2014
Lynda
Barton Poulson
2:12
English

Big data is big news. But what is big data, and how do we use it? Simply put, big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Spreadsheets and relational databases just don't cut it with big data. In this course, Barton Poulson tells you the methods that do work, introducing all the techniques and concepts involved in capturing, storing, manipulating, and analyzing big data, including data mining and predictive analytics. He explains big data's relationship to data science, statistics, and programing; its uses in marketing, scientific research, and tools like Amazon's recommendation engine; and the ethical issues that lie behind its use.
This course qualifies for 2 Category A professional development units (PDUs) through lynda.com, PMI Registered Education Provider #4101.

Introduction
Welcome
What Is Big Data?
The three Vs of big data
Volume
Velocity
Variety
Does big data need all three?
2. How Is Big Data used?
Understanding big data for consumers
Understanding big data for business
Understanding big data for research
3. Big Data and Data Science
Ten ways big data is different from small data
The three facets of data science
Types and skills in data science
Data science without big data
Big data without data science
4. Ethics in Big Data
Challenges with anonymity
Challenges with confidentiality
5. Sources and Structures of Big Data
Human-generated data
Machine-generated data
Structured data
Unstructured data
6. Storing Big Data
Distributed storage and the cloud
Cloud computing: IaaS, PaaS, SaaS, and DaaS
A brief introduction to Hadoop
7. Preparing Data for Analysis
Challenges with data quality
ETL: Extract, transform, load
Additional Vs of big data
8. Big Data Analysis
Monitoring and anomaly detection
Data mining and text analytics
Predictive analytics
Visualization
The role of Excel in big data
Conclusion
Next steps

Download File Size:203.8 MB

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