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Big Data Fundamentals

Learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms.

Big Data Fundamentals

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Comienza el Jul 26
10 semanas estimadas
8–10 horas por semana
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Sobre este curso

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Organizations now have access to massive amounts of data and it’s influencing the way they operate. They are realizing in order to be successful they must leverage their data to make effective business decisions.

In this course, part of the Big Data MicroMasters program, you will learn how big data is driving organisational change and the key challenges organizations face when trying to analyse massive data sets.

You will learn fundamental techniques, such as data mining and stream processing. You will also learn how to design and implement PageRank algorithms using MapReduce, a programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. You will learn how big data has improved web search and how online advertising systems work.

By the end of this course, you will have a better understanding of the various applications of big data methods in industry and research.

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  • Idioma:English
  • Transcripción de video:English
  • Programas asociados:

Lo que aprenderás

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  • Knowledge and application of MapReduce
  • Understanding the rate of occurrences of events in big data
  • How to design algorithms for stream processing and counting of frequent elements in Big Data
  • Understand and design PageRank algorithms
  • Understand underlying random walk algorithms

Plan de estudios

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Section 1: The basics of working with big data
Understand the four V’s of Big Data (Volume, Velocity, and Variety); Build models for data; Understand the occurrence of rare events in random data.

Section 2: Web and social networks
Understand characteristics of the web and social networks; Model social networks; Apply algorithms for community detection in networks.

Section 3: Clustering big data
Clustering social networks; Apply hierarchical clustering; Apply k-means clustering.

Section 4: Google web search
Understand the concept of PageRank; Implement the basic; PageRank algorithm for strongly connected graphs; Implement PageRank with taxation for graphs that are not strongly connected.

Section 5: Parallel and distributed computing using MapReduce
Understand the architecture for massive distributed and parallel computing; Apply MapReduce using Hadoop; Compute PageRank using MapReduce.

Section 6: Computing similar documents in big data
Measure importance of words in a collection of documents; Measure similarity of sets and documents; Apply local sensitivity hashing to compute similar documents.

Section 7: Products frequently bought together in stores
Understand the importance of frequent item sets; Design association rules; Implement the A-priori algorithm.

Section 8: Movie and music recommendations
Understand the differences of recommendation systems; Design content-based recommendation systems; Design collaborative filtering recommendation systems.

Section 9: Google's AdWordsTM System
Understand the AdWords System; Analyse online algorithms in terms of competitive ratio; Use online matching to solve the AdWords problem.

Section 10: Mining rapidly arriving data streams
Understand types of queries for data streams; Analyse sampling methods for data streams; Count distinct elements in data streams; Filter data streams.

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Preguntas frecuentes

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Question: This course is self-paced, but is there a course end date?
Answer: Yes. The first course release started on May 15, 2017 and ends on December 1, 2018.
The new release of the course starts on December 1, 2018 and ends on December 1, 2020.

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

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