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Module 1: Introduction of Python
Module 2: Working with IPython
Module 3: Introduction to NumPy
Module 4: Working with Pandas
Module 5: Visualisation with Matplotlib
(344 remaining)
This Python Data Science Course is designed to instruct Software Developers and Data Scientists in the advanced principles of utilising Python for Data Science Applications. The following professionals can benefit from this course:
There are no formal prerequisites for attending Python Data Science Training. However, a basic understanding of programming would be beneficial.
Python is an open-source programming language known for its premier status and robust capabilities. It is also renowned for its user-friendly nature and extensive libraries tailored for data manipulation and analysis. This versatile language accommodates multiple programming paradigms, including object-oriented, functional, and structured programming.
Understanding Python for data science is crucial for professionals aiming to master the subject. Data scientists, analysts, and anyone working with large datasets benefit from the efficiency and flexibility Python offers. Proficiency in this language opens doors to advanced data manipulation techniques, aiding professionals in extracting meaningful insights and patterns from complex datasets.
This 3-day Python Data Science Training by the Knowledge Academy is designed to equip delegates with essential programming skills. Participants will explore three-dimensional function visualisation, mastering techniques such as histograms, binning, and density estimation. The course dives deep into constructing arrays, working with Python lists and custom arrays, and enhances data manipulation skills using the powerful Pandas library.
Course Objectives:
Upon completing this course, delegates will benefit from an enriched skill set in Python for data science, enabling them to tackle complex data analysis tasks with confidence. They will gain proficiency in visualising and interpreting data, ultimately enhancing their value as professionals in the rapidly evolving field of data science.
Module 1: Basic Probability Theory
Module 2: Random Variables
Module 3: Multivariate Random Variables
Module 4: Expectation
Module 5: Random Processes
Module 6: Convergence of Random Processes
Module 7: Markov Chains
Module 8: Descriptive Statistics
Module 9: Frequentist Statistics
Module 10: Bayesian Statistics
Module 11: Hypothesis Testing
Module 12: Linear Regression
(344 remaining)
This Probability and Statistics for Data Science Training Course is designed to provide foundational and practical knowledge in Probability and Statistics, which are crucial for Data Science, Machine Learning, and Data Analysis. The following are some professionals who will benefit from attending this course:
There are no prerequisites for Probability and Statistics for Data Science Course.
Probability and statistics form the foundational pillars of data science, providing the necessary tools for understanding uncertainty, variability, and making informed decisions based on data. This training course delves into the fundamental concepts of probability and statistics, emphasising their crucial role in the field of data science. Participants will explore how these concepts contribute to the extraction of meaningful insights and patterns from data.
Understanding probability and statistics is essential for professionals in the data science domain. Data scientists, analysts, and decision-makers rely on these principles to draw accurate conclusions and predictions from data. Mastery of probability allows for the quantification of uncertainty, while statistics enables the analysis of data patterns and trends.
This 2-day Probability and Statistics for Data Science Training will empower the delegates with the skills to apply probability and statistics in practical data science scenarios. They will learn key concepts such as probability distributions, hypothesis testing, and regression analysis. The course provides a comprehensive understanding of statistical methods, enabling professionals to make informed decisions and predictions based on data
Course Objectives:
Upon completion of this Data Science Course, the delegates will possess a strong foundation in probability and statistics for data science. They will be equipped with the tools and techniques needed to analyse data effectively, make informed decisions, and contribute meaningfully to data-driven projects within their organisations.
Module 1: Introduction to Text Mining
Module 2: Core Text Mining Operations
Module 3: Text Mining Pre-Processing Techniques
Module 4: Categorisation
Module 5: Introduction to Clustering
Module 6: Information Extraction (IE)
Module 7: Probabilistic Models for IE
Module 8: Pre-Processing Applications
Module 9: Presentation-Layer Considerations
Module 10: Visualisation Approaches
Module 11: Introduction to Link Analysis
(344 remaining)
This Text Mining Training Course is suitable for a wide range of professionals and individuals who want to gain expertise extracting information, sentiment, or patterns from unstructured text-based data. The following professionals can benefit from this course:
There are no formal prerequisites for attending this Text Mining Course.
Text mining is a knowledge-intensive process that is a pivotal skill in today's information-driven world. It involves interacting with text-based document collections using powerful analysis tools to uncover valuable insights and patterns within vast data sources, be it reports, articles, or social media data. In this Data Science Course, participants embark on a journey to master text mining, a crucial skill set for professionals navigating the sea of textual information.
Understanding text mining is vital for professionals in various domains, including data analysts, market researchers, content creators, and information scientists. As digital content continues to explode, the ability to extract meaningful information from text sources becomes a competitive advantage.
