What is the Difference between Data Science and Data Engineering?
India has been making some serious waves in the world of data. Just like the rest of the world, we're seeing a massive jump in the demand for data science and data engineering roles. And this isn't just a passing trend.
Companies, big and small, are hungry for data. They want to gather it, crunch it, and use it to supercharge their businesses. And guess where a lot of this action's happening? Tech hotspots like Bengaluru, Hyderabad, and Pune!
But here's the thing: within this exciting world of data, there are some roles that often get mixed up – like data scientists and data engineers.
If you've been scratching your head, willing to understand the difference between data science and data engineering, or wondering which path might be the perfect fit for you, this blog post is for you. Let's begin.
Table of contents
- Is There a Difference Between Data Science and Data Engineering?
- Data Science Vs. Data Engineering: Roles and Responsibilities
- What Does a Data Scientist Do?
- What Does a Data Engineer Do?
- Data Science Vs. Data Engineering: Educational Requirements
- What Are the Educational Requirements to Become a Data Scientist?
- What Are the Educational Requirements to Become a Data Engineer?
- Data Scientist Vs. Data Engineer: Salary Differences
- How Much Do You Earn as a Data Scientist?
- How Much Do You Earn as a Data Engineer?
- Data Science Vs. Data Engineering: Career Paths
- What's a Typical Career Path for a Data Scientist?
- What's a Typical Career Path for a Data Engineer?
- Can a Data Engineer Become a Data Scientist?
- Data Scientist Vs. Data Engineer: Which Is Best for You?
- When Should You Consider Becoming a Data Scientist?
- When Should You Consider Becoming a Data Engineer?
- Frequently asked questions
- Is data science the same as data engineering?
- Which is better, data science or data engineering?
- Is data engineering harder than data science?
- What is the future of data science and data engineering?
- How much do data science and data engineering freshers make in India?
Is There a Difference Between Data Science and Data Engineering?
Back in the day, data scientists used to perform some of the duties of data engineers. However, as the field of data has grown and evolved, businesses now seek more intricate analysis and insights from the data gathered, and the roles have become more distinct.
Today, data scientists are responsible for analyzing data to predict trends, extracting insights, and harnessing data to drive business decisions. On the other hand, data engineers are tasked with building robust data systems and organizing data gathered by data scientists.
Data Science Vs. Data Engineering: Roles and Responsibilities
The roles of data engineers and data scientists overlap to some extent, especially in terms of the tools and technologies they use. However, a data engineer's primary focus is setting up and maintaining data infrastructure, ensuring data is available, reliable, and ready for analysis.
On the other hand, data scientists work with processed data provided by data engineers to derive patterns, predictions, and actionable insights for businesses.
What Does a Data Scientist Do?
Data scientists play a crucial role in eCommerce, finance, and healthcare sectors. They are responsible for decoding complex data to predict consumer trends and derive actionable business insights.
What Does a Data Engineer Do?
Data engineers are critical for creating the foundation for analysis. Their responsibilities include managing and optimizing data pipelines, ensuring seamless data flow, and making data readily available.
In India's growing startup ecosystem, data engineers have become increasingly important in integrating various data sources, managing big data technologies, and ensuring data scalability and security.
Data Science Vs. Data Engineering: Educational Requirements
The educational path to data science and data engineering roles in India is diverse. While a bachelor's degree in computer science or related fields is common, it's not uncommon for professionals to transition from other backgrounds equipped with relevant certifications and courses.
What Are the Educational Requirements to Become a Data Scientist?
When it comes to data science roles in India, candidates often come from backgrounds like computer science, statistics, or even physics. You must be fluent in programming languages like Python, R, SQL, and Java, with experience using tools like Hive, Hadoop, MongoDB, and Cassandra.
GUVI offers a specialized data science course with placement tailored to aspiring data scientists. Gain practical experience by working on real-time projects from an IIT-M recognized institute of excellence.
What Are the Educational Requirements to Become a Data Engineer?
To become a data engineer in India, you must possess a degree in computer science or IT, with proficiency in languages such as Java, Python, SQL, and Scala being imperative.
