We are in an age where Data is everything. Amount of data burst every single second is humongous. Data science along with its different aspects focus on getting valuable information out of the data, for improvement of every system.
Everybody knows data science is a hot cake that sells fast and offers the most lucrative professional opportunities in technology.
Data analysis was first used to analyze data and it has evolved in modern data science.
So, what is Data Science? To simply put, Data science is the method of deriving valuable information out of large sets of data.
Data engineering focuses on building the architecture and platform for the collection of data and gives the data output in a usable form.
The three main careers with data science are Data analysts, Data engineers and Data scientists.
At first Data scientists had the role of both data scientists and data engineers, which is a huge skill set to cover. With the advent of big data, more streamlining required, thus came a new job role, data engineers or big data engineers.
The number of skilful people in data science is low, and hence there is a huge career opportunity with data science.
Let’s see the difference between data engineer, data analyst and data scientist.
Who is a data engineer?
A data engineer is a person who uses computer science engineering to build data infrastructure and pipelines for gathering and maintaining data. The main work of data engineers is to design, build, integrate and manage data and give data in a usable form for the data analysts and data scientists to compute with. They closely monitor the quality of data pipelines and infrastructure and ensure that data is accurate and easily accessible.
Some companies term data engineering as data infrastructure or data architecture & the associated job role is data architect.
Data engineers don’t involve in the analysis of data; hence they don’t involve in decision making based on data.
In short, data engineers receive, store, clean and prepare the data for data analysts and data scientists.
Education & Skill Set
A data engineer should mainly have a computer science engineering or software engineering background. They should possess strong programming skills such as Java, C#, Scala or Python. Sound knowledge in SQL and NoSQL databases (MongoDB, Cassandra DB) and frameworks like Apache Hadoop, Hive. Along with programming and database skills, data engineers should have strong mathematical(statistics) and big data skills.
According to Glassdoor, the number of job openings for data engineers is almost five times higher than that of data scientists. Even in this pandemic, there is a drop-in data scientist job vacancy, but the openings of the data engineers are on the rise.
As per PayScale, data engineers get around $63k to $131k yearly salary. The average salary of data engineers according to Glassdoor is $172k. DataCamp mentions salary of data engineers are minimum of $43K to $364k, based on years of experience and expertise.
Who is a data analyst?
Data analysts cleanse and analyze data with the help of statistical tools to derive business decisions from historical data. They focus on finding insights for specific business goals. Data analyst job will typically analyze data for a specific query and present them in visual form. Data analyst to some extent helps the organization to make data-driven decisions based on historical data.
Education & Skill Set
A data analyst can be considered an entry-level job role. They don’t have to possess strong programming skills or algorithm skills. Graduates with mathematics, statistics or any math-related fields with a bachelor’s degree can pursue data analyst jobs. The essential skills of a data analyst include statistics, communication, business knowledge and fair knowledge in excel or any computing software, tableau and SQL.
According to Robert Half Technology’s 2020 Salary guide, Data analysts earn an average of $83k per year.
Who is a data scientist?
Data scientists are actually predicting the future by manipulating data to solve critical business problems and provide predictive algorithms based on the model they used.
The job role of data scientist and data analyst may look the same on a wider level, but data scientists work with a large volume of big data and use predictive analysis, machine learning and programming skills to automate the process.
A data scientist is responsible for cleaning, processing and verifying the data from data engineers and performing analysis on the data using advanced automation and present the business with useful insights and solutions.
Education & Skill Set
Data scientists should typically possess a master’s degree in the field of Data Science. They should have extensive programming skills to create various algorithms for data automation. They should have command over mathematical and statistical skills. A data scientist should have hands-on knowledge in machine learning, coding, software development, data mining and data analysis, python, SQL and NoSQL database, python, Scala, Java, etc.
According to PayScale and Glassdoor, the average salary of data scientists ranges from $80k – $130k. As per DataCamp, a data scientist earns $34k – $341k based on their experience and expertise.
So, which one is for you? Data Analyst or Data Scientist or Data Engineer?
The job role mainly depends on your educational background and skill interest. If you are more inclined towards programming and love building things, data engineering is the way to go.
You don’t like programming but would love to compute and work with data in an analytical & statistical manner – data analyst should be your job role.
On the other hand, if you love the manipulation of data, along with programming skills and highly analytical – data scientist will be your ideal job.
In the end, it is personal interests that matter. You can develop the right skills for any of the above job roles in data science if you invest time, money and determination to learn them!
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