Data Science vs Data Analytics – Top 6 Differences

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Last updated on
28th Mar, 2022
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03rd Mar, 2022
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Data Science vs Data Analytics – Top 6 Differences

Data Science and Data Analytics have become the buzzword in technology lately. Today, the whole world contributes and generates massive amounts of data, and it is termed Big Data. According to the survey given by The World Economic Forum, it states that by the end of 2020, the data generation would reach 44 Zettabytes, and it is estimated that the data generated would reach 463 exabytes by the end of 2025. Every human being is responsible for generating data and data generation is of various forms like interaction, mobile communication, working with a laptop or desktop, etc., On average a normal human being uses 2.5 quintillion bytes of data per day. 

Most of the industries like the Automobile Industry, Software Industry, HealthCare, Retail Industry etc., deals with large amounts of data and makes use of the latest technologies like Data Science and Data Analytics. The Data Science with Python training helps you to know better about Data Science with real-world applications. Data has become a precious resource in today’s real world.   

The term Big Data includes every type of data like texts, images, emails, videos, chats on social media platforms, tweets, the search operations that we perform on the Search Engines like Google Chrome, Microsoft Edge, Mozilla etc., and the data generated from the connected devices, from IoT devices, and so on. In the Digital World, the data generated is very vast and complex.

A Quick Glance at Data Science vs Data Analytics:

Let us have a quick understanding of Data Science versus Data Analytics with a small table, as shown below.   


Data AnalystData Science
Educational Requirements
Bachelor's degree in any of the fields like Computer science, Statistics, finance or mathematics.Requires a Master’s or Doctoral Degree in any of the fields like Information Technology, Data Science, Statistics or Mathematics.
Working
Drawing insights, identifying patterns etc., from the data by performing some operations related to Analysis.Generates predictive models, Futuristic models from the data by observing previous data by applying some machine learning techniques.
Characteristics
Good background in the field of Mathematics and Statistics are required to make good decisions out of the given data.Good at mathematics, Statistics, Domain knowledge and Computer science are required.
Skills and Tools
  • Skills: - Mathematics, statistics, Analytical, Visualization, etc.,  
  • Tools: - Tableau, SAS, Power BI, Excel, SQL etc.,  
  • Programming Languages: - R, Python etc.,
  • Skills: - Probability, Advanced Statistics, Calculus, Linear Algebra, Predictive Analytics, etc.,  
  • Tools: - Hadoop, Spark, MYSQL etc.,  
  • Programming Languages: - R, Python, Scala, MATLAB, SQL, Julia etc.,  

Roles and Responsibilities
Examining Large Volumes of data to draw conclusions, identify patterns and representation of data in the pictorial format. Works with structured data.   Perform operations on the data using the advanced concepts like Machine Learning, Data Modelling, Custom Analysis, Predictive Modelling etc.,   
Applications
Transportation, Manufacturing, Health Industry, Military etc.,   Artificial Intelligence, Search Engines, Business Analytics, Finance etc.,   

Who Works with Data?

In the present scenario, the Data Scientists and Data Analysts work with data most of the time. The years 2020 and 2021 have seen rapid growth in the field of data and has seen a sharp rise in demand for Data Scientists and Analysts. These Data Scientists and Data Analysts help for the growth of an organization and improve businesses with the help of data. They draw insights from the data, help to recognize patterns, perform certain analysis techniques, and generate a model that predicts the outcomes from data for further use. At present most of the world is dependent on technologies like Data Science and Data Analytics to work with real-world data and to solve many problems using it. The roles of Data Scientists and Data Analysts are the highest paid jobs in 2021.    

An Overview of Data Science and Data Analytics 

The streams of Data Science and Data Analytics deal with data, and they are like two sides of the coin, having a different approach. Data Science is like a Big Ocean and Data Analytics is like a part of it. These streams help to gain insights from the data, used to draw patterns from it, moreover, they help to generate predictions for the future on the data. Is data science and data analytics the same...?? We can get an overview of those streams stated below: - 

