Difference Between Data Science and Data Engineering

Data Science: The detailed study of the flow of information from the data present in an organization’s repository is called Data Science. Data Science is about obtaining meaningful insights from raw and unstructured data by applying analytical, programming, and business skills.

Data Science life cycle includes:

  1. Data Discovery: Searching for different sources of data and capturing structured and unstructured data.
  2. Data Preparation: Converting data into a common format.
  3. Mathematical model: Using variables and equations to establish a relationship.
  4. Getting things in action: Gathering information and deriving outcomes based on business requirements.
  5. Communication: Communicating findings to decision-makers.

Data engineering: Data engineering focus on the applications and harvesting of big data. Data engineering focuses on practical applications of data collection and analysis. In this data is transformed into a useful format for analysis. Data engineering is very similar to software engineering in many ways. Beginning with a concrete goal, data engineers are tasked with putting together functional systems to realize that goal.


Below is a table of differences between Data Science and Data Engineering:

S.No. Data Science Data Engineering
1. Develop, construct, test, and maintain architectures (such as databases and large-scale processing systems) Cleans and Organizes (big)data. Performs descriptive statistics and analysis to develop insights, build models and solve business need.
2. SAP, Oracle, Cassandra, MySQL, Redis, Riak, PostgreSQL, MongoDB, neo4j, Hive, and Sqoop. Scala, Java, and C#. SPSS, R, Python, SAS, Stata and Julia to build models. Scala, Java, and C#.
3. Ensure architecture will support the requirements of the business Leverage large volumes of data from internal and external sources to answer that business
4. Discover opportunities for data acquisition Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
5. Develop data set processes for data modeling, mining and production Explore and examine data to find hidden patterns
6. Employ a variety of languages and tools (e.g. scripting languages) to marry systems together Automate work through the use of predictive and prescriptive analytics
7. Recommend ways to improve data reliability, efficiency and quality Communicating findings to decision makers
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