The 2-day Data Science Training offers a comprehensive understanding of text mining operations and preprocessing techniques. Delegates will delve into the intricacies of text categorisation, explore significant algorithms, leverage unlabelled data effectively, and evaluate text classifiers for improved accuracy. The training enhances their ability to fine-tune text mining processes through constraints and specification filters
Course Objectives:
Upon completing this course, delegates will possess a comprehensive skill set in text mining, enabling them to efficiently extract valuable insights from textual data, automate processes, and enhance decision-making in their respective professional domains.
Module 1: Introduction to Keras
Module 2: Exploring Models
Module 3: Overview of Keras Layers
Module 4: Pre-Processing
Module 5: Optimisers
(344 remaining)
The Keras Training for Data Scientists Course is tailored for data scientists and professionals who want to gain proficiency in deep learning using the Keras framework. This course is particularly beneficial for the following individuals:
There are no formal prerequisites for attending this Keras Training for Data Scientists Course. However, having some prior knowledge of machine learning concepts and Python programming can be beneficial.
Keras is an open-source neural network library that has become a crucial tool for professionals in the data-driven landscape. This course, "Keras Training for Data Scientists," delves into the intricacies of this robust framework. With a Python foundation and seamless integration with TensorFlow, CNTK, and Theano, Keras facilitates rapid experimentation and is essential for data scientists navigating the complexities of modern data science.
Proficiency in Keras is vital for diverse professionals, including data scientists, machine learning engineers, software developers, deep learning engineers, medical researchers, bioinformaticians, and data journalists. Mastering Keras empowers professionals to architect and experiment with these models efficiently as the demand for neural networks and deep learning solutions grows. Its relevance is undeniable in the dynamic landscape of data science.
This 1-day Keras Training for Data Scientists Course is designed to accommodate individuals from diverse backgrounds and industries. Participants in this will acquire comprehensive knowledge of different Keras layers, ranging from core to recurrent layers. Additionally, they will learn essential preprocessing techniques for sequences, text, and images—critical skills for effectively preparing data for neural network applications.
Course Objectives:
Upon completing this course, delegates will possess a robust understanding of Keras, enabling them to efficiently experiment with and architect diverse neural network models. The acquired skills in preprocessing for sequences, text, and images will enhance their ability to tackle complex data science problems, providing a valuable edge in the dynamic field of neural network development.
Online Instructor-led (4 days)
Online Self-paced (32 hours)
Module 1: Python for Data Analysis - NumPy
Module 2: Python for Data Analysis – Pandas
Module 3: Python for Data Visualisation – Matplotlib
Module 4: Python for Data Visualisation – Seaborn
Module 5: Machine Learning
Module 6: Natural Language Processing
Module 7: Deep Learning
Module 8: Big Data
Module 9: Working with Data in R
Module 10: Regression in R
Module 11: Modelling Data
Module 12: Shaping and Combining Data using Power BI
Module 13: Interactive Data Visualisations
(344 remaining)
This Advanced Data Science Certification Course is tailored for individuals seeking to enhance their expertise in the field of data science. This course is particularly beneficial for professionals such as:
There are no formal prerequisites for the Advanced Data Science Certification Course. However, having prior knowledge of multiple programming languages will be beneficial for the delegates.
In today's data-driven world, the demand for professionals with expertise in data science is soaring, and this Advanced Data Science Certification stands as a beacon for individuals seeking to navigate the intricate landscape of data science. Covering critical topics such as NumPy arrays, regression analysis, machine learning mathematics, and more, this course provides a robust foundation for those eager to harness the power of data.
Business analysts, data engineers, software developers, and even executives can benefit significantly from mastering data science. In an era where every bit of data holds invaluable insights, those well-versed in data science possess a distinct advantage. They can unravel complex patterns, make informed decisions, and drive innovation.
This 4-day Advanced Data Science Certification Course is designed to empower delegates with practical knowledge and hands-on skills. It delves into essential concepts such as working with time series, three-dimensional plotting, data analytics lifecycle phases, and data manipulation using Power BI. By the end of this training, delegates will emerge as adept data scientists capable of working with data at expert-level.
Course Objectives:
Upon completion of this course, delegates will not only possess a solid foundation in data science but also practical skills in tools like Pandas and Power BI. This empowers them to analyse data effectively, make informed decisions, and contribute meaningfully to their organisations' success, providing a distinct advantage in today's data-centric professional landscape.
Module 1: Introduction and Installation
Module 2: Getting Started with Pandas
Module 3: User Guide
Module 4: Pandas Ecosystem
Module 5: Development Phase
(344 remaining)
The Pandas for Data Analysis Course aims at equipping learners with the knowledge and skills to manipulate, analyse, and visualise data using the Pandas library in Python. The following professionals can benefit greatly with this course:
There are no formal prerequisites to attend Pandas for Data Analysis Course. However, a basic knowledge of programming can be beneficial in this Data Science Course.
