Both Data Science and Big Data have risen to prominence recently. Whilst they are not immutably linked, it is certainly true that many data scientists work extensively with big data. Both topics are so new that they are poorly understood; nevertheless there is considerable interest in them and there is a significant shortfall in the number of trained data scientists in the job market. This course introduces both the job role of the data scientist and big data itself.
This course is vendor neutral; it is not about how to use any one vendor's products, it is about the fundamental underpinnings of these two important subject areas. This course is aimed at people who are trying to understand data science and big data and want to know about the range of skills, technologies and techniques that are appropriate to these new areas. So, for example, if you have already chosen, say, Hortonworks as a platform and want to acquire specific skills in that area, then take a look at QA's 'Data Science for the Hortonworks Data Platform'. On other hand, if you fit the profile below, then this is the course for you.
At the end of this course you will be able to:
This course is intended for people aspiring to be data scientists and/or to work with Big Data. Others who may take this course include Business Intelligence (BI) professionals who want to work with big data and/or are looking to move into Data Science. People coming into the course are expected to have at least 3 years experience working in the IT field-typically in the areas of databases, BI, analytics or related areas.
Module 1: Introduction
This module introduces both Data Science and Big Data.
Module 2: Big Data
This module outlines the difference between the tabular data that underpins the relational model and Big Data. It explores not only what Big Data is, but why the commercial (and scientific) worlds are so interested in it. Finally it looks at how we can cross analyse tabular data and Big Data.
Module 3: Finding the patterns in data
One of the vital skills for a data scientist is to be able to understand how numbers behave, how they are distributed and how we can determine the significance of any differences that we observe between numerical values. This involves an understanding of normal distributions, means, modes and standard deviations as well as, for example, Chi squared and t tests. This module covers these topics.
Module 4: Data models - relational and NoSQL
This module describes the different models that are used to represent data and specifically contrasts the relational and NoSQL worlds. It covers CAP theorem and why that is relevant to data models.
Module 5: Hadoop, HDFS and MapReduce
Hadoop, HDFS and MapReduce are well established examples of tools/methodologies for manipulations Big Data.
Module 6: Data visualisation
The ability to create data visualisations that have meaning for a given set of data and the target audience is a major part of being a Data Scientist. This module describes how to plan and deploy visualisations and provides two case studies of where this has been successfully achieved.
Module 7: Introduction to R
R is a well-established, open source language, very specifically aimed at analysis. This module introduces the language and provides some practical work in using it.
Module 8: Data mining
This module introduces not only data mining, but the CRISP methodology which helps to ensure that data mining is carried out effectively. It also introduces the Monte Carlo methodology for modelling and analysing systems.
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