Advanced Big Data Analytics Expert (ABDE)

Upcoming Batch
25 to 29 Feb 2021
Online ‘Live’
9:30am to 5:30pm
Singapore Timezone

5 Sessions / 40-Hours


Apply Now

1 to 5 Mar 2021
Online ‘Live’
9:30am to 5:30pm
Singapore Timezone

5 Sessions / 40 Hours


Apply Now

26 to 30 Apr 2021
Online ‘Live’
9:30am to 5:30pm
Singapore Timezone

5 Sessions / 40 Hours


Apply Now

Course Information
  • Duration: 5-Day / 40 Hours

  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination

  • Who Should Attend: Data Scientist, Analyst, Data Engineers, CIO, CTO, Software Programmers, and Anyone interested in acquiring advanced knowledge and skills in Big Data, Hadoop and Python


Course Objective

To provide participants with the advanced knowledge on Big Data Analytics.

Learn how Big Data Analytics is being applied in real life through real-time demonstration on scenario based hands-on exercises.


Pre-Requisite

It is preferred that participants successfully complete and pass Advanced Big Data Professional



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,Hadoop and Python based on the syllabus covered


Module 1 Overview on Big Data Hadoop Ecosystem

Topics Covered
  • Python Refresher
  • Standard Toolkit for Hadoop and Analytics
  • Understanding Relational, NoSQL, and Graph Databases
  • Construction of Data Pipelines
  • Data Modeling in Hadoop
  • HDFS Schema Design
  • HBase Schema Design
  • Working on Metadata

Module 2 Advanced Hadoop Techniques

Topics Covered
  • What is Data Ingestion?
  • Different Ways to Perform Data Ingestion
  • Data Extraction
  • Data Processing in Hadoop
  • Overview on MapReduce
  • Working on Spark components
  • Pig and How it is Being Used
  • Overview on Hive
  • Impala Speed-Oriented Design

Module 3 Introduction to Orchestration

Topics Covered
  • Orchestration Frameworks in Hadoop
  • Oozie Terminology and Workflow
  • Windowing Analysis using Spark
  • Parameterizing Workflows
  • Scheduling Patterns
  • Execution of Workflows

Module 4 Real-Time Processing with Hadoop

Topics Covered
  • Stream Processing
  • Integration of Apache Storm with HDFS and HBase
  • Trident Overview
  • Spark Streaming Overview
  • Flume Interceptors
  • Low-Latency Enrichment, Validation, Alerting, and Ingestion
  • NRT Counting, Rolling Averages, and Iterative Processing
  • Complex Data Pipelines

Module 5 Working with Big Data Framework using Python and Spark

Topics Covered
  • Hadoop and Spark Refresher
  • Spark SQL and Python Pandas DataFrame
  • Improving Analysis Performance with Parquet and Partitions
  • Working with Unstructured Data
  • Working on Spark DataFrames
  • Writing Output from Spark DataFrames
  • Data Manipulation with Spark DataFrames
  • Plotting Graph in Sparks

Module 6 Exploratory Data Analysis

Topics Covered
  • Handling of Missing Values using Spark DataFrame
  • Correlation Analysis with Python PySpark DataFrame
  • Improving Analysis Performance with Parquet and Partitions
  • Understanding Exploratory Data Analysis
  • Identify Target Variable and Related KPIs
  • Feature Importance of Target Variable
  • Different Phases of an Analytics Project Life Cycle
  • Gaussian Distribution of Numeric Features

Module 7 Advanced Big Data Analysis

Topics Covered
  • Reproducible Approach to Gathering Data
  • Understanding the Standards and Code Practices
  • Segmentation of Workflow
  • Missing Value Preprocessing with High Reproducibility
  • Use of Functions / Loops to Optimize Coding
  • Utilization of Libraries / Packages / Algorithms
  • Normalization of Data

Module 8 Putting Everything To Together

Topics Covered
  • Reading Data from a CSV File with Python PySpark Object
  • Reading JSON Data with Python PySpark Object
  • Using Python PySpark Objects for SQL Operations
  • Generating Statistical Measurements
  • Visualisation Using Plotly

Module 9 Big Data and Machine Learning using Spark

Topics Covered
  • Resilient Distributed Datasets with Spark
  • Introduction to Spark MLlib
  • Decision Tree with Spark MLlib
  • K-Means Clustering with Spark
  • Term Frequency – Inverse Document Frequency (TF-IDF)
  • DataFrame API with Spark MLlib
  • Understanding A/B Testing

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

What Past Participants Say
The knowledge acquired from CASUGOL program has allowed me to gain a deep understanding on the technology and will come handy when there is a requirement to kickstart future projects.

Syed Othman Abd Rahman

The hands-on exercises which is part of CASUGOL course syllabus are clearly explained, demonstrated and implemented in class with the guidance of experienced CASUGOL trainers.

Andrew Lim

Why CASUGOL

Customization of Programs for specific industry, organisation, government agencies, statutory boards.


Flexible programmes designed to cater to the individual needs of participants, whether for professional upskilling, or for general interest.

Benefit from contribution from leading Industry Experts, Academics, and Researchers from across the world.


Opportunities for employers to develop their workforce and for individuals to enhance their career.

Dynamic learning environment that providing participants with professional networking opportunity.


Online support for participants after the training.

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