In the realm of data science, Pandas stands as a beacon of efficiency and versatility. This open-source Python library offers high-performance tools for data analysis and data structure manipulation. With its extensive capabilities, Pandas has become indispensable in various fields, including statistics, economics, and analytics.
Data scientists, analysts, economists, and anyone dealing with data in Python can greatly benefit from mastering this versatile library. Pandas simplifies data manipulation tasks, allowing professionals to clean, transform, and analyse data efficiently. In an era where data quality and speed are essential, Pandas empowers professionals to make informed decisions, extract valuable insights, and drive business success.
Over the course of this intensive 2-day training by the Knowledge Academy, delegates will embark on a comprehensive journey through Pandas and its data structures. They will gain a deep understanding of how to work with various data types, including text, missing, and categorical data, enabling them to handle diverse datasets effectively. Delegates will also explore essential Pandas operations such as merging, joining, concatenating, reshaping, etc.
Course Objectives:
Upon completion of this course, delegates will benefit from enhanced proficiency in data analysis using Pandas. They will be equipped with the skills to efficiently manipulate diverse datasets, perform essential Pandas operations, and utilise data visualisation techniques. This training will empower participants to apply Pandas effectively in their roles, making data-driven decisions with confidence and contributing to the success of their organisations.
Module 1: Introduction to Predictive Analytics
Module 2: Setting Up the Problem
Module 3: Understanding the Data
Module 4: Data Preparation
Module 5: Itemsets and Association Rules
Module 6: Descriptive Modelling
Module 7: Interpreting Descriptive Models
Module 8: Predictive Modelling
Module 9: Predictive Models Assessment
Module 10: Model Ensembles
Module 11: Text Mining
Module 12: Predictive Model Deployment
(344 remaining)
This Predictive Analytics Course aims to provide professionals the core concepts, techniques, and tools used to predict future events based on historical data and delves into the various stages of the analytics life cycle. This course can help various professionals, including:
There are no formal prerequisites required for attending this Predictive Analytics Course.
Predictive Analytics is a powerful tool in the Data Science domain that plays a crucial role in foreseeing unknown future events that organisations must navigate. Utilising techniques like data mining, modelling, statistics, artificial intelligence, and machine learning, predictive analytics empowers organisations to identify forthcoming risks and opportunities by deciphering patterns within transactional and historical data.
Proficiency in Predictive Analytics is indispensable for a range of professionals, including Data Scientists, Business Analysts, Marketing Experts, Financial Analysts, and decision-makers in diverse industries. As organisations seek a competitive edge, Predictive Analytics offers a strategic advantage, allowing professionals to anticipate market trends, customer behaviours, and operational efficiencies.
This 2-day Predictive Analytics Course provides delegates with knowledge on essential aspects like data cleaning, feature creation, item sets, and association rules. This Data Science Training equips participants with the knowledge to uncover valuable patterns and associations within data. Regardless of their field, individuals keen on augmenting their data science skill set will benefit from this training, gaining familiarity with predictive modelling techniques.
Course Objectives:
By the end of this Predictive Analytics Training, the delegates will possess the skills to develop and apply predictive models effectively, enabling them to make data-driven predictions, optimise decision-making processes, and unlock new avenues for organisational growth.
Module 1: Installing KNIME Analytics Platform
Module 2: Introduction to KNIME Analytics Platform
Module 3: KNIME Workbench
Module 4: KNIME Flow Control
Module 5: KNIME Extensions and Integrations
Module 6: CSS Styling for JavaScript Views and QuickForm Nodes
Module 7: Creating New KNIME Extension
(344 remaining)
The Introduction to Knime Analytics Platform Course is designed to provide an in-depth understanding of the Knime platform, a leading open-source data analytics, reporting, and integration tool. This course can be beneficial for various professionals, including:
There are no formal prerequisites required for attending this Introduction to Knime Analytics Platform Training Course.
In the realm of data science, Knime Analytics Platform shines as an open-source software that empowers individuals to create data science applications and services seamlessly. It's the key to unravelling the power of data, enabling the understanding of data intricacies, designing data science workflows, and building reusable components accessible to everyone.
Proficiency in the Knime Analytics Platform is a significant asset for a broad spectrum of professionals. Data scientists, data analysts, business intelligence professionals, and anyone dealing with data in various industries can greatly benefit from mastering Knime. With the exponential growth of data, the ability to create visual workflows and perform data analysis without coding is a game-changer.
In this intensive 1-day training course, delegates will embark on a comprehensive journey through the Knime Analytics Platform. They will gain practical knowledge on how to install and update the platform and its extensions, ensuring they are up to date with the latest tools. Delegates will become well-versed in navigating the Knime workbench and handling Knime tables, essential for data manipulation and analysis.
