Did you know that people across the world generate 2.5 quintillion bytes of data every single day as of 2022, where one quintillion bytes equals one exabyte? The World Economic Forum states that by 2025, this number can touch 463 exabytes! How does one make sense of such massive volumes of data? This is where data science and analytics come into the limelight. Data analytics is the process of analysing data sets to find trends and gain insights that can be valuable across industries. Data science uses scientific methods, algorithms and processes to extract valuable information from structured, semi-structured and unstructured data.
Data science aspirants can consider taking up GeekLurn’s Data Science Architect Program, which offers a 100% placement guarantee. The course includes plenty of tech talks, webinars, data science meetups and powerful tools and resources to understand data science vs data analytics and achieve the learning goals. You can expect well-structured modules, which are ideal for anyone looking to kick start their career in these fields. The 24-month program will make you eligible for the role of a data analyst, analytics manager and data scientist.
But first, you need to know the difference between data science and data analytics to pick the right career for yourself.
Table of Contents
What is Data Science?
There are 5 stages to data science.
| Stages | What They Include |
| Capture | Data Acquisition, Data Entry, Signal Reception and Data Extraction |
| Process | Data Mining, Clustering/Classification, Data Modelling and Data Summarization |
| Maintain | Data Architecture, Data Warehousing, Data Cleansing, Data Staging and Data Processing |
| Communicate | Data Reporting, Data Visualisation, Decision Making and Business Intelligence |
| Analyse | Exploratory, Predictive, Regression, Text Mining and Qualitative |
The field of data science combines domain expertise and knowledge of math and statistics. It then applies machine learning algorithms to texts, images, video and audio to produce AI systems. These perform tasks that need human intelligence while the systems generate insights that analysts may use to translate into tangible business value.
The main duties of a data scientist are preparing data for analysis, exploring, analysing, visualising, building models with R and Python, and deploying models as applications. They also develop hypotheses, analyse market trends and draw inferences. Other basic roles and responsibilities include using various reporting tools and analytics to detect patterns in data sets. All of these can help organisations make better decisions.
What is Data Analytics?
This is the process of analysing raw data to draw conclusions. It can help businesses optimise their performance, maximise profit, perform efficiently and ensure strategically-guided decisions. There are different types of analytics, such as descriptive, predictive and prescriptive analytics. A data analyst uses different software tools like data mining programs, data visualisation and spreadsheets for such analyses.
A data analyst works in sync with local, national and global trends for designing and preparing reports, using statistical tools and maintaining data systems. They also collaborate with data engineers and organisation authorities to identify opportunities for improvements and build policies for data governance. The main tools they use are Microsoft Excel, SQL, SAS, Tableau and Google Sheets.
The Difference Between Data Science and Analytics
Data science is a broad term for the fields that extract valuable information from large data sets. Data analytics is a part of this bigger picture. Here are a few basic points that differentiate data science from data analytics:
| Data Analytics | Data Science |
| Data analytics is focused on answering questions based on actionable data | Data science is not meant to solve queries but to wade through chunks of data to expose insights |
| A data analyst must be well-versed in Excel and SQL databases | A data scientist must be well-versed in probability & statistics, calculus and linear algebra |
| Major fields of data analytics are healthcare, gaming, travel and industries with immediate data needs | The major fields of data science are Machine Learning, AI, search engineering and corporate analytics |
| Data analytics simply converts figures into plain English using automation, statistical analysis, business intelligence and ETL tools | Data science applies various processes, algorithms and scientific methods from both structured and unstructured data |
| Data analytics is based on finding ways to analyse and model data | Data science is more concerned about asking questions |
But it is vital to know that their functions are highly interconnected and are two sides of the same coin. By adding data analytics to the data science mix, insights can be turned into practical applications for businesses.
Data Science vs Data Analytics: Job Roles And Responsibilities
In your quest to understand data analytics vs data science, let’s also get to know the job profiles of both.
Roles And Responsibilities Of A Data Analyst
- Develop and implement databases and data collection systems
- Acquire data from primary and secondary sources
- Filter and cleanse the data and analyse it using statistical techniques
- Maintain data systems
- Identify trends and patterns in complex datasets
- Work with business heads to identify and prioritise business needs
Roles And Responsibilities Of A Data Scientist
- Automate data collection processes after identifying reliable data sources
- Pre-process structured and unstructured data
- Develop machine-learning algorithms and build predictive models
- Use data visualization techniques to present information in an easy-to-understand manner to stakeholders
- Give solutions and develop strategies to tackle business challenges
While the average salary of a data analyst in India is 6 lakhs per annum, the average salary of a data scientist is 11 lakhs per annum. You can start your job as a Data Analyst while being a fresher. On the other hand, Data Scientists are experts in their field and generally have over 10 years of relevant experience. Both these fields tread on similar lines. Yet a clear understanding of data science vs data analytics will help you choose your career as per your interests.
Neel Neeraj
Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
