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Big Data

(UOP-DSC460.AE1)
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1

Introduction, Storage Concepts and NoSQL Database

  • Understanding Big Data
  • Evolution of Big Data
  • Failure of Traditional Database in Handling Big Data
  • 3 Vs of Big Data
  • Sources of Big Data
  • Different Types of Data
  • Big Data Infrastructure
  • Big Data Life Cycle
  • Big Data Technology
  • Big Data Applications
  • Big Data Use Cases
  • Cluster Computing
  • Distribution Models
  • Distributed File System
  • Relational and Non‐Relational Databases
  • Scaling Up and Scaling Out Storage
  • Introduction to NoSQL
  • Why NoSQL
  • CAP Theorem
  • ACID
  • BASE
  • Schemaless Databases
  • NoSQL (Not Only SQL)
  • Migrating from RDBMS to NoSQL
2

Big Data Processing, Management, and Cloud Computing

  • Data Processing
  • Shared Everything Architecture
  • Shared‐Nothing Architecture
  • Batch Processing
  • Real‐Time Data Processing
  • Parallel Computing
  • Distributed Computing
  • Big Data Virtualization
  • Cloud Computing Types
  • Cloud Services
  • Cloud Storage
  • Cloud Architecture
3

Driving Big Data with Hadoop Tools and Technologies

  • Apache Hadoop
  • Hadoop Storage
  • Hadoop Computation
  • Hadoop 2.0
  • HBASE
  • Apache Cassandra
  • SQOOP
  • Flume
  • Apache Avro
  • Apache Pig
  • Apache Mahout
  • Apache Oozie
  • Apache Hive
  • Hive Architecture
  • Hadoop Distributions
4

Big Data Analytics

  • Terminology of Big Data Analytics
  • Big Data Analytics
  • Data Analytics Life Cycle
  • Big Data Analytics Techniques
  • Semantic Analysis
  • Visual analysis
  • Big Data Business Intelligence
  • Big Data Real‐Time Analytics Processing
  • Enterprise Data Warehouse
  • Introduction to Machine Learning
  • Machine Learning Use Cases
  • Types of Machine Learning
5

Mining Data Streams, Cluster Analysis and Big Data Visualization

  • Itemset Mining
  • Association Rules
  • Frequent Itemset Generation
  • Itemset Mining Algorithms
  • Maximal and Closed Frequent Itemset
  • Mining Maximal Frequent Itemsets: the GenMax Algorithm
  • Mining Closed Frequent Itemsets: the Charm Algorithm
  • CHARM Algorithm Implementation
  • Data Mining Methods
  • Prediction
  • Important Terms Used in Bayesian Network
  • Density-Based Clustering Algorithm
  • DBSCAN
  • Kernel Density Estimation
  • Mining Data Streams
  • Time Series Forecasting
  • Clustering
  • Distance Measurement Techniques
  • Hierarchical Clustering
  • Analysis of Protein Patterns in the Human Cancer‐Associated Liver
  • Recognition Using Biometrics of Hands
  • Expectation Maximization Clustering Algorithm
  • Representative‐Based Clustering
  • Methods of Determining the Number of Clusters
  • Optimization Algorithm
  • Choosing the Number of Clusters
  • Bayesian Analysis of Mixtures
  • Fuzzy Clustering
  • Fuzzy C‐Means Clustering
  • Big Data Visualization
  • Conventional Data Visualization Techniques
  • Tableau
  • Bar Chart in Tableau
  • Line Chart
  • Pie Chart
  • Bubble Chart
  • Box Plot
  • Tableau Use Cases
  • Installing R and Getting Ready
  • Data Structures in R
  • Importing Data from a File
  • Importing Data from a Delimited Text File
  • Control Structures in R
  • Basic Graphs in R

1

Introduction, Storage Concepts and NoSQL Database

  • Discussing Big Data Characteristics
2

Big Data Processing, Management, and Cloud Computing

  • Implementing the Data Processing Cycle
  • Manipulating Data in a DataFrame
  • Modifying Data in a DataFrame
3

Mining Data Streams, Cluster Analysis and Big Data Visualization

  • Implementing the Eclat Algorithm Using R
  • Implementing Apriori Algorithm Using R
  • Implementing K-Means Clustering
  • Creating a Bubble Chart
  • Using the length(), mean(), and median() Functions
  • Using the if-else Statement
  • Using the while Loop

1

Mining Data Streams, Cluster Analysis and Big Data Visualization

  • Implementing Frequent Itemset Mining Using R
  • Determining the Support Count and Confidence Count
  • Creating a Connection in a New Workbook
  • Creating a Bar Chart
  • Creating a Line Chart
  • Creating a Pie Chart
  • Creating a Box Plot
  • Assigning Value to a Variable
  • Using the matrix() Function
  • Using the for Loop

Big Data

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