IM522 Big Data

DESCRIPTION

In an ever-increasing data driven world, this unit equips students to understand and apply techniques, such as data mining, machine learning, and deep learning from computer science, statistics, and maths to a range of organisational and business fields. Big data analytics provides critical assistance in determining sustainability and profitability of businesses. Big data projections aim to reduce overall costs, increasing the consumer base for products, and improving quality. Students will learn a variety of techniques used in big data and analysis of data to deliver business outcomes and gain competitive advantage.

RELEVANT COURSES

Master of Business Administration
Graduate Diploma of Business Administration

*Core unit

CREDIT POINTS

10

DELIVERY MODE

On campus

PREREQUISITE OR CO-REQUISITE

IM421 Information Systems

UNIT LEARNING OUTCOMES

LO1 Critically evaluate the use of big data tools and techniques in business contexts

LO2 Apply various data mining techniques to discover patterns in large data sets

LO3 Critically examine the application of big data tools and techniques in scenarios involving various combinations of physical and economic factors

LO4 Apply R programming to produce real-time analysis of emerging issues in commercial products.

LO5 Critically evaluate the advanced algorithms of social media analytics to leverage insights into customer sentiment.

CONTENT

  • Big Data and big data science in business
  • Software and programs of big data analytics including social media analytics and its use in business
  • Hadoop architecture and HDFS
  • Hadoop clusters and the Hadoop ecosystem
  • Incorporation of business data into Hadoop MapReduce Framework
  • R programming basics for application of big data analytics in organisations
  • Statistical analysis of data in R Studio; data preparation and missing value treatment, univariate statistics, ANOVA, Chi-Square Test
  • Basics of correlation test and developing the correlation matrix, logistic regression, and discriminant analysis; use of regression techniques
  • Nonlinear regression, factor analysis, segmentation and clustering, decision tree analysis, consumer behaviour analytics-neural network, text analytics, sentiment analysis, perceptual mapping
  • Big analytics and incorporation of its technique at the commercial level

ASSESSMENT METHODS

  1. Individual Project – 20%
  2. Group Project – 30%
  3. Model of Consumer Behaviour – 50%

PRESCRIBED READINGS

Nil

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