Papers in this syllabus

Paper 10

DATA MANAGEMENT AND ANALYTICS

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This unit specifies competencies required to apply R for big data management and analytics. It enables the learner to identify key concepts in big data management, visualize real world big data problems, apply statistical tools for big data analysis and manage big data using R and perform data analytics using R.

On completion, a candidate should be able to

  • Identify key concepts in big data management
  • Visualize real world big data problems
  • Apply statistical tools for big data analysis
  • Manage big data using R
  • Perform data analytics using R

Content

  1. 1

    Identify key concepts in Big data management

    Identify key concepts in Big data management

    1. ❑ Definition of: ✓ Big data ✓ Big data Analytics ✓ Big data management ❑ Characteristics of big data ✓ Volume ✓ Velocity ✓ Variety ✓ Veracity ✓ Value ❑ Different types of data ✓ Structured data ✓ Unstructured data ✓ Semi-structured data ✓ Metadata ✓ Big data life cycle ❑ Examples of big data ❑ Techniques of big data analytics ✓ Predictive analytics ✓ Collective intelligence ✓ Machine learning ❑ Big data analytics business uses and examples ❑ Big data analytics benefits and challenges

  2. 2

    Visualize real world big data problems

    Visualize real world big data problems

    1. ❑ Definition of data visualization tools ❑ Types of data visualization ✓ Charts, tables, graphs, maps ✓ Infographics ✓ Dashboards ❑ Examples of methods to visualize data ✓ Area chart ✓ Box-and-whisker plots ✓ Bubble cloud ✓ Bullet graph ✓ Cartogram ✓ Circle view ✓ Dot distribution map ✓ Gantt chart ✓ Highlight table ✓ Histogram ✓ Matrix ✓ Network ✓ Polar area ✓ Radial tree ✓ Scatter plot (2d or 3d) ❑ Examples of big data visualization tools ✓ tableau ✓ infogram ✓ chartblocks ✓ datawrapper ✓ plotly ❑ Excel data analysis ✓ Visualizing data with charts ✓ Chart elements and chart styles ✓ Using pictures in column charts ✓ Sparklines ✓ Pivotcharts

  3. 3

    Apply statistical tools for Big Data Analysis

    Apply statistical tools for Big Data Analysis

    1. ❑ Excel data validation ❑ Data visualization in r ✓ Basic visualization - Histogram - Bar / line chart - Box plot - Scatter plot ✓ Advanced visualization - Heat map - Mosaic map - Map visualization - 3D graphs - Correlogram ❑ data visualization use cases ✓ Data visualizations for business intelligence ✓ Data visualizations on internet for public consumption ✓ Data visualizations for research and data mining ❑ Advantages and benefits of good data visualization Definition of dataset Purpose of datasets ❑ Types of data sets ✓ numerical dataset ✓ bivariate dataset ✓ multivariate dataset ✓ categorical dataset correlation dataset ❑ Properties of dataset ✓ Centre of data ✓ Skewness of data ✓ Spread among the data members ✓ Presence of outliers ✓ Correlation among the data ✓ Type of probability distribution that the data follows Examples of datasets

  4. 4

    Manage Big Data using R

    Manage Big Data using R

    1. ❑ Setting Up R Environment ✓ R and R studio ✓ Installation of R Studio ✓ Console ✓ Script Editor ✓ Installation of R Packages ✓ R Calculator ✓ R help ❑ R Operations ✓ R Syntax - Using the Console - Using R Scripts - R Comments ✓ R Operators ✓ Variables ✓ Data Structures - Vectors including Scalars - Matrices - Arrays - Data frames - Lists - Reading Data Frames - Manipulating Data - Exporting Data ❑ Descriptive Statistic Measures

  5. 5

    Perform Data Analytics using R

    Perform Data Analytics using R

    1. ❑ Big Data Ecosystem ✓ The Hadoop ecosystem ✓ Hadoop core components ✓ Concepts of Hadoop Distributed File System (HDFS) ✓ MapReduce Architecture - The MapReduce Programming Model ✓ Other Components Of Hadoop - Hive - Pig - Sqoop - Spark - HBase - Zookeeper