Data science
| Institution | University of Nairobi |
| Course | Bacher of computer s... |
| Year | 3rd Year |
| Semester | Unknown |
| Posted By | KCSE / KJSEA/CBC EXAMINER |
| File Type | |
| Pages | 2 Pages |
| File Size | 171.87 KB |
| Views | 128 |
| Downloads | 0 |
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CMT 429: Introduction to Data Science
A Data Scientist must find patterns within data. This data must be organized in a standard format in order to find patterns.
A Data Scientist analyzes the data, finds patterns, makes future predictions and presents the result with useful insights in a way the company can understand
This is done through a Data Science Life Cycle.
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Python for Data Science
The volume of data that one has to deal has exploded to unimaginable levels in the past decade, and at the same time, the price of data storage has systematically reduced. Private companies and research institutions capture terabytes of data about their users’ interactions, business, social media, and also sensors from devices such as mobile phones and automobiles. The challenge of this era is to make sense of this sea of data. This is where big data analytics comes into picture.
Big Data Analytics largely involves collecting data from different sources, merge it in a way that it becomes available to be consumed by analysts and finally deliver data products useful to the organization business. The process of converting large amounts of unstructured raw data, retrieved from different sources to a data product useful for organizations forms the core of Big Data Analytics.
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