数据科学：数据科学是一个通过应用各种算法、流程、科学方法从结构化和非结构化数据中提取有意义的信息和见解的领域。该领域与大数据相关，是目前最需要的技能之一。
数据科学包括数学、计算、统计、编程等，以从以各种格式提供的大量数据中获得有意义的见解。
数据分析：数据分析用于通过处理原始数据来得出结论。它对各种业务都有帮助，因为它可以帮助公司根据数据得出的结论做出决策。基本上，数据分析有助于将大量数据形式的数字转换为简单的英语，即进一步有助于做出决策的结论。
下表列出了数据科学和数据分析之间的差异：
| 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 doesn’t makes use of machine learning. | |||||||||
| 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 | The statistical skills are necessary in the field of Data Science.. | The statistical skills are of minimal or no use in data analytics. |