Advanced Big Data Analytics Expert (ABDE)

Course Information
  • Duration: 5-Day / 40 Hours
  • Who Should Attend: Advanced Big Data Analytics Expert (ABDE)is designed for anyone who is interested in advanced knowledge and skills in Big Data and would like the opportunity to learn in a supportive and encouraging environment.
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.

Pre-Requisite

It is preferred that participants have some knowledge in Big Data, Data Analytics or successfully received a Certificate of Competency in Advanced Big Data Professional (ABDP).

Examination

Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Big Data based on the syllabus covered.

Certification

Participants will be awarded a Certificate of Competency and recognized as a Advanced Big Data Analytics Expert (abde) upon meeting the requirements and passing the examination.

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 Analytsics/li>
  • 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

    • Advanced Big Data Analytics Expert (ABDE) involves rigorous usage of real-time case studies, hands-on exercises and group discussion