Major Difference Between Data Science and Data Analytics

The gradual advent and upsurge of Big Data in the world economy have brought buzzwords in the Industry, that is Data Analytics and Data Science. Big data includes everything like emails, taxes, tweets, social media chats, user searches, data generated from connected devices and IoT, and basically everything that we are doing online.

The data which is generated on an everyday basis through the digital world is so complex and huge that the traditional data analysis and processing systems cannot handle it alone. Thereby, enters Data Analytics and Data Science. Data Analytics and Data Sciences are emerging technologies and a still revolving stage, that often confuse people. This blog covers the major data science and data analytics differences, its career prospects, which one is a better option, why one should study these courses, and other aspects of the Data Science and Data Analytics course.

Is Data Science and Data Analytics Same?

In the world of big data, there is a high requirement of having organized optimally structured, and analysed data in order to create or make something useful out of it. Both data science and Data Analytics have their own role to play in the world of big data.

Data science is a combination of various disciplines including mathematics, computer science, statistics, information science, Artificial Intelligence, and machine learning. It also includes the concepts of data mining, predictive modelling, data interference, and other machine learning algorithms through which patterns are extracted from complex data sets and then transform into actionable business strategies or insights. Whereas Data Analytics is only concerned with mathematics, statistics, and statistical analysis.

While data science mainly focuses on finding only meaningful correlations between the large data sets, Data Analytics is specifically designed to bring out the specifics of the extracted insights. In other words, Data Analytics can be considered as a branch of data science that specifically focuses on answers to the questions which data science brings forth. Let’s check out some of the other major differences between Data Science and Data Analytics in the next section.

Difference Between Data Science and Data Analytics

The job roles of data analyst and data scientist vary in degrees of data collection and analysis, cleaning, and gaining actionable insights for a data-driven decision-making process. Henceforth, the responsibilities of a data analyst and a data scientist are different. Let’s check out the key differences with few critical metrics:

Data Science Vs Data Analytics: Meaning

In the field of data science, a person works in provision with different scientific methods and processes especially with the algorithms and with the intention of extracting knowledge and insights about a particular subject objective. Data science relates to data mining. It brings together the concept of statistics, data analysis, and machine learning altogether.

Data analytics, whereas an entire science behind analyzing raw data of any form with the intention of making predictions on conclusions. It can fetch or capture necessary information like trends and metrics which could be otherwise lost in the massive pool of data and other information. Businesses and Enterprises use this technology for optimizing their operational processes to ensure productivity and efficiency, thus turning up the entire revenue setup.

Data Science Vs Data Analytics: Scope

The scope of data science is vast as the data science graduates can apply for jobs in various sectors including e-commerce, manufacturing, banking and finance, healthcare, transport and work as big data engineer, machine learning engineer, data scientist, data engineer, statistician, business analyst, data analyst and other designations.

As per expert research, India is leading in the huge data analytics market. The reason behind this is the change that the country is going through, as data around us is bolstering at a rapid rate. Currently, it has become very crucial for most businesses to hire big data professional for valuable information insights. Further, the data analyst graduates can work on industries including manufacturing, banking, Healthcare, Information Technology and others.

 Data Science Vs Data Analytics: Fundamental Goal

The purpose and fundamental goal of data science is to identify and find patterns. Understanding these patterns means understanding the world. Right from a mechanic fixing a car to a scientist doing research breakthroughs, identifying a pattern is the first step towards progress. Whereas, the goal of Data Analytics is to apply statistical technologies and analysis on data for finding trends and solving problems.

Data Analytics Vs Data Science: Salary

Data science is an extremely lucrative career option. The path towards success is long because a career in data science requires a lot of sound technical knowledge along with patience and the end is actually rewarding. The average salary for any experienced data scientist is up to 10 lacs per annum and it can go up to 20 lacs per annum depending on the skills, experience, and educational background.

