MACHINE LEARNING | 3 mins read

The Difference between Data analytics and Data science

By Kant on 29 May 2020

Since, data science, analytics, and machine learning are growing and the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. By the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet, which makes it extremely important to know the basics of the field at least. After all, here is where our future lies.

What is Data Science?

Data science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and can provide accurate predictions and insights that can be used to power critical business decisions.


Skills Required to Become a Data Scientist

  • Strong knowledge of PythonSASRScala
  • Structured and unstructured database understanding
  • Understand multiple analytical functions
  • Knowledge of machine learning


What is a Data Analyst?


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


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