Sas Training, Data Science Analytics and Big data — What’s the Difference?

For student undertaking SAS training at one of the reputed institutes, they might have a lot of queries about their field. While they may get a lot of theoretical knowledge about the concepts and practices used in big data analytics, they might be wondering about the different streams that they can have an impact on when they pass out of the SAS Training in Mumbai.

In order to simplify things this post will help SAS training students to understand the difference between data science, big data, and data analytics.

First off it is imperative to understand that all three fields pertain to the key component i.e. data. Data is all around us in today’s time. Right from unstructured social media data to structured data from past historical reports about a stock’s performance.

What is the definition?

1 — Data Science –students would define Data Science Training In Mumbai that pertains to data cleansing, preparation for analysis and data wrangling.

2 — Big Data — This is the fodder (i.e. massive amount of data) that is used to obtain insights to aid in business decision making

3 — Data analytics — For students, this is nothing but making sense of the individual numbers using data aggregation procedures

Where are they used?

1 — Data Science — This is primarily applied in Internet searches and search recommendation engines for online retail with the help of modeling and algorithms

2 — Big Data — For students Big Data Training in Mumbai is used in retail, financial, and media and communication industry

3 — Data analytics — Is used in healthcare, travel and tourism, and gaming industry

What are the skills needed?

1 — Data Science — In depth experience in Data Science and R, Python, Hadoop and SQL database would count as major skills here.

2 — Big Data — Big Data Hadoop Creativity, analytical skills, math sans stats skills and computer science expertise are major skills needed for a big data specialist.

3 — Data analytics — Maths and stats skills through Data analytics, machine learning, and data wrangling are some of the core skills needed to be learnt by a data analyst. Also by empowering themselves with skills like communication and data visualization and data intuition, they can take their job to the highest trajectory of success and growth.

To conclude this post we summarized the key differences between data science, big data, and data analytics. With this knowledge the students’ SAS training is made more effective.