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Big Data in the Age of AI (2019)

Big Data in the Age of AI (2019)

2h 8mBeginner2019-09-19

Authors

Barton Poulson

Barton Poulson

Professor, Designer, Data Analytics Expert

Course details

The hype about big data may have peaked several years ago, but big data is far from gone. Instead it forms the foundation for some of today's most exciting technologies. Artificial intelligence (AI), machine learning, and data science rely on big data, or data that—by virtue of its velocity, volume, or variety—can't be easily stored or analyzed with traditional methods. In this nontechnical course, Barton Poulson digs into the topic of big data, explaining how it works and shapes our modern data universe. Barton explains big data's relationship to AI, data science, social media, and the Internet of Things (IoT). He goes over some of the ethical issues behind the use of big data. Plus, he covers techniques involved in analyzing big data, including data mining and predictive analytics.

Topics include:
Identify the components that make up big data.
Examine how big data has grown over the last few years.
Explain the importance of using big data in business organizations.
Distinguish between knowledge requirements for using big data and for understanding data science.
Justify the need for training on big data within an organization.
Analyze the factors that go into utilizing big data on a project.
Differentiate outcomes that are derived from big data from outcomes that are derived from observing behaviors.

Skills covered

Data EngineeringData AnalysisFoundationsData ScienceBusiness Analysis and StrategyBusiness Software and Tools

Concepts

0. Introduction

  • 01 - How big data shapes AI

1. Defining Big Data

  • 02 - The volume, velocity, and variety of big data
  • 03 - Artificial intelligence and machine learning
  • 04 - Social media and the Internet of Things
  • 05 - Data warehouses, data lakes, and the cloud
  • 06 - Edge computing and fog computing

2. How Is Big Data Used

  • 07 - Big data for business strategy
  • 08 - Big data for customer interactions
  • 09 - Big data for applications

3. Big Data and Data Science

  • 10 - Ten ways big data is different from small data
  • 11 - The three facets of data science
  • 12 - Data science without big data
  • 13 - Big data without data science

4. Ethics in Big Data

  • 14 - Big data and privacy
  • 15 - Data governance

5. Data Logistics

  • 16 - Structured, semi-structured, and unstructured data
  • 17 - Batch processing vs. stream processing
  • 18 - Distributed storage and processing
  • 19 - An evolving data landscape

6. Analyzing Big Data

  • 20 - Challenges with data preparation
  • 21 - Visualizing big data
  • 22 - Data mining
  • 23 - Text analytics
  • 24 - Sentiment analysis
  • 25 - Predictive analytics
  • 26 - Anomaly detection

Conclusion

  • 27 - Next steps

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