Course Description

Data Analytics – Mining and Analysis of Big Data

In this online data analysis course Data Analytics – Mining and Analysis of Big Data you will be introduced to the concept of big data and to a number of techniques that are used to analyse and interpret big data. The course starts off with introducing you to big data and lists the four V’s of big data. You will learn about associative rule mining, and about when association can be applied and the patterns that arise in mining. In the second module, you will learn about clustering analysis. You will examine the difference between clustering and classification and the different types of clustering. You will also learn about K-means clustering and K-meloids. In the last module, you will learn about online and active learning, learn about experimentation and the difference between an online and offline context of creating data. You will be introduced to the n-arm bandit problem and how to find solutions for the multi-arm bandit problem. This free online course will be of great interest to professionals involved in data science and data analysis and any learner who wants to learn more about analysing big data using mining and clustering techniques.

Registration Link

Provider

alison

Target

  • Data scientists
  • Data analysts
  • Business intelligence professionals
  • Students and learners interested in data analysis

Sector

  • Information technology
  • Data science
  • Analytics
  • Business and marketing analytics

Area

  • Big data analysis
  • Data mining techniques
  • Clustering and classification methods
  • Online and active learning methodologies

Method

Online

Certification

Yes

Duration

1.5-3 Avg Hours

Assessment

No

Cost

Learning Outcomes

  • Define association rule mining
  • Explain mining frequent patterns and rules
  • List when association rules can be applied
  • Define the apriori algorithm
  • List the four V’s of big data
  • Explain why social media data can be hard to disambiguate
  • Distinguish between clustering and classification
  • Explain why clustering is used
  • List the different types of clustering
  • Define K-Means clustering
  • Explain k-meloids
  • Define what stochastic multi-choice problems are
  • Explain the n-arm bandit problem
  • Describe some solutions for the multi-arm bandit problem

Learning Content

  • MODULE 1: Introduction to Associative Rule Mining
  • MODULE 2: Introduction to Big Data
  • MODULE 3: Introduction to Clustering Analysis
  • MODULE 4: Experimentation and Active Learning