Data Science vs Big Data vs Data Analytics | Simplilearn

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Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012.

An article by Forbes states that Data is growing faster than ever before and 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 at least know the basics of the field. After all, here is where our future lies.

In this video, we will differentiate between the Data Science, Big Data, and Data Analytics, based on what it is, where it is used, the skills you need to become a professional in the field, and the salary prospects in each field.

Download the Big Data career guide to explore and step into the exciting world of Big Data, and follow the path towards your dream career:

➡️ About Data Analyst Master’s Program
This online data analyst course will transform you into a data analytics expert. In this certification course, you will learn the latest analytics tools and techniques, how to work with SQL, the languages of R and Python, the art of creating data visualizations, and how to apply statistics and predictive analytics in a business environment.

✅ Key Features
– Simplilearn’s JobAssist helps you get noticed by top hiring companies
– Industry-recognized Data Analyst Master’s certificate from Simplilearn
– Dedicated live sessions by faculty of industry experts
– Masterclasses from IBM experts
– Industry-recognized IBM certifications for IBM courses
– Ask-Me-Anything (AMA) sessions with IBM leadership
– Capstone from 3 domains and 20+ projects
– Exclusive hackathons conducted by IBM
– Lifetime access to self-paced learning content
– Program crafted to initiate your journey as a Data Analyst

✅ Skills Covered
– Data Analytics
– Statistical Analysis using Excel
– Data Analysis using Python and R
– Data Visualization Tableau and Power BI
– Linear and logistic regression modules
– Clustering using KMeans
– Supervised Learning
– Unsupervised Learning

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