Difference Between Data Science and Data Analytics
There is a significant difference between Data Science and Data Analytics. We will see them one by one for each feature.
Feature | Data Science | Data Analytics |
---|---|---|
Coding Language | Python is the most commonly used language for data science along with the use of other languages such as C++, Java, Perl, etc. | The Knowledge of Python and R Language is essential for Data Analytics. |
Programming Skills | In-depth knowledge of programming is required for data science. | Basic Programming skills is necessary for data analytics. |
Use of Machine Learning | Data Science makes use of machine learning algorithms to get insights. | Data Analytics does not use machine learning to get the insight of data. |
Other Skills | Data Science makes use of Data mining activities for getting meaningful insights. | Hadoop Based analysis is used for getting conclusions from raw data. |
Scope | The scope of data science is large. | The Scope of data analysis is micro i.e., small. |
Goals | Data science deals with explorations and new innovations. | Data Analysis makes use of existing resources. |
Data Type | Data Science mostly deals with unstructured data. | Data Analytics deals with structured data. |
Statistical Skills | Statistical skills are necessary in the field of Data Science.. | The statistical skills are of minimal or no use in data analytics. |
Data Science vs Data Analytics
In this article, we will discuss the differences between the two most demanded fields in Artificial intelligence that is data science, and data analytics.