Data Analytics vs Data Science – Which one to choose

March 04, 2022

Data Analytics vs Data Science – Which one to choose

 

 The era of technology is evolving towards innovation and digitalization, AI, machine learning prominently. Data analytics and Data scientists have long-term career potential across the globe. The role of a data analyst is to make the most appropriate decision for the company by analyzing all the customer-related data, market-related data using several techniques. In brief, we can say a data analyst is one who collects existing data from different sources, analyzes them, and makes an appropriate decision for the company. 


A data scientist is one who thinks beyond time and works on upcoming probable difficulties by predictive models and finds out calculative merits and demerits, and prepares a solution beforehand by using data visualisation tools and machine learning algorithms. He plays a pivotal role in providing critical information and recommending solutions for forthcoming issues. 


These are crucial jobs for the growth and success of a company. Both these roles can be confused over some overlapping parameters of analyzing: where one analyzes the historical records and the other predicts upcoming problems. The scope of both careers is nowhere to be stopped as machine learning has become a vital part of our daily life and we are living in the age of emerging technology.


Choosing one as a career can be a tricky part. The decision may lie in the vision of your career goals and your skills. Here are some detailed explanations of both careers.


Data Analysis

In simple 'word data' analysts analyze the data generated for improving business and its decision-making process by utilizing the existing information.  It is a process of collecting, inspecting, and transforming data into meaningful conclusions which helps in making decisions. All these can be done with the knowledge of statistics and problem-solving attitude. To choose data analytics as a career, one need not be from an engineering background, but one having statistical skills, database, modeling, and analyzing skills. These could add an advantage to learning and working efficiently.


In today's world of cooperative business, data analysis helps to make decisions in a scientific way to operate the business more effectively. Data means the information related to business (intelligence), statistical modeling, and technique. In statistical analysis, data analysis can be divided into:

  • EDA (exploratory data analysis)
  • CDA (confirmatory data analysis)

Job profiles seem fascinating to collect data for the benefit of the company, but there are different kinds of data to be analyzed like data related to marketing, media, sales, consumers, etc. It fulfills a specific purpose and can be beneficial to all kinds of industries like- IT, tourism, etc.


Roles data analysts play

  • To make a report in such a way that it should be convenient to understand to the end-user.
  • To make a report on the basis of analyzing information, to identify a particular pattern.
  • To collect a large amount of information to reach a final conclusion, data analysts have to undergo a chain of processes and link to a large group of people. A data analyst is expected to collaborate well with all the people around him.
  • Data analysts are expected to use powerful tools/software to handle and store data to save as a record and to modify strategies.

Titles for Data Analysts

Titles are decided on the basis of their skills like python, R, SQL programming, excel, google slides/presentation etc.

  • Junior analysts
  • Data analysts
  • Senior data analyst
  • Analytics manager
  • Director of analytics
  • Chief data officer
  • Business analysts
  • Financial analysts
  • Operational analysts
  • Marketing analyst
  • Health care analyst
  • Data analyst consultant 

Data Science

To find out the structural and non-structural insights from the data by using several methods and techniques to make an informed decision. A data scientist is a person who collects data and after performing several experiments of different modules- predicts the demerits and merits for the company. It involves several machines/tools and algorithms. One should be curious enough to see beyond the pattern and discover upcoming trends. One should be able to convert data into insights. Leading programming languages for data science-R, Python, Scala, etc.


Job profile

  • A scientific approach to data and analyzing it, to create solutions for upcoming outcomes of the company. It requires the knowledge of mathematics, probability, and statistics to make predictions and to create solutions. The key role is to combine the tools and algorithms to get the final outcome.
  • Data mining, data modeling, and reporting is the basic role of the data scientist. Problem-solving attitude with presentation skills.
  • It is generally fit for professionals like IT engineers, architects, etc.

Roles of data scientist

  • To collect authentic data from the sources.
  • To analyze statistically and create solutions.
  • Making strategies to counter upcoming problems.
  • Present information using tools.
  • Using techniques and technology to make strategic solutions.
  • Creating end-to-end solutions.
  • Updated with latest trends and tools.
  • Working with every department of a company from production to marketing.
  • Check feasibility of upcoming marketing strategies and business procedures and their outcomes.

Titles for Data Scientist

  • Junior data scientist
  • Senior data scientist
  • Data architect
  • Machine learning engineer
  • Database administrator

Choose your career wisely that matches your interest and skills which fulfills your life goals.

By Mithila Rathod

Also Read:

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