The Difference between Data analytics and Data science

Skills Required to Become a Data Analyst

• Knowledge of mathematical statistics
• Fluent understanding of R and Python
• Data wrangling
• Understand PIG/ HIVE

Data Science vs. Data Analytics

Data science is a term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. A data scientist creates questions, while a data analyst finds answers to the existing set of questions.

Data science can be seen as the incorporation of multiple parental disciplines, including data analytics, software engineering, data engineering, machine learning, predictive analytics, data analytics, and more. It includes retrieval, collection, ingestion, and transformation of large amounts of data, collectively known as big data. Data science is responsible for bringing structure to big data, searching for compelling patterns, and advising decision-makers to bring in the changes effectively to suit the business needs. Data analytics and machine learning are two of the many tools and processes that data science uses.

Data science, 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 strong career in these trending domains.

Source: "Data Science vs. Data Analytics vs. Machine Learning: Expert Talk"  By Srihari Sasikumar