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Data Science Vs Data Analytics: Difference Between Data Science and Data Analytics

Summary:

In this article, you will learn,

  1. Difference between Data Science and Data Analytics
  2. Job roles
  3. Skills
  4. Career perspectives
  5. Which one is right for you.

Read more to know each in detail.

The upsurge of Big Data has brought along two other buzzwords in the industry, Data Science and Data Analytics. Today, the whole world contributes to massive data growth in colossal volumes, hence the name, Big Data. The World Economic Forum states that by the end of 2020, the daily global data generation will reach 44 zettabytes. By 2025, this number will reach 463 exabytes of data!

Big Data includes everything – texts, emails, tweets, user searches (on search engines), social media chatter, data generated from IoT and connected devices – basically, everything we do online. The data generated every day via the digital world is so vast and complex that traditional data processing and analysis systems cannot handle it. Enter Data Science and Data Analytics. 

Since Big Data, Data Science, and Data Analytics are emerging technologies (they’re still evolving), we often use Data Science and Data Analytics interchangeably. The confusion primarily arises from the fact that both Data Scientists and Data Analysts work with Big Data. Even so, the difference between Data Analyst and Data Scientist is stark, fuelling the Data Science vs. Data Analytics debate. 

In this article, we’ll address the Data Science vs. Data Analytics debate, focusing on the difference between the Data Analyst and Data Scientist.

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Data Science vs. Data Analytics: Two sides of the same coin

Data Science and Data Analytics deal with Big Data, each taking a unique approach. Data Science is an umbrella that encompasses Data Analytics. Data Science is a combination of multiple disciplines – Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence.

It includes concepts like data mining, data inference, predictive modeling, and ML algorithm development, to extract patterns from complex datasets and transform them into actionable business strategies. On the other hand, data analytics is mainly concerned with Statistics, Mathematics, and Statistical Analysis. 

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While Data Science focuses on finding meaningful correlations between large datasets, Data Analytics is designed to uncover the specifics of extracted insights. In other words, Data Analytics is a branch of Data Science that focuses on more specific answers to the questions that Data Science brings forth. 

Data Science seeks to discover new and unique questions that can drive business innovation. In contrast, Data Analysis aims to find solutions to these questions and determine how they can be implemented within an organization to foster data-driven innovation. 

Read: Career in data science and its Scope.

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Data Science vs. Data Analytics: Job roles of Data Scientist and Data Analyst

Data Scientists and Data Analysts utilize data in different ways. Data Scientists use a combination of Mathematical, Statistical, and Machine Learning techniques to clean, process, and interpret data to extract insights from it. They design advanced data modeling processes using prototypes, ML algorithms, predictive models, and custom analysis. 

While data analysts examine data sets to identify trends and draw conclusions, Data Analysts collect large volumes of data, organize it, and analyze it to identify relevant patterns. After the analysis part is done, they strive to present their findings through data visualization methods like charts, graphs, etc. Thus, Data Analysts transform the complex insights into business-savvy language that both technical and non-technical members of an organization can understand. 

Both the roles perform varying degrees of data collection, cleaning, and analysis to gain actionable insights for data-driven decision making. Hence, the responsibilities of Data Scientists and Data Analysts often overlap. 

Responsibilities of Data Scientists

  • To process, clean, and validate the integrity of data.
  • To perform Exploratory Data Analysis on large datasets. 
  • To perform data mining by creating ETL pipelines. 
  • To perform statistical analysis using ML algorithms like logistic regression, KNN, Random Forest, Decision Trees, etc.
  • To write code for automation and build resourceful ML libraries
  • To glean business insights using ML tools and algorithms.
  • To identify new trends in data for making business predictions.

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Responsibilities of Data Analysts

  • To collect and interpret data.
  • To identify relevant patterns in a dataset. 
  • To perform data querying using SQL.
  • To experiment with different analytical tools like predictive analytics, prescriptive analytics, descriptive analytics, and diagnostic analytics.
  • To use data visualization tools like Tableau, IBM Cognos Analytics, etc., for presenting the extracted information. 

