Fundamentals of Big Data Training

  • Delivery Method: Online Instructor-Led / Classroom Based / Onsite
  • Participation Model: Public Training / Private / In-House Training
  • Duration: 3 Days
  • Level: Fundamentals
  • Price: From €4,500 +TAX
  • Upcoming Date:

The Fundamentals of Big Data course is a three-day introductory programme for professionals who want to understand Big Data technologies, Data Science concepts, and the data models that underpin modern analytical platforms.

The course is particularly suitable for organisations and technical professionals evaluating which NoSQL technologies and Big Data solutions may be appropriate for their environment. It also provides a strong first step for learners beginning a broader professional journey into Data Science.

Throughout the programme, participants explore different ways of modelling data, the core differences between SQL and NoSQL systems, and several of the major technologies used within the modern Big Data ecosystem.

Platforms including Neo4j, Hadoop, Spark, and MongoDB are introduced, with practical discussion of how they store, query, process, and analyse data.

The course also introduces fundamental Data Science concepts together with the mathematical and statistical thinking that supports analytical work. Participants learn how Python can be used to work with different data sources, interact with NoSQL databases through APIs, and support Big Data analysis.

By the end of the course, learners will understand the main technologies within the Big Data landscape, recognise different data modelling approaches, and have a clearer understanding of how Hadoop and Spark can be applied to large-scale analytical problems.


For corporate groups, we can organize this training as an on-site private group.
HAVELSAN
ASELSAN
PepsiCo Türkiye
JTI TÜTÜN ÜRÜNLERİ SANAYİ A.Ş.
ING Bank
DHL
MASTERCARD
Huawei
TotalEnergies
Mercedes-Benz Türk A.Ş.
Goodyear
FORD OTOMOTIV SANAYI A.S.
3M COMPANY
Schneider Electric
Central Bank of Azerbaijan
BP Petrolleri
Procter & Gamble (P&G)
Qatar Foundation
Bank Of China
GlaxoSmithKline (GSK)
MORGAN STANLEY & CO Inc
Novo Nordisk
DEUTSCHE BANK
Azerbaycan Cumhuriyeti Emek ve Ahalinin Sosyal Güvenliği Bakanlığı
ACCENTURE

Prerequisites

Participants should have:

  • Previous experience using Python
  • Experience working with data

Familiarity with the following areas will also be beneficial:

  • Basic Python programming
  • Tabular data
  • Data analysis
  • Fundamental database concepts
  • Basic SQL querying concepts

Who Should Attend

This course is particularly suitable for:

  • Existing Python developers
  • Data Analysts
  • Technical professionals moving into Big Data
  • Learners beginning a Data Science development path
  • Technical teams evaluating NoSQL technologies
  • Developers working with data platforms
  • Analysts who need to work with larger and more varied datasets

What You Will Learn

By the end of the course, participants will understand:

  • The fundamentals of Data Science
  • The role of a Data Scientist
  • Different data models
  • The differences between SQL and NoSQL
  • What a NoSQL database is
  • Major categories of NoSQL databases
  • The role of Hadoop within Big Data
  • The fundamentals of Spark
  • Graph database concepts
  • Document-oriented databases

Participants will also be able to:

  • Use common Data Science terminology with greater confidence
  • Work with Python's native data structures and storage approaches
  • Query NoSQL databases through Python APIs
  • Perform foundational Big Data processing using Hadoop
  • Analyse large datasets using Spark
  • Evaluate technology options for different data problems
  • Approach analytics problems from a Data Science perspective

Training Outline

Introduction to Data Science

This module introduces the fundamental concepts behind Data Science and its role within modern organisations.

Topics include:

  • What is Data Science?
  • The role of a Data Scientist
  • Roles within Data Science teams
  • Technical and analytical skills
  • Data Science methods
  • Asking useful analytical questions
  • Answering insight questions
  • Applying Data Science to business problems
  • Science in Business
  • Data-driven decision-making

Participants learn that effective Data Science is not simply about tools; it is also about framing the right questions, understanding available data, and creating useful insights.

Python for Data Science

This section focuses on Python as a tool for data processing and analytics.

Topics include:

  • Using Python for Data Science
  • Python data structures
  • Data manipulation
  • Reading and writing data
  • Dataset processing with Python
  • Python-based analytics
  • Connecting to NoSQL systems
  • Working with APIs

Participants develop their ability to use Python as a common interface across different data technologies.

Data Analytics and Statistical Inference

This section introduces the statistical thinking used within data analysis.

