Introduction to Big Data – an overview of the 10 V's

Introduction to Big Data – an overview of the 10 V's, available at Free, has an average rating of 4.45, with 12 lectures, based on 545 reviews, and has 16412 subscribers.

You will learn about This course will give the students an overview of this field and understand the various characteristics of Big Data that will help them later in their career to make better decisions in terms of architectures, tools, and technologies to use when dealt with a certain business problem. This course is ideal for individuals who are Anyone curious about Big Data. This course is not limited to experienced Data Scientists or Machine Learning Engineers. It is particularly useful for Anyone curious about Big Data. This course is not limited to experienced Data Scientists or Machine Learning Engineers.

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Summary

Title: Introduction to Big Data – an overview of the 10 V's

Price: Free

Average Rating: 4.45

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

This course will give the students an overview of this field and understand the various characteristics of Big Data that will help them later in their career to make better decisions in terms of architectures, tools, and technologies to use when dealt with a certain business problem.

Who Should Attend

Anyone curious about Big Data. This course is not limited to experienced Data Scientists or Machine Learning Engineers.

Target Audiences

Anyone curious about Big Data. This course is not limited to experienced Data Scientists or Machine Learning Engineers.

This course is designed to be an in-depth overview of the field of Data Science. It teaches the students various Characteristics of Big Data as well as discuss a few types of Data that exists. After completing this course, you will have the knowledge that can be applied later on in your journey into this field when you’re selecting an Algorithm, a Tool, a Framework, or even while making a Blueprint of how to deal with the current problem at hand.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction Lecture

Chapter 2: Volume

Lecture 1: Volume Lecture

Chapter 3: Velocity

Lecture 1: Velocity Lecture

Chapter 4: Variety

Lecture 1: Variety Lecture

Chapter 5: Veracity

Lecture 1: Veracity Lecture

Chapter 6: Validity

Lecture 1: Validity Lecture

Chapter 7: Volatility

Lecture 1: Volatility Lecture

Chapter 8: Value

Lecture 1: Value Lecture

Chapter 9: Variability

Lecture 1: Variability Lecture

Chapter 10: Viability

Lecture 1: Viability Lecture

Chapter 11: Visualization

Lecture 1: Visualization Lecture

Chapter 12: Conclusion

Lecture 1: Conclusion Lecture

Instructors

Taimur Zahid
Machine Learning Engineer

Rating Distribution

1 stars: 18 votes
2 stars: 39 votes
3 stars: 136 votes
4 stars: 198 votes
5 stars: 154 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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