Data Science Stages

Data Science is like the ultimate solution provider for a data problem. It is a collection of various technologies like Data Analytics, Machine Learning, Data Mining and many more. It can deal with both Structured and Unstructured Data. It is a concept of working with Big Data, which includes many steps like cleaning, organizing and analysis of data. The pillars of Data Science are domain knowledge, computer Science skills, mathematical skills, and communication skills. Data Science helps forecast and predict the results based on past data. It can help with the growth of an organization and can scale up the business to gain higher profits. To better understand the data science course and to have a real exposure to Data Science Platform, completing KnowledgeHut Data Science with Python training will help learners to know better about Data Science with real-world applications. A Data Scientist is a person who has a good grip on Statistical models, Machine Learning algorithms and has the capability to deal with any type of data to analyze it, understand it, and generate the best model out of the given data. Some of the common tasks performed by Data Scientists are:  

  • Collection, Cleaning, Analyzing and Processing of raw data  
  • Building Machine Learning Models out of big datasets  
  • Develops processes and tools for monitoring and analyzing data accuracy  
  • Build data Dashboards, Visualization tools and reports  
  • Programming on data to automate some of the processes  

Data Analytics refers to digging deeper into the data to understand it. This is the practice of Analyzing data to understand basic insights, to answer questions on data, and to identify the patterns of data with suitable types of techniques, tools, certain programming languages and different frameworks etc., A Data Analyst is a person who possesses better skills in the field of Statistics, has better visualization techniques, can work on various tools like SQL, Tableau etc., and is comfortable in working with programming languages like Python or R, and the more important thing is he should have better communication skills to describe the data. They work with structured data to deal with many complex business problems. Some of the common tasks handled by a Data Analyst are: -   

  • Identifying the problem and understanding it  
  • Collecting data from various sources  
  • Performing Basic operations like cleaning and structuring the data  
  • Analyzing the data to draw insights, identify patterns from data  
  • Presentation of data for better understanding and to make decisions out of it  

A Deep Dive into Data Science vs Data Analytics: 

Let us have a deep understanding of the difference between Data Science and Data Analytics based on some factors.  

  • Educational Requirements  
  • Working  
  • Skills and Tools  
  • Roles and Responsibilities  
  • Applications  

Educational Requirements:

Holding a Bachelor’s Degree in any of the fields like Computer Science, Statistics, finance, or mathematics can act as the upper hand to become a Data Analyst. Whereas the role of a Data Scientist is the topmost role in any industry and it requires a Master’s or Doctoral Degree in any of the fields like Information Technology, Data Science, Statistics, or Mathematics.   

Pursuing a degree would be beneficial to start our career in the field of data, it would act as the Initial Step towards our career. But there exists an alternative without pursuing a degree or any previous work experience one can get into the field of working with data by completing the certification courses provided by giant companies like Google or IBM. And there are many more alternative courses offered by many other companies like KnowledgeHut. Through this, students or professionals can get the basic idea and can lay down their path in the field of data by mastering it. 

Working: 

Both the streams of Data Science and Data Analytics work with data. A data analyst works with data to draw insights, identify patterns etc., by analyzing the given data by applying some of the math, statistic skills and should have better knowledge of tools and programming languages. He works only with structured data to perform operations.  

Data Scientist is the one who works with data to generate predictive models and futuristic models from the data by observing the past records. He should have better skills to generate machine learning models by analysing and performing some statistical operations on the data. He should have skills in dealing with both structured and unstructured data. Should possess visualization skills and have the capability to automate the model to find the solutions to the given problem. 

Characteristics: 

Data Analysts should have a good background in the field of Mathematics and statistics and should be capable of making good decisions on data. They try to draw meaningful insights from the data and draw patterns accordingly. They help to regulate businesses in the real world by implementing many data techniques. Pursuing the field as a data analyst can lay a path to becoming a Data Scientist.  

Data scientists should be good at Mathematical, statistical, domain and computer science knowledge to the maximum extent possible. They should have a better understanding of complex data, be capable of working with various types of machine learning algorithms, able to predict the results from the data.

Skills and Tools: 

Some of the things like tools, programming languages, good knowledge of Maths and statistics, domain knowledge and getting a good understanding of data is a must for both the streams of Data Science and Data Analytics. You can learn these skills by practicing on data science problems or taking data science online courses in India or any other location.  

