professional is responsible for creating a structure that is needed to analyze and even work on data. Let us check some of the skills:
- Database architecture: a data storehouse where large quantities of data are gathered for analysis. This data is even used for analytics, data mining, and performing interpretation. A data engineer is familiar with basic data warehousing concepts.
- ETL: this tool helps in extracting data and this even transforms into the form which has to be analyzed. ETL tools help in gathering data from various sources, changing the concepts, and store in a database for analytics professionals in the company.
- Data structure knowledge: data engineer expects one to be holding good knowledge of data structures as well. This helps in understanding the business goals of the company and this even delivers solutions on the basis of the same.
- Programming skills: professional must even hold knowledge about Python, Java which is required for being a data engineer. Python is one of the most in-demand skills. Strong coding skills help data engineers to work in various languages.
Data scientist: this is one of the highly demanded jobs. Let us check some of the most demanded skills:
- Strong analytical & maths skills: whenever any company is hiring this professional the main aim of them is to analyze statistical and mathematical skills. Even for creating ML programs, the foundational concepts of statistics have to be top-notched. This professional holds the knowledge of distributions, confidence, etc.
- Programming skills: this possesses programming skills in the language R, python. Python has emerged as one of the popular choices for data scientists. This programming language makes it very easier and quicker for a data scientist to come out with insights from big datasets.
- Analytical skills: data scientists even work for analytical skills that help companies make better and more effective decisions. This becomes fundamentally important for the data scientist to get a glimpse of the requirement of the company.
Data Scientist vs Data Engineer: Salary
The salary of two working professionals varies according to the skills possessed. The salaries of the two based on the data from different job-hunting websites are below:
Data scientists are offered a pay scale of around $140,000 and data engineer is offered around $138,000. The difference in pay between data engineers and data scientists is not outrageous and is considered quite big. This is stipulated as this difference between a data scientist and a data engineer is because of the varying job demand for data scientists and data engineers.
Data Engineer vs Data Scientist: Demand
Data engineer and data science roles are growing by 35% annually. However, this has shown that data scientist is at 3rd position and data engineers is at 8. According to the job-hunting websites, there are 85,000 job openings for data engineers whereas there are about 110,000 job openings are for data scientists. Usually, companies prefer recruiting highly skilled data scientists and data engineers, an increase in data management issues has created a spike in demand for positions.
Due to the nature of work, data scientists and engineers perform various tasks for achieving tedious goals. However, if you are interested in becoming a data engineer must pursue, Data Engineer Course. Instead of choosing one over the other, companies usually keep a healthy mix of two to reap effective and maximum benefits out of the power.
You can become a data engineer and possess skills in software design, data architecture, etc. You must also require to have a strong command of SQL. Even students or professionals must hold Python and Bash which is a requirement in data engineering job descriptions.