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
