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 Analytics vs Data Science Salary in India: 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. This makes for data science and data analytics difference.

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. This is another difference between data analysis and data science.

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. , and people often find themselves wondering Is data science and data analytics same?. 

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. This is another data analytics and data science difference.

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.

Data science can be stressful at times due to deadlines, complex problem solving requirements, and the need to continually learn new tools & techniques. But keep in mind that it offers rewarding challenges, opportunities for innovation and high salary.

Checkout: Data Science Skills

Is coding requires in data analytics?

Yes, coding is required in data analytics for tasks such as data manipulation, analysis, and visualization. Some of the common languages are Python, R, and SQL.

Data Science vs. Data Analytics Salary: 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.

The Data Analysis Process

The journey of data analysis encompasses several key steps:

Data Requirement Gathering: This initial phase of data analysis involves a meticulous understanding of why the analysis is being conducted and what specific types of data are required to achieve the intended goals.

It’s akin to creating a roadmap before embarking on a journey. This step involves delineating the purpose behind the analysis, defining the scope, and identifying the various categories or forms of data that will be most beneficial for the analysis process.

Whether it’s sales figures, customer demographics, or product performance metrics, this stage sets the foundation for acquiring the right data that aligns with the objectives.

Data Collection: Once the blueprint is set, the next pivotal stage is data collection. This process involves sourcing the identified data from diverse channels and platforms.

It includes a range of methods such as surveys, interviews, database extraction, web scraping, or utilizing APIs to gather relevant datasets. Here, the emphasis lies not only on obtaining data but also on ensuring its quality, relevance, and accuracy.

Data collected should ideally cover all necessary aspects outlined in the requirement-gathering phase and should be organized in a manner conducive to subsequent analysis.

Data Cleaning: Raw data is often riddled with imperfections and inconsistencies. Data cleaning, also known as data cleansing or data scrubbing, is the process of refining the collected data to ensure its accuracy and consistency.

This phase involves identifying and rectifying errors, removing duplicate entries, dealing with missing or irrelevant data points, standardizing formats, and addressing any other anomalies that might distort or hinder the analysis process.

The goal is to prepare a clean and robust dataset, free from redundancies or inaccuracies, forming a solid foundation for subsequent analysis steps.

Data Analysis: This phase involves leveraging a myriad of tools and software specifically designed for interpreting and comprehending the collected datasets.

Utilizing tools such as Excel, Python, R, Tableau, or Power BI, data analysts dig into the dataset, applying statistical, mathematical, or computational techniques to uncover patterns, trends, correlations, or anomalies within the data.

The goal is to extract meaningful insights that support decision-making processes and help achieve the predefined objectives outlined during the initial stages.

Data Interpretation: Once the data is rigorously analyzed, the next critical step is interpreting the findings. Data interpretation involves making sense of the analyzed information, drawing logical conclusions, and deriving actionable insights from the patterns or trends identified during the analysis phase.

Analysts use their expertise to decipher what the data signifies, translating it into meaningful recommendations or strategies that businesses or stakeholders can implement to address challenges or capitalize on opportunities.

Data Visualization: Communicating complex data insights in an understandable and compelling manner is the essence of data visualization. This process entails transforming the analyzed data into visual representations like charts, graphs, maps, or infographics.

These visuals serve as powerful tools for elucidating intricate patterns or trends, making it easier for stakeholders or decision-makers to grasp the insights swiftly.

Through visualization, data analysts present compelling narratives and compellingly showcase the significance of the analyzed data, enhancing its impact and aiding in effective decision-making processes.

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.

Is Data Analytics harder than data science?

Data science is generally considered harder than data analytics because it involves a broader range of tasks, including advanced statistical modeling and machine learning.

Difference between data science and data analytics with example in tabular form –

Data Science	Data Analytics
Skillsets include- Data Modelling Predictive Analytics Advanced Statistics Engineering Programming	Skillsets include- BI Tools Statistics SQL Programming
The scope is macro.	The scope is micro.
The goals are to discover new questions and their answers.	The goals are to use the existing information and take appropriate steps.

Why is data science important?

The primary data science vs data analytics difference would is that data is the new oil used in almost every industry today. The data is meaningless unless it is made meaningful. Data science functions to mine a huge amount of data containing all types of data, from unstructured to structured data.

The importance of data lies in its ability to make sense of the information given. Well-put data allows the users to identify and make the best out of the available information; this is another significant difference between data analytics and data science.

Data science allows the business to measure, track, and record the metrics and development in the process. This process enables individuals to make practical and rational decisions about their business. This is also a  difference between data science and data analytics. 

The organisational importance of data science is ever-increasing. There are various applications of data science, and it is relevant in almost every industry today, from healthcare, finance, banking, retail, entertainment, education and so on.

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.

The key industries where data science and data analytics or data analysis vs data science find significant application:

Data science vs data analytics are important across various industries, playing a pivotal role in leveraging insights from data to drive decision-making and innovation. Some key industries where data science vs data analytics fields find substantial application include

Healthcare: From patient diagnostics to personalized treatment plans, data science aids in analyzing medical records, imaging data, and genomic sequences, while data analytics assists in optimizing hospital operations and resource allocation.

Finance: Both data science and analytics are extensively used in the banking and financial sectors for fraud detection, risk assessment, algorithmic trading, and customer segmentation, enabling better financial decision-making.

Retail: Data science helps in forecasting demands, optimizing inventory, and personalized customer experiences, while data analytics aids in market segmentation and analyzing customer purchasing patterns.

Telecommunications: Both data science and analytics is utilized for network optimization, predictive maintenance, and customer churn prediction improving service quality and understanding user behavior.

Marketing and Advertising: Both data science and data analytics contribute significantly by analyzing customer preferences, optimizing ad campaigns, and delivering personalized marketing strategies based on data-driven insights.

Manufacturing and Supply Chain: Data science optimizes production processes, predictive maintenance, and supply chain management, while data analytics aids in inventory optimization and improving operational efficiency.

Here are the difference between data science and data analytics with example. To conclude, even though data science and business 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.

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