Data science is one of the fastest-growing fields and has become more competitive as talented and smart people are stepping into this industry in search of long-term, prosperous careers. When you are trying to pursue a career in Data Science Certification in Denver, it is important to do everything you can to polish your skills and establish your credibility as one of the top candidates for the job. This involves gaining the necessary knowledge, training, and certifications that employers seek nowadays. No wonder training programs like a data science course and data engineer Bootcamp have become the top choice of professionals to build a strong foundation in this field.
When you look at any job portal or go through job outlook-related surveys, you will come to know that data-related jobs have high demand and are also associated with higher paychecks. The various roles available in the field of data science are data scientists, data analysts, data engineers, data architects, machine learning engineers, business intelligence analysts, and so on. However, the two most common roles among these are data scientists and data engineers. People new to this field often tend to get confused between these two roles.
If you are interested in starting a data science career, then you should know the difference between a data scientist and a data engineer. And this article is the right place to dive into this topic. So, let’s get started.
What is a Data Scientist?
Though the data scientist job role is much hyped, people don’t understand what exactly the responsibilities are handled by them. Simply put, a data scientist is someone who collects and analyzes a massive amount of data (both structured and unstructured) to find hidden trends and patterns in them. Such trends are important for business leaders as they give actionable insights to make better business decisions. It generally combines the roles of mathematics, computer science, and statistics to create data plans for companies.
Though the actual responsibilities of a data scientist vary from organization to organization, here are the general tasks they handle in their day-to-day life:
- Understand the business problems an organization is trying to solve through data science.
- Contribute their efforts in the entire data science lifecycle, beginning with data collection, data cleaning, and data analysis.
- Perform exploratory data analysis and later build predictive models to solve the business problems
- Analyze a large amount of data to discover important correlations.
- Visualize the findings through interactive dashboards on tools like Tableau and Power BI.
What is a Data Engineer?
Data engineers form the backbone of any data science operation of an organization. Such professionals handle the delivery, storage, and processing of data, providing a reliable infrastructure for these functions. Working in this role means focusing more on areas like data workflows, data pipelines, and the ETL process or Extract, Transform, Load process. You need to have good programming skills and must be familiar with Apache Spark, Hadoop, working of databases, automation, scripting, and many other concepts to succeed in this role.
Here are some of the responsibilities associated with a data engineer job profile:
- Expand and optimize data and data pipeline architecture
- Optimize the flow and collection of data sets for cross-functional teams
- Transform and transport data from a data source to a data warehouse using ETL pipelines.
- Clean the data and transform it into a usable format so that it can be taken up for analysis.
- If there is an unexpected failure, then analyze the risk and ways to mitigate them.
Data Engineer and Data Scientist – The Difference
Here goes the difference between the two data related roles data scientist and data engineer. To put in simple words, the task of a data engineer begins early in the data science lifecycle, while that of a data scientist starts late. Right from the first phase, i.e. data collection, the data engineer ensures that data workflow and and its underlying infrastructure is built and maintained. The data, right at the collection stage, isn’t ready for analysis. So, it is the responsibility of a data engineer to clean the data, i.e. remove duplicate entries, missing values, or corrupt information and finally transform the data into a single usable format. At this stage, the data can be taken up by data scientists.
In later phases like data modeling and analysis, data scientists contribute the most. They are involved in data manipulation, create hypothesis, test, analyze the clean data to uncover meaningful trends and patterns. Through these trends, they find solutions to critical business problems like reducing the operational costs, optimizing business processes, improving the features of a product, prevent losses, and ensure customer satisfaction.
Based on skills required, data engineers need to be more skilled in big data technologies, using ETL tools, data warehousing, advanced programming, distributed systems, and data pipelines. On the other hand, data scientists need to be proficient in advanced mathematics, statistics, machine learning, and advanced analytics. This doesn’t mean that skills required for both the roles are entirely different. Topics like data analysis, R programming, Python programming, big data concepts, and SQL need to be learned by both data scientists and engineers.
Now that you have a clear idea of both the job roles, you can decide whether you want to become a data scientist or a data engineer.
