4301 Operations courses in Redruth delivered Online

Name: UK Import/Export : Master UK Logistics & Supply Chain Management
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Quality Management, Control and Audits Training

By Online Training Academy

Are you ready to take your career to the next level? Our Quality Management, Control and Audits Training is designed to provide you with the knowledge and skills needed to excel in the field of quality management. Whether you're a seasoned professional or just starting, this course will equip you with the tools to meet and exceed industry standards. Key Features: CPD Certified Free Certificate from Reed CIQ Approved Developed by Specialist Lifetime Access The "Quality Management, Control, and Audits Training" course teaches you the essentials of managing and improving quality in any organization. You'll learn how to understand and meet customer needs, ensuring products and services meet high standards. The course covers techniques for continuous improvement, including Total Quality Management and Six Sigma. You'll also explore the importance of quality control and measurement to maintain consistency. The training includes methods for conducting effective quality audits to identify and address issues. Additionally, you'll gain insights into managing the supply chain to ensure quality from suppliers to customers. This comprehensive training prepares you to implement quality management practices, enhance processes, and deliver better outcomes for your organization. Course Curriculum Module 01: Introduction to Quality Management Module 02: Understanding Customer Expectations and Needs Module 03: Total Quality Management Module 04: Quality Control Module 05: Quality Measurement and Improvement Module 06: Quality Audits Module 07: Six Sigma Module 08: Supply Chain Management Learning Outcomes: Understand fundamental principles of quality management and its key components. Identify and assess customer expectations and needs in quality management. Implement Total Quality Management (TQM) methodologies in various organizational settings. Apply quality control techniques to maintain product and service standards. Measure and improve quality through continuous improvement processes. Conduct quality audits to ensure compliance with established standards. CPD 10 CPD hours / points Accredited by CPD Quality Standards Quality Management, Control and Audits Training 1:46:25 1: Module 01: Introduction to Quality Management 19:32 2: Module 02: Understanding Customer Expectations and Needs 11:14 3: Module 03: Total Quality Management 09:20 4: Module 04: Quality Control 08:57 5: Module 05: Quality Measurement and Improvement 15:30 6: Module 06: Quality Audits 10:52 7: Module 07: Six Sigma 12:59 8: Module 08: Supply Chain Management 17:01 9: CPD Certificate - Free 01:00 Who is this course for? Individuals interested in quality management concepts Employees in quality assurance departments Managers seeking to enhance quality in operations Professionals involved in supply chain management Auditors focusing on quality compliance Career path Quality Assurance Manager Quality Control Inspector Quality Auditor Supply Chain Manager Operations Manager Six Sigma Consultant Certificates Digital certificate Digital certificate - Included Reed Courses Certificate of Completion Digital certificate - Included Will be downloadable when all lectures have been completed.

Delivered Online On Demand1 hour 48 minutes

£12

Mastering Scala with Apache Spark for the Modern Data Enterprise (TTSK7520)

By Nexus Human

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.