Both data analytics and data science deal with various information. However, the main difference in both processes is what they do with the data sets.
Big data is slowly becoming a big name in terms of the outsourcing industry – granted that businesses are now able to collect and store loads of data on a daily basis. Technological advancements these days have helped organizations to turn their collected information into something that is valuable and insightful for their operations.
Along with data being an emerging trend in the market world comes various terminologies that people should be aware of. Part of the innovations in the Business Process Outsourcing (BPO) industry are the use of data analytics and data science. While a lot of people tend to use these words interchangeably, the reality is, they are different from one another.
What is data analytics?
Data analytics deals with examining and analyzing serieses of raw data to come up with specific conclusions. This process is being utilized in various industries to help make informed decisions in a company and confirm or disapprove certain theories. It is also used to determine new trends that may arise in the market industry.
Data analytics procedure makes use of different mechanical and algorithmic processes in different data sets to arrive at much meaningful results.
Roles of a data analyst
Data analysts’ job responsibilities may vary depending on the company and its type of industry. However, data analysts generally utilize raw data sets to solve problems and come up with valuable conclusions. More so, data analysts roles include the following:
- Data cleansing
- Exploratory data analysis
- Determine new trends and patterns by using different statistical tools
- Develop Key Performance Indicators (KPIs)
- Design and maintain data systems and database
- Prepare visual reports
What is data science?
On the other hand, data science is a field that deals with both structured and unstructured data. It is also known as data-driven science.
Unlike data analytics, its procedure involves a mixture of mathematics, programming, statistics, problem solving, and data collection. Data science processes basically include everything that is related to data cleansing, preparation, and data analysis.
Roles of a data scientist
Data scientists are usually assigned to develop predictive models and algorithms to be used by organizations. These people are able to organize undefined data sets either by using software tools or by creating their own automation systems and frameworks.
Further, part of the roles and responsibilities of a data scientist are the following:
- Exploratory data analysis
- Process, cleanse, and verify data
- Obtaining results and conclusions via machine learning and algorithms
- Predict new trends
Both data analytics and data science deal with various information. However, the main difference in both processes is what they do with the data sets.
Data analysts work with data to determine trends, solve problems, and create visual representations to help organizations make better decisions. On the other hand, data scientists develop and design processes for data modeling and productions. They make use of various predictive models, prototypes, algorithms, and custom analysis in the developmental process.
