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Data Analytics - Mining and Analysis of Big Data Free Course

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  • Description
  • Outcome
  • Certification
  • In this free 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 interpet big data.

    The course begins by 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.

    Next, 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.

    Finally, you will learn about online and active learning. You will 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.

  • Having completed this course you will be able to:
    - 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.
    - Distinguish between online and offline context of creating data.
    - Define what stochastic multi-choice problems are.
    - Explain the n-arm bandit problem.
    - Describe some solutions for the multi-arm bandit problem.

  • All Alison courses are free to study. To successfully complete a course you must score 80% or higher in each course assessments. Upon successful completion of a course, you can choose to make your achievement formal by purchasing an official Alison Diploma, Certificate or PDF.

    Having an official Alison document is a great way to share your success. Plus it’s:

    • Ideal for including in CVs, job applications and portfolios
    • An indication of your ability to learn and achieve high results
    • An incentive to continue to empower yourself through learning
    • A tangible way of supporting the Alison mission to empower people everywhere through education.

Modules List( 5 )
  • Module 1: Data Analytics - Mining and Analysis of Big Data
    • Learning Outcomes
    • Associative Rule Mining - Part I
    • Associative Rule Mining - Part II
    • Lesson Summary
  • Module 2: Introduction to Big Data
    • Learning Outcomes
    • Big Data: A Small Introduction - Part I
    • Big Data: A Small Introduction - Part II
    • Lesson Summary
  • Module 3: Introduction to Clustering Analysis
    • Learning Outcomes
    • Clustering Analysis - Part I
    • Clustering Analysis - Part II
    • Lesson Summary
  • Module 4: Experimentation and Active Learning
    • Learning Outcomes
    • Introduction to Experimentation and Active Learning - Part I
    • Introduction to Experimentation and Active Learning - Part II
    • An Introduction to Online Learning - Reinforcement Learning - Part I
    • An Introduction to Online Learning - Reinforcement Learning - Part II
    • Lesson Summary
  • END OF COURSE ASSESSMENT
  • Module 5: Data Analytics - Mining and Analysis of Big Data Assessment
    • Data Analytics - Mining and Analysis of Big Data Assessment
Topics List ( 4 )
Module 1: Data Analytics - Mining and Analysis of Big Data
This module introduces associative rule mining. You will learn about frequent patterns and rules in mining and be able to list when association rules can be applied. You will also learn about the apriori algorithm.
Topics List ( 4 )
Module 2: Introduction to Big Data
In this module you will learn about big data. You will learn about the four V’s of big data and why social media data can be hard to disambiguate.
Topics List ( 4 )
Module 3: Introduction to Clustering Analysis
In this module you will learn about clustering analysis. This module will teach you about why clustering is used and the different types of clustering. You will also learn about K-means and K-meloids.
Topics List ( 6 )
Module 4: Experimentation and Active Learning
In this module you will learn about experimentation and active learning and creating data in an online and offline context. You will learn about online learning and the n-arm bandit problem.
Topics List ( 1 )
Module 5: Data Analytics - Mining and Analysis of Big Data Assessment
You must score 80% or more to pass this assessment.
Course Features
  • Duration

    2-3 Hours

  • Publisher

    NPTEL

  • Video

    Yes

  • Audio

    Yes

  • Assessment

    Yes

  • Certification

    Yes

  • Price

    Free

  • Reward

    50 Pts

  • Responsive

    No

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