Big Data Analytics

Course Description


Introduction

 

Welcome to the Big Data Analytics training course by Cambridge for Global Training. This course is designed to provide participants with the skills and knowledge required to effectively analyse and derive insights from large and complex datasets. With the exponential growth of data in today's digital age, organisations need professionals who can harness the power of big data to drive decision-making and innovation. Through this course, participants will learn the fundamentals of big data analytics and gain hands-on experience with cutting-edge tools and techniques.

 

Course Objectives

By the end of the course, participants will be able to:

 

  • Explore and understand the concepts and challenges of big data analytics.
  • Implement various big data processing frameworks such as Hadoop and Spark.
  • Apply machine learning algorithms to extract patterns and insights from large datasets.
  • Utilise data visualisation techniques to communicate findings effectively.
  • Develop strategies for scalable and efficient data storage, retrieval, and processing.
  • Analyse real-world big data use cases and apply appropriate analytical methods.
  • Collaborate with stakeholders to translate data insights into actionable business strategies.

 

Who Should Attend

 

  • Data scientists
  • Data analysts
  • IT professionals
  • Business intelligence professionals
  • Database administrators
  • Anyone interested in leveraging big data for business insights and decision-making
Course Outline


Unit 1: Introduction to Big Data Analytics

 

  • Overview of big data concepts and challenges
  • Characteristics of big data: volume, velocity, variety, and veracity
  • Introduction to big data processing frameworks
  • Understanding the Hadoop ecosystem
  • Overview of Apache Spark and its applications

 

Unit 2: Big Data Processing Frameworks

 

  • Hadoop MapReduce: principles and applications
  • Introduction to Hadoop Distributed File System (HDFS)
  • Apache Spark architecture and components
  • Spark RDDs (Resilient Distributed Datasets) and transformations
  • Spark SQL for querying structured data

 

Unit 3: Machine Learning for Big Data

 

  • Introduction to machine learning algorithms
  • Supervised, unsupervised, and semi-supervised learning techniques
  • Regression, classification, and clustering algorithms
  • Ensemble learning methods
  • Deep learning and neural networks for big data analytics

 

Unit 4: Data Visualisation and Communication

 

  • Importance of data visualisation in big data analytics
  • Data visualisation tools and techniques
  • Design principles for effective data visualisation
  • Interactive visualisation with tools like Tableau and Power BI
  • Communicating insights to non-technical stakeholders

 

Unit 5: Big Data Storage and Processing

 

  • Scalable data storage solutions: NoSQL databases, distributed file systems
  • Data processing pipelines and workflows
  • Data streaming and real-time analytics
  • Optimising big data processing for performance and efficiency
  • Case studies and practical applications of big data analytics
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Big Data Analytics
REF code: V-1328
Date: 09 - 13 Dec 2024
City: Kigali
Language: English
Price: 4500 £

Course Description


Introduction

 

Welcome to the Big Data Analytics training course by Cambridge for Global Training. This course is designed to provide participants with the skills and knowledge required to effectively analyse and derive insights from large and complex datasets. With the exponential growth of data in today's digital age, organisations need professionals who can harness the power of big data to drive decision-making and innovation. Through this course, participants will learn the fundamentals of big data analytics and gain hands-on experience with cutting-edge tools and techniques.

 

Course Objectives

By the end of the course, participants will be able to:

 

  • Explore and understand the concepts and challenges of big data analytics.
  • Implement various big data processing frameworks such as Hadoop and Spark.
  • Apply machine learning algorithms to extract patterns and insights from large datasets.
  • Utilise data visualisation techniques to communicate findings effectively.
  • Develop strategies for scalable and efficient data storage, retrieval, and processing.
  • Analyse real-world big data use cases and apply appropriate analytical methods.
  • Collaborate with stakeholders to translate data insights into actionable business strategies.

 

Who Should Attend

 

  • Data scientists
  • Data analysts
  • IT professionals
  • Business intelligence professionals
  • Database administrators
  • Anyone interested in leveraging big data for business insights and decision-making

Course Outline


Unit 1: Introduction to Big Data Analytics

  • Overview of big data concepts and challenges
  • Characteristics of big data: volume, velocity, variety, and veracity
  • Introduction to big data processing frameworks
  • Understanding the Hadoop ecosystem
  • Overview of Apache Spark and its applications

Unit 2: Big Data Processing Frameworks

  • Hadoop MapReduce: principles and applications
  • Introduction to Hadoop Distributed File System (HDFS)
  • Apache Spark architecture and components
  • Spark RDDs (Resilient Distributed Datasets) and transformations
  • Spark SQL for querying structured data

Unit 3: Machine Learning for Big Data

  • Introduction to machine learning algorithms
  • Supervised, unsupervised, and semi-supervised learning techniques
  • Regression, classification, and clustering algorithms
  • Ensemble learning methods
  • Deep learning and neural networks for big data analytics

Unit 4: Data Visualisation and Communication

  • Importance of data visualisation in big data analytics
  • Data visualisation tools and techniques
  • Design principles for effective data visualisation
  • Interactive visualisation with tools like Tableau and Power BI
  • Communicating insights to non-technical stakeholders

Unit 5: Big Data Storage and Processing

  • Scalable data storage solutions: NoSQL databases, distributed file systems
  • Data processing pipelines and workflows
  • Data streaming and real-time analytics
  • Optimising big data processing for performance and efficiency
  • Case studies and practical applications of big data analytics
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