Effective Report Writing and Presentation Skills
24/10/2024Leading Digital Transformation
24/10/2024Big Data and Data Analytics
£5,000.00
Overview:
This training program comprehensively explores big data and analytics, covering everything from foundational concepts to advanced data processing, real-time analytics, and AI-driven insights. Participants will gain hands-on experience with key technologies like Hadoop, Spark, Flink, and NoSQL databases while learning about data governance and cloud-based solutions. By the end of the course, participants will be able to design scalable, actionable data strategies that align with organizational goals.
Program Objectives:
By the end of this course, participants will be able to:
- Understand foundational concepts in big data and data analytics, including real-time data processing.
- Identify, store, and manage structured, unstructured, and semi-structured data.
- Design and implement big data architectures suitable for batch and real-time analytics.
- Utilize advanced big data technologies like Hadoop, Spark, Flink, and NoSQL databases.
- Leverage machine learning and AI for predictive analytics and business intelligence.
- Develop a data governance framework to support compliance and data quality.
- Create a strategic roadmap for implementing big data solutions within their organization.
Target Audience:
- Data Analysts and Data Scientists
- Business Intelligence Professionals
- IT Managers and System Administrators
- Business Analysts, Decision Makers, and Digital Transformation Leads
- Compliance Officers and Data Governance Specialists
- Anyone interested in leveraging big data for business insights and analytics
Program Outline:
Day 1: Foundations of Big Data Concepts and Architectures
- Introduction to Big Data – Defining Big Data and the 5 V’s: Volume, Velocity, Variety, Veracity, and Value.
- Big Data’s Role in Business Analytics and Decision-Making.
- Overview of Big Data Architectures – Data Warehouses, Data Lakes, and Cloud Storage Solutions.
- Introduction to Hadoop Ecosystem – HDFS, MapReduce, and YARN.
- RDBMS vs. NoSQL Databases (e.g., MongoDB, Cassandra) for Big Data Storage.
- Exploring Cloud-Based Storage and Distributed Systems (e.g., AWS S3, Google Cloud Storage).
- Hands-On Exercise: Setting Up a Basic Data Storage Solution Using Hadoop and Cloud Storage.
Day 2: Big Data Processing and Computation
- Processing Frameworks for Big Data – Hadoop, Spark, Flink, and Kafka.
- Understanding Batch vs. Real-Time Data Processing.
- MapReduce Fundamentals and Distributed Data Processing.
- Leveraging Cloud Platforms for Scalable Data Processing (AWS, Google Cloud, Azure).
- Building ETL Pipelines for Big Data – Extract, Transform, Load Processes.
- Hands-On Exercise: Creating a Data Pipeline Using Spark and Apache Flink.
Day 3: Data Analytics and AI in Big Data
- Core Principles of Data Analytics – From Descriptive to Predictive Analytics.
- Machine Learning Techniques for Data Analysis – Supervised, Unsupervised, and Reinforcement Learning.
- Integrating AI with Big Data Analytics (e.g., Image Recognition, NLP, and Predictive Modeling).
- Overview of Analytics Tools – Databricks, Tableau, Power BI, and Google BigQuery.
- Cloud-Based AI and Machine Learning Platforms (Google AI, AWS ML) for Scalable Analytics.
- Hands-On Exercise: Building a Predictive Model Using Spark ML and Visualizing with Tableau.
Day 4: Big Data Project Management and Strategy Development
- Identifying Business Use Cases for Big Data Analytics.
- Case Studies on Successful Big Data Implementations (Netflix, Amazon, and Google).
- Project Planning for Big Data – Assessing Organizational Needs and Goals.
- Best Practices for Big Data Project Management and Cross-Functional Collaboration.
- Establishing Data Governance and Compliance Frameworks.
- Hands-On Exercise: Creating a Data Strategy and Project Plan for a Big Data Initiative.
Day 5: Architecting Big Data Solutions for Business Intelligence
- Designing Effective Big Data Architectures – Storage, Processing, and Analytics Layers.
- Identifying and Integrating Data Sources for Comprehensive Analytics.
- Building Data Pipelines for Continuous Data Flow and Real-Time Analytics.
- Optimizing Storage and Processing Costs in Cloud Environments.
- Data Quality and Governance – Ensuring Data Accuracy and Compliance.
- Producing Actionable Insights Through Predictive Analytics and Visualization.
- Capstone Project: Developing an End-to-End Big Data Solution Architecture for Business Intelligence.