Topics include:

  • Data Analytics fundamentals
  • Descriptive analysis
  • Statistical inference
  • Understanding data distributions
  • Evaluating relationships within data
  • Producing insights
  • Interpreting analytical results
  • Translating business questions into analytical problems

The aim is to help learners see Big Data technologies as part of a broader analytical process rather than as isolated infrastructure tools.

Data Models

This module introduces different approaches to storing and representing data.

Topics include:

  • Structured data
  • Semi-structured data
  • Unstructured data
  • Relational data models
  • Document models
  • Graph models
  • Key-value approaches
  • Advantages of different data models
  • Selecting a model for a specific use case

SQL and NoSQL

Participants compare relational and non-relational database approaches.

Topics include:

  • What is a SQL database?
  • Relational database structures
  • What is a NoSQL database?
  • SQL versus NoSQL
  • Schema approaches
  • Scalability
  • Distributed data
  • NoSQL use cases
  • Categories of NoSQL databases

Big Data with Neo4j

This section introduces graph databases using Neo4j.

Topics include:

  • Graph databases
  • Nodes
  • Relationships
  • Properties
  • Graph data modelling
  • Connected data
  • Neo4j fundamentals
  • Graph querying
  • Graph database use cases

Participants examine why graph databases can be useful when relationships between data points are especially important.

Big Data with Hadoop

This module introduces the fundamentals of the Hadoop ecosystem.

Topics include:

  • What is Hadoop?
  • Distributed computing
  • Hadoop architecture
  • Distributed storage
  • Storing large datasets
  • Parallel data processing
  • Hadoop use cases
  • Python and Hadoop scenarios

Participants learn how large datasets can be distributed and processed across multiple systems.

Big Data with Spark

This section introduces Apache Spark and its role in scalable analytics.

Topics include:

  • What is Spark?
  • Spark architecture
  • Distributed processing
  • In-memory processing
  • Analysing large datasets
  • Data transformation with Spark
  • Python and Spark
  • Analytical workloads
  • Key differences between Hadoop and Spark

Participants examine how Spark can support fast and scalable analytical processing.

Big Data with Mongo

This section introduces document-oriented NoSQL databases through MongoDB.

Topics include:

  • Document databases
  • MongoDB fundamentals
  • Documents
  • Collections
  • Flexible schemas
  • CRUD operations
  • Using MongoDB through Python APIs
  • MongoDB queries
  • Document database use cases

Participants learn how structured and semi-structured data can be represented using document-based models.

Analytics Case Study

The final section brings together the concepts and technologies covered during the course.

In the case study, participants:

  • Define a business problem
  • Evaluate suitable data models
  • Examine available data sources
  • Select a SQL or NoSQL approach
  • Choose an appropriate Big Data technology
  • Process data with Python
  • Evaluate analytical outcomes

This exercise helps learners understand how different Big Data technologies can be combined within a realistic analytical scenario.

Why Choose Us

Leading UK-based global training provider since 1995.

Experience Fundamentals of Big Data through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

  • Delivery Method: Online Instructor-Led
  • Participation Model: Public / Private (In-house)
  • Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
  • Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
  • Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
  • Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
  • Worldwide Access: Join our live virtual classrooms from anywhere in the world or arrange a dedicated online training program for your organization.

Experience Fundamentals of Big Data in a professional face-to-face classroom environment. Classroom Based training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class exclusively for your organization.

  • Delivery Method: Classroom Based
  • Participation Model: Public / Private (In-house)
  • Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
  • Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
  • Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
  • Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange Fundamentals of Big Data as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

  • Delivery Method: Onsite
  • Participation Model: Private (In-house)
  • Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
  • Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
  • Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
  • Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
  • Worldwide Onsite Delivery: Bilginç IT Academy trainers can travel internationally to deliver dedicated training programs at your preferred location.

Contact us for more detail about our trainings and for all other enquiries!

Fundamentals of Big Data Training Course Schedule

Join our public courses in our Istanbul, London and Ankara facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

For corporate groups, we can organize this training as an on-site private group.
10 October 2026 (3 Days)
Istanbul, Ankara, London
€4,500 +TAX
17 October 2026 (3 Days)
Istanbul, Ankara, London
€4,500 +TAX
31 October 2026 (3 Days)
Istanbul, Ankara, London
€4,500 +TAX
05 November 2026 (3 Days)
Istanbul, Ankara, London
€4,500 +TAX
10 November 2026 (3 Days)
Istanbul, Ankara, London
€4,500 +TAX
12 November 2026 (3 Days)
Istanbul, Ankara, London
€4,500 +TAX
14 November 2026 (3 Days)
Istanbul, Ankara, London
€4,500 +TAX
25 November 2026 (3 Days)
Istanbul, Ankara, London
€4,500 +TAX

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