Many data engineers also pursue certifications like Google's Professional Data Engineer or IBM Certified Data Engineer, which are highly valued in the job market. GUVI offers a premium zen-class data engineering course with job placement. It is one of the industry-leading data engineering programs featuring a faculty of experts from A-list companies, allowing you to be the best in the field.
Data Scientist Vs. Data Engineer: Salary Differences
The salaries for both data scientists and data engineers are competitive in India, especially in tech hubs like Bengaluru. However, it all depends on your experience and ability to upskill yourself constantly.
How Much Do You Earn as a Data Scientist?
Data scientists, given their role in decision-making, generally command higher salaries. Per Glassdoor, the average salaries range from ₹8 lakhs to ₹20 lakhs. You may earn even higher if you possess specialized skills or work in multinational companies.
How Much Do You Earn as a Data Engineer?
A data engineer's salary varies based on experience and location. Per Glassdoor, the average salary of a data engineer in India ranges anywhere between ₹6 lakhs to ₹14 lakhs annually, depending on your expertise and the company you work for.
Data Science Vs. Data Engineering: Career Paths
There is no established path to becoming a data scientist or a data engineer in India. Many professionals transition from related fields, leveraging their skills and upskilling when necessary.
Let's discuss some common ways professionals have navigated the field to get their dream jobs.
What's a Typical Career Path for a Data Scientist?
A lot of data scientists begin their journey in starter roles, like internships or positions as junior data scientists. These first jobs let them polish their skills while working on tasks given to them. Over time, they get to take on bigger challenges and design their own experiments.
A popular path to becoming a data scientist is starting as a data analyst. After gaining some experience and learning more, analysts often shift into data science roles. This can happen by self-learning or through online courses. GUVI offers premium data science courses to make it easier for you to transition to a successful data science career.
What's a Typical Career Path for a Data Engineer?
Most professionals don't start their careers directly as data engineers. Instead, they often begin in areas like software engineering or roles that deal with data and systems, like database developers or systems analysts. These positions help them get familiar with the important tools and systems used in data science.
As they gather more experience, they dive deeper into specialized roles like data architect or solutions architect. These jobs help them sharpen their skills, understand data better, and learn about cloud computing. Some even spend time in data analytics to learn what data experts need.
Can a Data Engineer Become a Data Scientist?
Yes, data engineers can become data scientists and the other way around, especially in our flexible tech industry. Both roles share many foundational skills, from knowledge of programming languages to working with data pipelines, making the switch easier.
But there are differences: as a data engineer, you mostly deal with building and maintaining the systems, while as a data scientist, you use data to come up with insights.
To make the transition, you will need to skill up to fully grasp the specifics of your new role.
Data Scientist Vs. Data Engineer: Which Is Best for You?
Both data scientist and data engineering roles offer immense opportunities. It ultimately boils down to your strengths and personal inclinations.
When Should You Consider Becoming a Data Scientist?
As a data scientist, you will use data to understand the past and forecast what might come next. So, you should be the person who loves asking questions, digging deep into data, and predicting future trends to be a data scientist.
You should enjoy using data to tell stories and find fulfillment in running advanced statistical analysis, writing machine learning algorithms, solving problems, and influencing business decisions.
When Should You Consider Becoming a Data Engineer?
As a data engineer, you will focus on creating the infrastructure that stores and organizes data. So, you should be someone who loves building strong systems, managing and organizing data, and always looking for ways to make things more efficient to become a data engineer.
You must enjoy using the latest tools and technologies and thrive on making sure data is always available for critical business decisions.
The demand for data scientists and data engineers is equally on the rise. Although both roles overlap to an extent, they serve distinct functions. Data scientists analyze data to predict trends, extract insights, and drive business decisions. While data engineers, on the other hand, build robust data systems and organize data for analysis.
If you're considering choosing between the two, reflect on your strengths and passions. Data science may be your path if you have an analytical mind and are curious about patterns and insights. However, if building, optimizing, and ensuring data flows seamlessly is your forte, data engineering may be the perfect fit.