Big Data is a technology "Moonshot," those that arise and change people's lives and their professional careers.
This eBook is organized to summarize Big Data, Data Science, Analytics and Machine Learning, structuring knowledge, less technical, for a better understanding and rapid learning, demystifying and guiding Executives and Market Professionals on how to use Big Data on their favor, for greater professional success.
It is the first stage to become interested in Big Data.
Check the learning summary you take on this journey.
- Introduction to Big Data and Data Science. Main Technologies applied to Big Data. Cloud technologies, systems, hardware, and software.
- Hadoop Ecosystem and its importance to Big Data. The parallel programming paradigm of MapReduce to solve problems in Big Data. Data Lake, Data Warehouse, and ETL processes for Big Data.
- Analytics Science and its derivations for Predictive and Big Data. Analytics Tools and their Big Data applications. Machine Learning (ML) and its relationship with Big Data. ML Applications for Big Data. Data Visualization introduction.
- Professional careers in Big Data. Companies that created Big Data and adopted the technology. Big Data applications for social networks and the Internet of things.
- Privacy and Governance in Big Data. Big Data and Data Science Influencers. How to be a Data Scientist.
- Big Data for Executives. Big Data for Market Professionals. Big Data summary and general conclusions. Its implications for business and professional life.
What goes on in this Second Edition?
In this eBook Second Edition, we looked at the content and revised the texts for readability.
The eBook includes more information to refresh the content.
The new sections included are:
Chapter 3 - Section 2 - Data is Files
Chapter 7 - Section 5 - Success Case - Tesla
Chapter 8 - Section 2 - GDPR and LGPD Privacy
Chapter 10 - Section 6 - Edge Computing
Chapter 10 - Section 7 - Digital Transformation
Chapter 11 - Section 10 - The Spark Importance
Chapter 16 - Section 7 - Big Data + Data Science + ML
Chapter 18 - Section 4 - Analytics Translator
Chapter 18 - Section 5 - Is it worth going for a new career?