Data Analyst vs Data Engineer vs Data Scientist

Sahiti Kappagantula
Edureka
Published in
5 min readDec 10, 2018
Data Analyst vs Data Engineer vs Data Scientist — Edureka

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:

  1. Who is a Data Analyst, Data Engineer, and Data Scientist?
  2. Skill Sets
  3. Roles and Responsibilities
  4. 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.

1.Data Science Tutorial

2.Math And Statistics For Data Science

3.Linear Regression in R

4.Data Science Tutorial

5.Logistic Regression In R

6.Classification Algorithms

7.Random Forest In R

8.Decision Tree in R

9.Introduction To Machine Learning

10.Naive Bayes in R

11.Statistics and Probability

12.How To Create A Perfect Decision Tree?

13.Top 10 Myths Regarding Data Scientists Roles

14.Top Data Science Projects

15.Top 5 Machine Learning Algorithms

16.Types Of Artificial Intelligence

17.R vs Python

18.Artificial Intelligence vs Machine Learning vs Deep Learning

19.Machine Learning Projects

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

24.Random Forest In R

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

30.Unsupervised Learning

31.10 Best Books for Data Science

32.Supervised Learning

Originally published at https://www.edureka.co on December 10, 2018.

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Sahiti Kappagantula
Edureka

A Data Science and Robotic Process Automation Enthusiast. Technical Writer.