What is the difference between Data Science and Data Analytics?

In simple words, data science can be defined as a field that concentrates on obtaining appropriate answers from massive raw and structured data. It is a general concept used for data preparation and analysis. The general task that a data scientist performs is to gather data from multiple sources and with the application of machine learning extracting critical information from the collected data sets. They interpret the collected data from a professional point and then provide detailed and accurate predictions that can be used to make business decisions. This can be seen in the incorporation of multiple disciplines, which includes data analytics, data engineering, machine learning, predictive analytics, and data analytics. It includes retrieval, collection, ingestion, and transformation of large amounts of data, collectively known as big data.

Data science is active in charge of shaping big data, searching and advising decision-makers to bring in the changes effectively, which suits best. In accordance, Data analytics and machine learning are two of the many tools that are used by data science.

Talking about data science, it is the study which represents where the data has come from and how it can be converted into a valuable resource. Data science can be rightly regarded as about uncovering data findings through a number of process, techniques, and tools that are involved in identifying the patterns from raw data. The raw data is basically a big data in structured data form, along with semi-structured and unstructured data. Data Analytics, or data analysis, is pretty much similar to data science, but in a more intensive way. However, many people use these terms convertibly, data science and data analytics are unique fields, with the significant difference being the scope. Although the differences do exist, both data science and data analytics will play a unique and essential part of the future of work and data.

The purpose of data analytics is to generate insights from data by connecting patterns and trends with organizational goals. Data Analytics uses basic query expressions like SQL to segment and view data. A person working as a data scientist plays a role to generate their own set of questions; on the other hand, a data analyst prepares answers to those questions from a collection of data. Once one has come across understanding all of the differences between data analytics and data science—and can completely relate it with the identification of an absolute career path—one can start evaluating which path suits them the most. To decide which option is best aligned as per your personal and professional goals, you can consider some key factors such as educational and professional background, personal interests and desired career trajectory. Students who want to discover something new and looking forward to making a career in the field of Data Analytics or Data Science should enroll themselves in Top Engineering Colleges in India for these reputed courses.

The candidate for this field must have done a Bachelor's degree in science/engineering/business administration/commerce/mathematics or masters in mathematics/statistics with 50% or equivalent passing marks. As everyone has noticed the number of opportunities in data Science in India is continuously increasing from the last three years. There are many top engineering colleges in India which conducts this program.

Data science and data analytics are some of the most in-demand domains in the industry right now. A combination of the right skill sets and real-world experience can help you secure a substantial career in these trending domains.