Read: Career in Data Science

Data Science vs. Data Analytics: Core Skills 

Data Scientists must be proficient in Mathematics and statistics and expertise in programming (Python, R, SQL), Predictive Modelling, and Machine Learning. Data Analysts must be skilled in data mining, data modeling, data warehousing, data analysis, statistical analysis, and database management & visualization. Data Scientists and Data Analysts must be excellent problem solvers and critical thinkers. 

A Data Analyst must be:

  • Well-versed in Excel and SQL database.
  • Proficient in using tools like SAS, Tableau, Power BI, to name a few. 
  • Proficient in R or Python programming. 
  • Adept in data visualization.

A Data Scientist must be:

  • Well-versed in Probability & Statistics and Multivariate Calculus & Linear Algebra. 
  • Proficient in programming in R, Python, Java, Scala, Julia, SQL, and MATLAB.
  • Adept in database management, data wrangling, and Machine Learning.
  • Experienced in using Big Data platforms like Apache Spark, Hadoop, etc.

Checkout: Data Science Skills

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Data Science vs. Data Analytics: Career Perspective 

The career pathway for Data Science and Data Analytics is quite similar. Data Science aspirants must have a strong educational foundation in Computer Science, or Software Engineering, or Data Science. Similarly, Data Analysts can pursue an undergraduate degree in Computer Science, or Information Technology, or Mathematics, or Statistics.

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Data Science vs. Data Analytics: Which One is Right For You?

Typically, Data scientists are much more technical, requiring a mathematical mindset, and Data Analysts take on a statistical and analytical approach. From a career perspective, the role of a Data Analyst is more of an entry-level position. Aspirants with a strong background in statistics and programming can bag Data Analyst jobs in companies.

Usually, when hiring Data Analysts, recruiters prefer candidates who have 2-5 years of industry experience. On the contrary, Data Scientists are seasoned experts having more than ten years of experience. 

When talking about the salary, both Data Science and Data Analytics pay extremely well. The average salary of Data Scientists in India ranges between Rs. 8,13,500 – 9,00,000, while that of a Data Analyst is Rs. 4,24,400 – 5,04,000. And the best part about choosing to build a career in Data Science or Data Analytics is that their career trajectory is positive, continually scaling up. Read more on data scientist salary in India.

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Here are the differences between data science and data analytics. To conclude, even though Data Science and Data Analytics tread on similar lines, here’s a fair share of differences between Data Analyst and Data Scientist job roles. And the choice between these two largely depends on your interests and career goals.

If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Executive PG Programme in Data Science.

Which is better – Data Science or Data Analytics?

Businesses are seeing huge profits and growth with the help of insights obtained from the data available in the organization. This is the main reason why there is a huge increment in the number of job opportunities for data scientists, data analysts, and data engineers in every organization.

Data has become the most crucial element of every organization. Data Science is useful for analyzing raw and unstructured datasets to find actionable insights. This field focuses on finding answers to questions that the company doesn't know about. Data scientists make use of different methods and tools to obtain the answers.

Data Analytics processes the available datasets and performs different statistical analysis to obtain actionable insights from them. It focuses on solving the current business problems from the data available by presenting the information in a visual format that becomes easy to understand for every individual. On top of that, data analytics focuses on coming up with results that can provide immediate improvements.

Both Data Science and Data Analytics have a huge demand in the market. Whether you look at it from the scope point of view or salary, both of them are great options.

Can a data analyst work as a data scientist?

Both the fields work with data over here. There is a requirement for a bachelor's degree in both fields. Once you have become a data analyst, you can go on to become a data scientist by advancing more on programming and mathematical skills. You need to be very clear with math and programming concepts to work as a data scientist. Other than that, you also need to get an advanced degree to begin as a data scientist.

Is it necessary for data analysts to be excellent with math?

Data Analysts need to be good with numbers along with possessing a fundamental knowledge of different math and statistics concepts. But, it is not necessary even if you are a bit low on this knowledge. Data Analysis is more about following a set of logical steps. You can clear out the basics of required mathematical concepts to get better at data analysis. Other than that, it is not necessary for you to be very good at math to become a data analyst.

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