Data Analyst vs Data Engineer vs Data Scientist
Data has always been vital to any kind of decision making. Today’s world runs completely on data and none of today’s organizations would survive without data-driven decision making and strategic plans. There are several roles in the industry today that deal with data because of its invaluable insights and trust. In this article, we will discuss the key differences and similarities between a data analyst, data engineer and data scientist.
Before we delve into the technicalities, let’s look at what will be covered in this article:
- Who is a Data Analyst, Data Engineer, and Data Scientist?
- Skill Sets
- Roles and Responsibilities
- Salary Trends
Who is a Data Analyst, Data Engineer and Data Scientist?
Data Analyst
Most entry-level professionals interested in getting into a data-related job start off as Data analysts. Qualifying for this role is as simple as it gets. All you need is a bachelor’s degree and good statistical knowledge. Strong technical skills would be a plus and can give you an edge over most other applicants. Other than this, companies expect you to understand data handling, modeling and reporting techniques along with a strong understanding of the business.
Data Engineer
Data Engineer either acquires a master’s degree in a data-related field or gather a good amount of experience as a Data Analyst. A Data Engineer needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization.
Data Scientist
Data Scientist is the one who analyses and interpret complex digital data. While there are several ways to get into a data scientist’s role, the most seamless one is by acquiring enough experience and learning the various data scientist skills. These skills include advanced statistical analyses, a complete understanding of machine learning, data conditioning etc.
For a better understanding of these professionals, let’s dive deeper and understand their required skill-sets.
Skill-Sets
The below table illustrates the different skill sets required for Data Analyst, Data Engineer and Data Scientist:
As mentioned above, a data analyst’s primary skill set revolves around data acquisition, handling, and processing. A data engineer, on the other hand, requires an intermediate level understanding of programming to build thorough algorithms along with a mastery of statistics and math! And finally, a data scientist needs to be a master of both worlds. Data, stats, and math along with in-depth programming knowledge for Machine Learning and Deep Learning.
Now that we have a complete understanding of what skill sets you need to become a data analyst, data engineer or data scientist, let’s look at what the typical roles and responsibilities of these professionals.
Next, let us compare the different roles and responsibilities of a data analyst, data engineer and data scientist in their day to day life.
Roles And Responsibilities
The roles and responsibilities of a data analyst, data engineer and data scientist are quite similar as you can see from their skill-sets. Refer the below table for more understanding:
Now data scientist and data engineers job roles are quite similar, but a data scientist is the one who has the upper hand on all the data related activities. When it comes to business-related decision making, data scientist have higher proficiency.
After these two interesting topics, let’s now look at how much you can earn by getting into a career in data analytics, data engineering or data science.
Salary
The typical salary of a data analyst is just under $59000 /year. A data engineer can earn up to $90,8390 /year whereas a data scientist can earn $91,470 /year.
Looking at these figures of a data engineer and data scientist, you might not see much difference at first. But, delving deeper into the numbers, a data scientist can earn 20 to 30% more than an average data engineer. Job postings from companies like Facebook, IBM and many more quote salaries of up to $136,000 per year.
With this, we come to an end to this article. If you wish to check out more articles on the market’s most trending technologies like Python, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series which will explain the various other aspects of Data Science.
2.Math And Statistics For Data Science
9.Introduction To Machine Learning
12.How To Create A Perfect Decision Tree?
13.Top 10 Myths Regarding Data Scientists Roles
15.Top 5 Machine Learning Algorithms
16.Types Of Artificial Intelligence
17.R vs Python
18.Artificial Intelligence vs Machine Learning vs Deep Learning
20.Data Analyst Interview Questions And Answers
21.Data Science And Machine Learning Tools For Non-Programmers
22.Top 10 Machine Learning Frameworks
23.Statistics for Machine Learning
25.Breadth-First Search Algorithm
26.Linear Discriminant Analysis in R
27.Prerequisites for Machine Learning
28.Interactive WebApps using R Shiny
29.Top 10 Books for Machine Learning
Originally published at https://www.edureka.co on December 10, 2018.
