Data Analysis vs Data Science vs Data Engineering

It is no longer a secret today that the key to successful business is data-driven decision-making. Data is at the heart of the decision-making process of all organizations today which has encouraged the emergence of data-based jobs in the industry as data analysts, Data engineer and data scientist.

I welcome you all to this blog for the key differences between the top three data-based jobs as data analysts, data engineers and data scientists. There is a great deal of ambiguity when it comes to the use of these topics which creates confusion. let's clear up all the confusion and find out how these job titles are different from the others. So, without wasting any time.

Who is Data Analyst?

A data analyst is one who collects, investigates and represents data in a way that everyone can understand. Data collected by Data Analysts usually comes from the same source. They are responsible for cleaning, editing, and translating raw data into functional business information, which is also used by the organization to make data-driven . Viewing data is an important part of their daily routine.

Who is Data Engineer?

Data engineers are responsible for building and maintaining the systems required by data scientists and data analysts to perform their functions. They create data centers for organizations, which means they make sure the data is accessible to anyone who needs to work on it. Accordingly, the main responsibilities of the Data Developer include, ensuring that data is properly received, modified and stored along with the building infrastructure or framework required for data production. Data engineers and data scientists work closely together, and as a result, many alternate between these two roles. Data engineers report to “big data” data scientists who are preparing it for analysis by a scientist.

Who is Data Scientist?

A data scientist is a professional who analyzes data strictly from a business perspective and is responsible for providing predictions that help the value of the business. They deal with both formal and informal data. Appropriate data centers where they can find relevant patterns to assist in the case of any business related problem. They make extensive use of machine learning for their predictive purposes, so training and developing data models is an integral part of their daily routine. Although Data Scientist can do most of the work done by Data Analysts.

Duties and Responsibilities Data Analyst:

• Collecting and interpreting data from source, analyzing results using mathematical strategies.
• Accessing data from primary or secondary data sources and maintaining website / data plans
• Data Mining - Where they have to organize raw data using various patterns or statistics. Also, extracting data from a company or external website to perform any type of research.
• Find patterns and trends in data sets.
• Create data dashboards, graphs and images, and provide field and skill measurements. The following, the roles of the Data Developer work Thus, their roles of daily tasks include tasks such as:
• Developing, constructing, inspecting and maintaining buildings.
• Provide and implement ways to improve the reliability, efficiency and quality of data.
• Building data pipelines> Creating and integrating APIs
• Improving data set processes for data modeling, extraction and production. The following is the role of data scientists
• Their main role is in selecting features, building, and developing class dividers using machine learning.
• Analyze relevant data by processing, filtering and compiling data
• Develop data algorithms and models that best suit a particular business need.
• Perform predictable analytics using machine learning concepts and Skillset Prediction Algorithms

 

Let’s start by discussing the set of skills and educational background required:

For Data Analysts:

• Basic knowledge of  languages ​​such as R, Python, SAS etc.
• SQL / Database information, as well as information on any data viewing tools such as Tableau,
• QlikView and PowerBI could be an additional benefit to you.
• Bachelor's degrees in computer science, mathematics, mathematics, information management or economics are not enough to begin your career as a Data Analyst.

For Data Engineer:

• Great skills are measured in this profile, as experience in Hadoop, MapReduce, Pig, Hive
• editing, Data Distribution. As architects and caregivers, their role is largely based on database systems, with complete SQL and NoSQL website information.
• Information on both of these technologies is essential if you want to expand your work
• horizontal over the field of data engineering 
• Bachelor's degrees in computer science, software engineering, applied math or math.
• Masters degrees are not compulsory at all.

For Data Scientist:

The job of a data scientist requires both strong business acumen and advanced data visualization competencies. Their conclusions must narrate a clear and compelling story to serve business needs. 

• For that, proficiency in any programming languages such as Python, R, Java, C/C++ or SAS are must. 
• Also, you must be acquainted with the skill sets in latest technologies such as Big data Hadoop. Machine learning or deep learning. 
• And as far as Education qualification is concerned, a bachelor's degree in computer science or software engineering, math, or statistics is preferred. However, Master's degree would come as an added advantage for you because if we look into current scenarios.

check out my Fiverr: https://www.fiverr.com/share/qPPZWd