A Data Analyst must have the following: - 

  • Good Capabilities on Excel and SQL database  
  • Should have foundational skills in mathematics and statistics  
  • Basic fluency in programming languages like R or Python  
  • Better understanding of tools like Tableau, SAS, Power BI etc.,  
  • Should possess better Analytical and Visualization skills  

A Data Scientist must have the following:  

  • Should have skills in Probability, Statistics, Calculus and Linear Algebra  
  • Proficient in Advanced statistics and Predictive Analytics  
  • Fluency in any of the programming languages like Python, R, Scala, MATLAB, SQL and Julia.  
  • Better Understanding of tools like Hadoop, Spark, MYSQL  
  • Good at technologies like data mining, data wrangling, machine learning etc.,

Roles and Responsibilities: 

Data Science and Data Analysts work with the data in different ways. The role of a Data Scientist is to use various Math, Statistical and Machine Learning skills to work with data in order to clean, analyze and interpret the data to draw insights from it. They can work with both structured and unstructured data and also on advanced concepts like machine learning, data modelling, custom analysis, predictive modelling etc.,  

A Data Analyst is responsible for examining the data to draw conclusions and identify patterns from data. They work with large volumes of data, clean it, organize it, and perform some analysis operations. After that, they try to represent the data in the pictorial format and work with the structured data.  

Responsibilities of a Data Analyst  

  • To Collect and Interpret data  
  • To gain basic insights and draw patterns from data  
  • Performs basic operations on data using SQL  
  • Analysis of data is done using some analytical tools like descriptive analytics, predictive analytics etc.,  
  • Usage of visualization like Tableau, Excel etc., for better understanding of data.  

Responsibilities of a Data Scientist 

  • To clean, process and validate the data  
  • Perform some operations on datasets like Exploratory Data Analysis  
  • Apply Data Mining techniques with the help of ETL pipelines  
  • Apply the suitable machine learning algorithm that better suits to deal with the given problem  
  • Code for the problem using ML libraries for automation  
  • Identify new trends in the data for making better predictions in business.  

Applications: 

The applications of Data Science are Artificial Intelligence, Search Engines, Health Industry, Business Analytics, Finance and many more  

Data Analytics is applied in fields like Transportation, Manufacturing, Health Industry, Military area etc., 

Factors to Consider While Choosing a Career Between Data Analytics and Data Science Career 

In order to choose the right career between Data Science and Data Analytics some of the following factors should be considered as follows: -

1. Personal background:

To be a Data Scientist or Data Analyst some of the basic information like Educational Details, Skills, type of technologies they knew, experience in the particular field etc.,   

2. Interests: 

Based on the data science skills you are more familiar with like mathematical, statistical or programming skills and solution development skills one can choose between the career of Data Science and Data Analyst.   

3. Salary:

Choosing a career might be the case with the amount of salary we require, in general, a Data Scientist is paid more compared to a Data Analyst. According to the Glassdoor website, the average salary of a Data Scientist is 9 Lac per annum and the Data Analyst is 6 Lac per annum.   

Conclusion  

In this blog, we have seen what data is, forms of data, who are the people working with data the most in the real world, and many more things. We have seen the overview of the Data Science vs Data Analytics roles and figured out the basic differences based on some of the context areas like Educational Requirements, working type, characteristics, Skills and Tools, Roles and Responsibilities, Applications and the choosing factors between the Data Science vs Data Analytics career by a person.   

Frequently Asked Questions (FAQs)

1. Which is better Data Science or Data Analytics...?

Both the streams work with Data, but the work nature varies. Data Science requires a high knowledge of various tools, algorithms, and programming languages to find solutions to the problem. The Data Analyst is responsible for bringing out the basic insights about the data and helps in decision making. The Data Analyst role is the basic building block for Data Science.   

2. What pays more Data Science or Data Analytics…...?

The Data Science Role is the highest paid of all the roles and the Data Analyst role is also a valuable role but pays less amount compared to Data Science.   

3. Is Data Analyst and Data Science the same?

Both the names are not the same, because Data Analyst works to find out the insights of data and help in decision making but Data Science helps to build the predictive model from the data to solve a business problem.   

Profile

Harsha Vardhan Garlapati

Blog Writer at KnowledgeHut

Harsha Vardhan Garlapati is a Data Science Enthusiast and loves working with data to draw meaningful insights from it and further convert those results and implement them in business growth. He is a final year undergraduate student and passionate about Data Science. He is a smart worker, passionate learner,  an Ice-Breaker and loves to participate in Hackathons to work on real time projects. He is a Toastmaster Member at S.R.K.R Toastmasters Club, a Public Speaker, a good Innovator and problem solver.