Big Data Training Program

Option: Fast-track (60 credits)
Sale price€2,997.00

Tax included

Big Data Training Program is a comprehensive training program to help anyone interested in pursuing a career in the big data industry develop career-relevant skills and expertise.

Big data is a field that treats methods to analyze and extract information from large or complex datasets that are difficult to be dealt with by traditional data-processing methods. Big data engineering involves interacting with massive data processing systems and databases in large-scale computing environments. Due to the significant growth of the big data market, the demand for professionals skilled in big data engineering and analytics has never been greater. Entering the world of big data requires an amalgam of experience, big data knowledge, and using the correct tools and technologies. It is a solid career choice for both new and experienced professionals. TechClass Big Data training program is a comprehensive online degree program tailored for big data and analytics-related job positions to prepare the students for the trending job opportunities in the industry.

Big Data Training Program learning path includes three options with a vast portfolio of practical courses. It will provide the students with a step-by-step guide towards learning the latest job-ready skills.

Learning outcomes

Big Data

Learn the fundamentals of essential topics of big data engineering such as big data systems, data ingestion, distributed storage, processing and storage life cycle, cluster management, real-time streaming, in-memory computing, map-reduce, data pipelines, resource pooling, queuing systems, and much more. Get familiar with the world of big data, its applications, common practices, tools, and technologies.

Data Ingestion

Learn the most common practices of data ingestion, cleaning, and manipulation. Gain expertise in using relational (SQL) and non-relational (NoSQL) databases to manage, query, and filter data and retrieve information. Learn how to use different database management systems to ingest and integrate data from different data sources.

Large-scale Data Processing

Learn the ins and outs of large-scale data processing and its common methods and practices. Learn how to use Apache Spark for executing data engineering, data science, and optimized query execution on single-node machines or clusters. Gain hands-on experience using PySpark library to perform exploratory data analysis at scale and write all sorts of Spark applications using Python APIs. Learn how to use big data processing tools and services on Azure and AWS.

Distributed Data Storage and Processing

Get familiar with the essential concepts of computing clusters, data persistence, distributed storage and processing, and management of distributed systems. Gain hands-on experience using Apache Kafka for ingesting and processing streaming data in real-time. Learn how to use Apache Hadoop for distributed processing of large datasets across computing clusters using simple programming models. Learn how to work with Redis for distributed, in-memory data storage, caching, and message broking. Learn how to use distributed data storage and processing services on Azure and AWS.


It is essential to understand each students' needs and provide a training package suitable for such demands. Students have three options to pick when they register in this program. Some want only to get familiar with the fundamental topics, some want to have more comprehensive knowledge, and others may wish to obtain ultimate and extensive skills and expertise.

  1. Fast-track (60 credits)
    Mandatory: 40 credits / Elective: 10 credits / Project : 5 credits / Job Preparation: 5 credits
    This option would be a suitable start to familiarize the students with the most fundamental topics. Throughout this program, students can make sure the area is interesting for them and obtain skills for entry-level jobs in the field.
  2. Professional (90 credits)
    Mandatory: 40 credits / Elective: 35 credits / Project : 10 credits / Job Preparation: 5 credits
    This option will start with the fundamental topics, then allow the students to select more elective subjects and extend their skills. The plan is to prepare the students for mid-level positions throughout this option.
  3. Masters (120 credits)
    Mandatory: 40 credits / Elective: 60 credits / Project : 15 credits / Job Preparation: 5 credits
    This option enables the students to have extensive and comprehensive skills and practical knowledge in the program's scope. Students may select various topics from the elective portfolio and even learn extended material.

Learning path

Stage 1: Introduction

  • Introduction to the program (0 credits)

Stage 2: Mandatory

  • Introduction to Python for Data Science (10 credits)
  • SQL for Data Science (5 credits)
  • MongoDB and NoSQL Databases (5 credits)
  • Fundamentals of Big Data (5 credits)
  • Big Data Analytics with Spark and Hadoop (10 credits)
  • PySpark (5 credits)

Stage 3: Elective

  • Programming Refresher (5 credits)
  • Basic SQL in PostgreSQL (5 credits)
  • Fundamentals of Data Engineering (5 credits)
  • Apache Kafka (10 credits)
  • Apache Storm (5 credits)
  • Redis (5 credits)
  • Qubole (5 credits)
  • HPCC (10 credits)
  • SparkSQL (5 credits)
  • Machine Learning with Big Data (10 credits)
  • Big Data on Cloud with AWS (15 credits)
  • Big Data on Cloud with Azure (15 credits)

Stage 4: Project

  • Final Project (5/10/15 credits)*

*Based on the program option. Fast-track: 5 credits / Professional: 10 credits / Masters: 15 credits.

Stage 5: Job Preparation

  • Job Preparation (5 credits)


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