Difference between Data Science and Data Analytics
Table of contents
- Introduction
- What is data science?
- What are data analytics?
- Difference between data science and data analytics?
- Conclusion
- FAQs
Introduction:
Data is the new gold in this epoch. Every company is standing because it is relying on this data that is being generated and consumed daily. We have spun all our lives around the internet and data in such a way that it has become impossible for us to think of our lives without these technological marvels. Be it social media apps like Instagram and YouTube or official working apps like Microsoft Excel and Microsoft Word. Every little thing that we need today is made easily available to us via the Internet. There is an app for all our requirements. We can count on our apps more than we can count on our fellow humans these days.
After all this data is generated, where does it go? Today, we are here to discuss the difference between data science and data analytics. To draw a clear line between the two as these are two of the most interchangeable terms in the market. So, let us start with the topic without wasting any time.
What is data science?
Data science is a complex amalgamation of different fields like computer science, data management, statistics and machine learning. It is more enhanced than data analytics as it requires much more clarity in sorting and cleaning the data. The following are some characteristics of data science:
- Predictive Modelling: One of the most important jobs for data scientists is to predict future trends using old data.
- Machine Learning: Machine Learning plays a pivotal role in developing mechanisms to enhance a company’s overall performance.
- Statistical Analysis: Sometimes when data scientists need, they utilise advanced statistical techniques to uncover various data-based relationships.
What is data analytics?
As discussed above, data science is a vast subject and includes other fields as well, on the other hand, data analytics is much smaller in work range and helps in concluding the data given. Businesses mainly use it to gain deep insights from the numbers presented to them. Also, it is a great tool for creating visually appealing data sets.
Differences between data science and data analytics:
| Aspect | Data science | Data analytics |
| Data Lifecycle | Covers the entire data lifecycle | Often focused on analysis and reporting |
| Data Volume | Deals with both small and big data | Typically deals with structured data |
| Expertise Required | Strong technical and domain knowledge required | Strong domain knowledge is often sufficient |
| Techniques | Advanced statistical analysis, machine learning | Statistical analysis, data visualization |
| Goal | Predictive and prescriptive insights | Descriptive insights for decision-making |
Conclusion:
Since the advent of data-driven technology, the world has never turned back again. We have time and time proved how valuable even a single Mb of data can be. Until something topples data, it is going to remain at the top position in the race of being relevant. We cannot imagine our lives without data now. All of this has happened in a matter of 2 decades.
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FAQs:
Q. Is data science different from data analytics?
Yes, in some ways that are mentioned above, data science is from data analytics.
Q. Is a course in data analytics worth it?
Yes, many companies are looking for specialists in this field.
Q. Can I join this course after college hours?
Yes, you can join an evening batch if that is comfortable for you.
Q. Is this course available offline as well?
Yes, all our courses are available both online and offline.
Q. What is the duration of the data science course?
However, the duration of the course depends on the needs and requirements of the students.