Data Scientist Vs Data Engineer: Data plays a vital role in the growth and evolution of any organization. Technology is evolving with each passing day, however in comparison with other countries, India is a bit slow in the data field. Despite that, the data industry has witnessed a huge boom. Now, companies are taking interest and learning how they can provide valuable insights to grow business with data analytics. Still, there are many who seek for clear vision and learning about data scientist vs data engineer.
In spite of the fact that data scientists and data engineers have similar skill sets, they fulfil multiple job roles in the fields of Big Data and AI development systems. The data scientist fosters analytical models, while data engineers deploy those models under production. All things considered, data scientists primarily focus on analytics, whereas data engineers rely more vigorously on programming.
Top notch insights and management are significant components for utilizing data to its fullest potential. At EngineerBabu, the data scientist and data engineer work in harmony to streamline data presentation and strategy. We’ll walk you through the responsibilities and job roles of data scientist and data engineer, so you can figure out how to utilize data for your potential benefit. Let’s learn it in detail.
Data Scientist Vs Data Engineer: What They Do?
What is a Data Scientist?
A Data Scientist analyzes and interprets data to solve business related issues. At first, data scientists investigate data and perform market research to formulate business inquiries or questions based on a particular pattern or problem area. The data scientists should then design business questions as data analytics issues.
To recognize basic patterns in a data set, data scientists utilize advanced analytical technologies supported by statistics and machine learning. Data Scientists construct models to set up relationships between data objects. However, the Predictive models forecast future occasions dependent on previous existing records. While prescriptive models suggest significant changes in business strategy dependent on current and historical information.
Data Scientists should likewise interpret the consequences of their analysis to design data-driven business arrangements. At the point when data scientists present their discoveries to stakeholders, they should construct a cohesive narration that imparts the meaning of their results and how those results can advise business strategies.
What is a Data Engineer?
A data engineer can be represented as a data proficient who develops the data infrastructure for analysis. They are centered around the production status of data and things like resilience, formats, security, and scaling.
Data Engineers as a rule hail from a software engineering background and are capable in programming languages like Java, Scala, and Python. On the other hand, they may have a degree in math or statistics that assists them with applying diverse analytical approaches to deal with business issues.
They are likewise knowledgeable about developing and managing distributed systems for the analysis of enormous volumes of data. Nonetheless, their essential target is to help data scientists transform a pool of data into important and actionable insights.
Data Scientist Vs Data Engineer: Role Requirements
What Are the Requirements for a Data Scientist?
Data Scientists should be acquainted with the accompanying programming languages:
In light of current requirements, this is what you’ll have to get a regular mid-level work:
- Master’s Degree or Ph.D. in Computer Science, Math, Engineering or a relevant quantitative field.
- At least five years of experience in an Analytics or Data Science Job role.
- Excellent proficiency in SQL.
- Working experience with Java and Python.
- Good Analytical and mathematical skills.
- Experience in Data Mining methods.
- Knowledge on advanced statistical concepts and methods.
- Hands-on knowledge of Predictive Modeling Algorithms and frameworks.
- Working experience with Machine Learning techniques (such as, artificial neural networks, decision tree learning, and clustering).
- Experience in creating automated work processes (Python or R).
- Experience in using web services like DigitalOcean, Redshift, Spark, and S3.
- Experimental designing experience and A/B testing.
- Experience in visualizing and presenting data utilizing Business Objects, Periscope, ggplot, and D3.
- Experience working in a cloud system with huge data sets.
- Proven working experience in Hadoop.
- Experience with both Relational Database and NoSQL Database (for instance, Couch, MongoDB, and Neo4J).
- Good understanding of architecture and system integration.
- Experience in data analysis from third-party suppliers like AdWords, Google Analytics, Facebook Insights, and Hexagon.
What Are the Requirements for a Data Engineer?
Data Engineers need to know the accompanying programming languages:
In light of current requirements, this is what you’ll require to get the data engineer designation:
- Bachelor Degree in Statistics, Computer Science, Information System, or another relevant quantitative field.
- Minimum five years of professional experience or a Masters Degree with minimum three years of experience.
- Advanced working knowledge on SQL (composing and troubleshooting).
- Experience working with query composing, relational database, and knowledge over other databases.
- Experience managing, developing, and optimizing big data models and pipelines.
- Working experience with PostgreSQL, MongoDB, and Redis.
- Experience performing inner and outer root cause analysis.
- Strong analytical skills while working with unstructured data sets.
- Cloud-based data solution working experience (e.g., AWS, EC2, EMR, RDS, and Redshift).
- Proven work experience in effectively processing, manipulating, and extracting values from huge and disconnected data sets.
- Excellent Project and Organization Management Skills.
- Experience with configuration and automation management.
- System Monitoring, alert, and dashboard experience.
- Hands-on experience with tools like Hadoop, Kafka, and Spark.
Difference Between Data Scientist and Data Engineer
Taking everything into account, there are many similarities between a data scientist and data engineer. The thing that makes them different is what they are focused on. How about we investigate the principle difference between both i.e., data scientist vs data engineer:
A. Data Engineer: A data engineer’s objectives are more centered around tasks and development. They are liable for building automated systems and model data structures to work with data processing. Subsequently, their goal is to develop and create data pipelines and tables to help data customers and analytical dashboards.
Data Scientist: On the other hand, data scientists are more focused on the queries. They need to ask and answer queries in order to minimize the overall expenses, increase profit, and improve customer experiences. Accordingly, data scientists gather support, analyze, and propose a conclusion to the inquiry or question. Some of the frequent inquiries that are faced, includes:
- What sort of advertisements would get the customer to buy something?
- Is there a speedier way for package delivery?
- What impacts patient readmission?
B. Data Engineer: Evidently, both data engineer and data scientist usually rely on SQL and Python. Despite that, the tech jobs vary a lot for both data engineers and data scientists. Data Scientists use libraries like Pandas and SciKit Learn. Whereas, data engineers use Python to manage pipelines. Libraries like Airflow and Luigi are valuable in such a manner.
Data Scientist: The questions of data scientists are more centered around ad-hoc. Data engineer questions are directed towards data transformation and cleaning up. The Data Scientists use tech-tools like Jupyter Notebook, Tableau, and so on.
C. Data Engineer: With respect to background, both data engineers and data scientists are needed to have a specific level of understanding for data and programming. Whereas, there are a few differences that surpass programming.
Data Scientist: Since data scientists are more similar to analysts, having a research-based foundation is an advantage. This could be in anything going from financial aspects to psychology to epidemiology, or anything as. As far as skills are concerned, data scientists ought to have a blend of SQL and Python experience along with a good business sense.
Despite the profession you choose, it will be fundamental to equip yourself with advanced degrees and certifications. All things considered, more organizations are acknowledging the worth of alternative education.
While there is some crossover when it is about required skills and job role responsibilities. These are not a type of interchangeable jobs. So you’ll need to make a firm decision and have some expertise in either. In any case, both positions have an amazingly positive and rewarding job outlook.
However, if you like to explore both data science and data engineering, then, at that point, you could look for a career in Machine learning. The Machine Learning Engineers are capable in both data science and data engineering and have sufficient knowledge and experience to work in both fields.
If you are looking to hire such experts then EngineerBabu is the right place for you. We are an experienced team of data scientists and data engineers to support our clients in taking their business to the next level. For any query or assistance, you can reach out to us and hire expert data scientists and data engineers or a machine learning engineer.