Menu

Big Data Science Certified Professional (BDSCP)

BDSCP Courses

The Big Data Science Certified Professional (BDSCP) curriculum from Arcitura is comprised of 15 individual course modules. Course delivery options can include private on-site workshops, live virtual training, public workshops or self-paced training via Study Kits or eLearning. Each module is a one-day course when taught by a Certified Trainer or can take 10-14 hours to complete via self-study.

Module 1
Fundamental Big Data

Foundational course that establishes a basic understanding of Big Data from business and technology perspectives, including common benefits, challenges and adoption issues.

[ learn more ]

 

Module 2
Big Data Analysis & Technology Concepts

Explores contemporary analysis practices, technologies and tools for Big Data environments at a conceptual level, focusing on common analysis functions and features of Big Data solutions.

[ learn more ]

 

Module 3
Big Data Analysis & Technology Lab

A hands-on lab providing a series of real-world exercises for assessing and establishing Big Data environments, and for solving problems using Big Data analysis techniques and tools.

[ learn more ]

 

Module 4
Fundamental Big Data Analysis & Science

Essential coverage of Big Data analysis algorithms, as well as the application of analytics, data mining and basic mathematical and statistical techniques.

[ learn more ]

 

 

Module 5
Advanced Big Data Analysis & Science

An in-depth course that covers the application of a range of advanced analysis techniques, including machine learning algorithms, data visualization and various forms of data preparation and querying.

[ learn more ]

 

Module 6
Big Data Analysis & Science Lab

A case study-based lab providing a series of real-world exercises that require participants to apply Big Data analysis and analytics techniques to fulfill requirements and solve problems.

[ learn more ]

 

Module 7
Fundamental Big Data Engineering

Focuses on the hands-on usage of the Hadoop and MapReduce framework, HDFS, Hive, Pig, Sqoop, Flume and NoSQL databases.

[ learn more ]

 

Module 8
Advanced Big Data Engineering

Builds upon Module 7 to delve into advanced engineering, testing and debugging techniques, as well as the application of Big Data design patterns.

[ learn more ]

 

Module 9
Big Data Engineering Lab

A hands-on lab during which participants carry out a series of exercises based upon the tools and technologies covered in preceding course modules.
 

[ learn more ]

 

Module 10
Fundamental Big Data Architecture

Coverage of the Hadoop stack, data pipelines and other technology architecture layers, mechanisms and components, and associated design patterns.
 

[ learn more ]

 

Module 11
Advanced Big Data Architecture

Drill-down of Big Data solution environments, additional advanced design patterns and coverage of cloud implementations and various enterprise integration considerations.

[ learn more ]

 

Module 12
Big Data Architecture Lab

A hands-on lab during in which a set of real-world exercises challenge participants to build and integrate Big Data solutions within IT enterprise and cloud-based environments.

[ learn more ]

 

Module 13
Fundamental Big Data Governance

Introduces Big Data governance frameworks, and covers the basics of governing high-volume, multi-source data and Big Data technology environments.

[ learn more ]

 

 

Module 14
Advanced Big Data Governance

Steps through the Big Data lifecycle to cover specific precepts, processes and associated policies for regulating disparate bodies of data and Big Data solution environments.

[ learn more ]

 

Module 15
Big Data Governance Lab

A hands-on lab during which participants are required to work with Big Data governance precepts, processes and policies to address a series of real-world governance concerns.
 

[ learn more ]