ABDE™ – Associate Big Data Engineer
The ABDE™ by the Data Science Council of America (DASCA) is the world’s most credible 3rd–party, vendor–neutral certification for young graduating university and tech–school students graduating with majors such as Information Technology, Computer Science and Software Engineering, readying themselves for exciting Big Data careers.
Foundational Data Science – Understanding of how data science helps tap the big power of big data and derive information and intelligence for decision-making; framework of concepts and generic models of managing and harnessing big data.
Data Processing Framework & Hadoop – Generic big data processing framework; the Hadoop Ecosystem; storing, processing and modelling data in Hadoop 3; Hadoop Ecosystem tools for big data processing; Python and Hadoop 3.
Big Data Analytics Basics – Understanding data types, data origination modes, streaming data; learning the frameworks of analysing, managing and harnessing massive data using tools and applications like Python and R.
R and Hadoop Applications – Building Data Analytics Applications; Capturing, processing, sorting, classifying, analysing, managing and harnessing big data using the Hadoop ecosystem; understanding the use of combination of tools like R, Python and MapReduce etc., in big data analytics.
Analytics in Machine Learning and AI – Foundational concepts and framework of machine learning and artificial intelligence; understanding how big data analytics fires machine intelligence; using R and Python to program ML and AI applications.
Streaming Data Storage – High-volume streaming data storage; applications of document-oriented NoSQL database in high volume data storage and processing using MongoDB; MongoDB Data Structures; MongoDB Shell Applications.
Streaming Data Architectures – Real time analytics; framework of real-time streaming data architectures; publish-subscribe messaging system; Kafka and Distributed Messaging; Building ETL Pipelines using Kafka; Building streaming applications using Kafka Streams.
Streaming & Batch Data Processing – Framework of stream and batch data processing and analytics; Big Data Processing with Apache, Spark, and Scala; PySpark and SparkR for Big Data Analytics; Big Data Processing with Apache Flink.
Enterprise Data Analytics Implementation – Framework of developing enterprise-wide data analytics ecosystem; Elasticsearch and Enterprise Big Data search; Hadoop Cluster deployment and security; Big Data Processing on Cloud; Building a Data Analytics Pipeline.
Am I eligible?
Track 1 (Bachelor’s Degree)
– Completed (or final year) a Bachelor’s degree in Computer Science / Technology / Engineering / Applied Sciences / Related disciplines.
– Experience is not mandatory.
– Basic proficiency using Python / Java or any other programming language is desirable.
Track 2 (Associate Degree/Diploma)
– Completed an Associate Degree/ Diploma in Computer Science / Technology / Engineering / Applied Sciences / Related disciplines.
– Minimum 2 years of computing experience with proficiency in using Python / Java or any other programming language.
I am eligible. What’s next?
Great! ABDA™ undoubtedly might just be the most important credential to earn at this stage. If you wish to participate, register for our next ABDA intake from here.
– Course duration: 16 weeks
– Class will be conducted on Saturday from 2.00 pm to 6.00 pm
– Maximum number of participants per intake is 15
– Classes will be conducted online using Google Classroom.
After registration, what's next?
Upon registration you will receive
– Exam preparation resources (ABDE ™ Book 1 and 2)
– Online learning and preparation resources
– Online digitally proctored exam.
– You can take the exam anywhere, even at home or in your office
Upon Successful Completion
– You will become a Digitally Badged ABDE™ . This digitally-equipped certification is the most convincing countersignature of your potential and talent to the onset of your industry careers.
– The ABDE™ Credential Case that includes your ABDE™ Credential Certificate and a lapel pin to be worn in your pocket tops