Advanced Big Data Analytics Expert (ABDE)

The worldwide revenue for Big Data and Data Analytics is expected to grow to more than $203 Billion in 2020. Banking specifically will see the fastest spending growth, and industries such as telecommunications, insurance, transportation, and utilities will also start to increase their own spending during this same period, helping to fuel growth.

Together with an increase interest and investment in AI, new tools for collecting and analysing data and new enterprise roles and responsibilities will emerge presenting IT professionals and individuals planning to pursue a career in Big Data and Data Analytics with tremendous career opportunities. This can never be a better time to acquire the necessary skills and gain proficiency in Big Data.

Course Objective
Advanced Big Data Analytics Expert is aimed to provide participants with the advanced knowledge on Big Data Analytics. Through real-time demonstration on scenario based hands-on exercises, Participants will be able to experience first-hand how Advanced Big Data Analytics can be applied in real life.
Course Duration
40 hours / 5-Days
Course Outline
MODULE 1 THE BIG DATA LANDSCAPE OVERVIEW  

  • What is Big Data?
  • Big data vs. its predecessors
  • How big data relates to data analytics and data science
  • The big data paradigm
  • Big data professional roles
  • How big data projects benefit businesses and industries
  • The Hadoop ecosystem and architecture
  • Other technologies in the big data paradigm
  • MODULE 2: BIG DATA PROJECT PLANNING  

  • Beyond the Hadoop ecosystem
  • Other popular projects by MapR
  • Commercial distributions of Hadoop
  • Security within Hadoop
  • Data engineering
  • Useful programming languages
  • The 4-step big data planning process
  • Staying competitive as a big data professional
  • MODULE 3:DATA MINING  

  • Predictive Analytics
  • Machine Learning
  • MODULE 4: DATA TYPES  

  • Structured vs unstructured
  • Static vs streamed
  • Attitudinal, behavioural and demographic data
  • Data-driven vs user-driven analytics
  • Data validity
  • Volume, velocity and variety of data
  • MODULE 5:MODELS  

  • Building models
  • Statistical Models
  • Machine learning
  • MODULE 6: DATA CLASSIFICATION

  • Clustering
  • K-groups, K-means, nearest neighbours
  • Ant colonies, birds flocking
  • MODULE 7: PREDICTIVE MODELS

  • Decision trees
  • Support vector machine
  • Naive Bayes classification
  • Neural networks
  • Markov Model
  • Regression
  • Ensemble methods
  • MODULE 8: BUILDING MODELS

  • Data Preparation (MapReduce)
  • Data cleansing
  • Choosing methods
  • Developing model
  • Testing Model
  • Model evaluation
  • Model deployment and integration
  • MODULE 9: ADVANCED ANALYTICAL METHODS FOR PROBLEM SOLVING

  • The nature of data science and analytics
  • Fraud prevention in real-time using machine learning
  • Online sales improvement through recommendation engines
  • Customer churn prediction and reduction through logistic regression
  • Best option selection using multi-criteria decision making
  • Stock price predictions using Markov Chains
  • Analyzing how price changes impact sales volumes using simple linear regression
  • MODULE 10: BASIC DATA SCIENCE MECHANICS

  • The benefits of object-oriented programming
  • Programming Python
  • R programming for data science
  • Where is your data coming from?
  • Traditional relational database management system (RDBMS – DSFD) source
  • Structured Query Language (SQL) in analytics and data science
  • Making value of location data with Geographic Information System (GIS)
  • Machine learning
  • Popular machine learning algorithms
  • MODULE 11: TOOLS TO ANALYZE DATA AND COMMUNICATE FINDINGS

  • Free applications for data science and analytics
  • Context and benchmarking using free and open data
  • Scraping the web for market data
  • The different types of data visualization
  • Three simple steps to design for your audience
  • Data graphics
  • Design styles to convey powerful messages
  • Design data analytics dashboards
  • MODULE 12: OVERVIEW OF OPEN SOURCE AND COMMERCIAL SOFTWARE

  • Selection of R-project package
  • Python libraries
  • Hadoop and Mahout
  • Selected Apache projects related to Big Data and Analytics
  • Selected commercial solution
  • Integration with existing software and data sources
  • Pre-Requisite
    NA
    Examination
    Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Advanced Big Data Analytics based on the syllabus covered
    Certification
    Participants will be recognised as a CASUGOL CERTIFIED PROFESSIONAL and be awarded a Certificate of Competency in Advanced Big Data Analytics Expert (ABDE)upon meeting the requirements and passing the examination.
    Who Will Benefit from the Course
    Advanced Big Data Analytics Expert (ABDE) is designed for participants who are interested in pursuing a career in the areas of Big Data and Data Analytics and would like the opportunity to learn in a supportive and encouraging environment.

    This course will equip you with a set of skills that you can draw on to implement the technology in your organisation.

    Class is limited to 20 participants as hands-on sessions and real-time demonstration is expected.