A Data analyst’s career is slightly inferior to data science and an individual with no experience can fetch a salary of around 5 lacs per annum. However, it goes up to 10 lacs per annum with skills, experience, and educational background.

 Data Analytics Vs Data Science: Key Skills

Every aspiring data scientist and data analyst should have key skills to get the desired job. Let’s check them out one by one.

Here are the skillsets required for data scientists:

  • Data Mining
  • SQL, Python & R
  • Machine Learning Modelling
  • Statistical Analysis
  • Text Analytics & Natural Language Processing
  • Neural Network (Deep Learning)

Check out the skillsets required for data analyst:

  • R & Python
  • Excel, SQL & Tableau/ QlikView/ PowerBI
  • Data Manipulation
  • Basic Arithmetic & Statistics
  • Data Reporting & Visualization
  • Statistical Analysis

Data Analytics Vs Data Science: Job Profiles

For being a data scientist, a candidate must love using various business intelligence tool, can model multidimensional data sets, and can easily partner with business leaders for answering key business questions. The candidate would also be responsible for maintaining the ongoing reports, metrics, analysis, dashboard etc, in order to drive key business decisions.

As a data analyst, a candidate would be responsible for cleansing, processing and verifying the integrity of data, gleaning business insights through the utilization of machine learning algorithms and techniques along with identifying new trends in data for making future predictions. They should also know the best utilization of the AI services and AWS cloud platform.

Read more: Top Data Science Jobs for Freshers and Experienced 2022

Data Analytics Vs Data Science: Education Required

A Data Science graduate for securing a good job in any multinational company must have a Masters’ or equivalent degree in Computer Science, Statistics, Computer Engineering, Mathematics, or any other related technical discipline. They must also possess 3+ years of professional experience in software development in language is such as Python, Java, and Scala. Candidates who have project-based learning and hands-on experience in Computer Engineering, Computer Science, Mathematics are often given the first preference.

A data analyst graduate must have cleared 3+ years graduation course in any discipline including Computer science, Business information systems, Economics, Mathematics, Information Management, or Statistics. Candidates must have higher proficiency in Microsoft Office and other Windows-based applications. Having SQL knowledge is a must-have requirement.

Data Science Vs Data Analytics: Roles and Responsibilities

A data scientist works closely with different business stakeholders for understanding their goals and determining how data can be utilized for achieving those goals. The design data modelling processes, predictive models and creating algorithms for extracting data for business needs and then analyzing the data and sharing insights with a full stop while each project is different for them the process of gathering and analyzing data and other information generally follows the same path.

Data analyst roles and responsibilities include analyzing data by utilizing statistical techniques and then providing reports. They also implement and develop databases and other data collection systems. Their job role is also to acquire data from various secondary and primary sources and maintain the data systems. They identify, analyze and interpret patterns and trends in complex data sets.

Read more: The Role of Data Scientist in 2024

Data Science or Data Analytics – Which Is Better?

Although there are a lot of differences between Data Science and Data Analytics both are rewarding career options. Both data scientists and data analysts are the most in-demand job roles in both India and abroad. In terms of a career fit, the data science course is good for those professionals who have one to ten years of experience and have the desire to learn extensive Python programming for the successful execution of data science projects.

On the other hand, Data Analytics courses are for those professionals who have two to five years of experience and are looking to build a career as a data warehouse expert. Therefore, when it comes to answering the question ‘which is the right career option’ then obviously there is no proper answer. It is totally up to the students’ choice.

Data Science or Data Analytics — Which One Should I Choose?

Businesses are now currently seeing huge growth and profit with the help of insights that they obtain from data. This is the main reason why there is a huge demand and increases in job opportunities for data analysts, data scientists, and data engineers in different business organizations. Whereas, if you are looking in terms of salary, then data science can be a better option than data analyst because the pay scale is higher for the formal designation. The choice between data science or data analytics largely depends on the candidate’s career goals, skill sets, and careergoals.