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Difference between Data Analytics and Data Science
This is an article in which we will look at the difference between data analytics and data science.
Data Science
Data science is a field in which structured and unstructured data are used to extract meaningful information with the help of different algorithms and scientific methods. One of its fields is artificial intelligence which has now become very popular. The subjects included in data science are mathematics, statistics, science, programming, and many more.
Importance of Data Science
There are many important factors related to data science and some of them are listed below −
- Insights are extracted from the data which help in making future plans and also making decisions
- Future trends can be predicted on the basis of the data
- New opportunities and areas for growth are identified
- Assessing risk factors
- Improvement in the accuracy of forecasting and predictions
Data Analytics
Data analytics is a process in which raw data is converted into useful information which is used by organizations to make different types of plans. The company can make useful decisions through data analytics. A large amount of data from various resources is compressed to make it useful for the organization.
Importance of Data Analytics
Data analytics has its own importance and some of them are listed below −
- Organizations use data analytics to convert raw data into useful information so that business goals can be achieved.
- The purchasing behaviors of customers can be easily understood.
- Data analytics lets the organization know about its position in the market.
Difference between Data Science and Data Analytics
There are many differences between data science and data analytics and we will look into them in detail.
| Data Science | Data Analytics |
|---|---|
| The programming languages used for data science include Python which is the major language. Other languages such as C++, Java, and Perl, etc. are also used in data science. | Python and R programming languages are used for data analytics. |
| Advanced programming skills are essential for data science. | Basic programming skills are required for data analytics |
| Insights can be gained with the help of machine learning. | Machine learning is not used to get data insight |
| Data mining activities are required to get meaningful information | Conclusions from the raw data can be extracted through Hadoop-based analysis |
| Large scope | Small scope |
| Deals with unstructured data | Deals with structured data |
| Maximum usage of statistical skills | Minimum usage of statistical skills |
| Data scientists use the data to make predictions and future plans | Data analysts solve problems related to the current trends. |
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Data analytics include −
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