Course Objectives:
Upon completion of this course, delegates will possess the proficiency to create data science applications effortlessly using the Knime Analytics Platform. They will be equipped with the skills to navigate, manipulate, and analyse data efficiently, fostering the ability to automate data processes.
Module 1: Getting Started with Data Mining
Module 2: Data Preprocessing
Module 3: Data Warehousing and Online Analytical Processing
Module 4: Data Cube
Module 5: Mining Frequent Patterns, Associations, and Correlations
Module 6: Advanced Pattern Mining
Module 7: Classification
Module 8: Advanced Methods of Classification
Module 9: Cluster Analysis
Module 10: Advanced Cluster Analysis
Module 11: Outlier Detection
(344 remaining)
The Data Mining Course is tailored to impart knowledge on the process of extracting patterns, correlations, and information from large amounts of data stored in databases, data warehouses, or other information repositories. This course can be beneficial for various professionals aiming to derive insights from data, including:
There are no formal prerequisites required for attending this Data Mining Course. Although, a prior understanding of IT concepts can be helpful.
Data mining is the method of detecting patterns in large data sets by making use of statistics, machine learning and database systems. It includes analysing large amounts of data and converting it into useful information. The insights gained from data mining can be used for fraud detection, marketing, scientific discovery, etc.
This Data Science Training course will provide delegates with extensive knowledge on data mining. This course will cover the main concepts of data mining, including data objects, data visualisation, measuring data similarity, and data preprocessing. Delegates will also learn about data transformation and data discretisation. Data warehousing and online analytical processing will also be crucial concepts of this course, including basic data warehousing concepts, data cube, and OLAP.
In addition, this 2-day training course will cover mining frequent patterns, associations, and correlations including pattern evaluation methods. Delegates will acquire knowledge on advanced pattern mining that comprises constraint-based frequent pattern mining, mining high-dimensional data and colossal patterns, and pattern exploration and application. By the end of this course, delegates will have gained comprehensive knowledge on classification methods, cluster analysis, and outlier detection.
Course Objectives:
Upon completing this Data Mining Training Course, delegates will gain advanced skills in extracting valuable insights from large datasets, enhancing their ability to make informed business decisions. Additionally, the course equips them with practical knowledge of cutting-edge data mining techniques, positioning them for career advancement in the rapidly evolving field of data analytics.
Module 1: Introduction to Geographic Information Systems (GIS)
Module 2: Basics of ArcGIS
Module 3: Making Maps with Common Datasets
Module 4: Retrieving and Sharing Data
(344 remaining)
The GIS Development Course offers professionals the knowledge of the concepts, tools, and techniques necessary for developing Geographic Information Systems (GISs). This course can be beneficial for various professionals, including:
There are no formal prerequisites required for attending this GIS Development Training Course.
In our increasingly interconnected world, Geographic Information Systems (GIS) have become the bedrock of data-driven decision-making. GIS is more than just a mapping tool; it's a powerful framework that enables the collection, management, analysis, and visualisation of spatial data. By integrating various data types and harnessing the power of spatial location, GIS transforms layers of information into insightful maps and 3D scenes.
Proficiency in GIS is invaluable for professionals across diverse domains. Urban planners, environmental scientists, data analysts, disaster management experts, and anyone dealing with location-based data can benefit significantly from mastering GIS. In a world where location data plays a pivotal role, GIS empowers professionals to make informed decisions, optimise resource allocation, and visualise complex information in a spatial context.
In this intensive 1-day GIS Development Training course, delegates will embark on a journey through the fundamentals of Geographic Information Systems. They will explore the realm of spatial analysis and cartography, gaining insights into the core concepts of GIS. Delegates will become familiar with ArcGIS, the leading software tool in the GIS domain, and understand how GIS has evolved from traditional paper maps to globally integrated electronic software packages.
Course Objectives:
Upon completion of this course, delegates will possess a solid understanding of GIS fundamentals and proficiency in using ArcGIS, the industry-leading software. They will be able to analyse spatial data, create insightful maps, and leverage GIS capabilities for informed decision-making.
Module 1: Introduction to Decision Tree
Module 2: Overview of R Programming
Module 3: Data Treatment Before Modelling
Module 4: Classification of Tree Development and Algorithm Details
Module 5: Decision Tree Analysis in Project Management
Module 6: Regression Tree and Auto Pruning
Module 7: CHAID Algorithm
Module 8: Other Algorithms
(344 remaining)
The Decision Tree Modelling Using R Training Course offers a detailed exploration of decision tree models, one of the most widely used algorithms in Machine Learning and Data Science. This course can be beneficial for various professionals, including:
There are no formal prerequisites for attending this Decision Tree Modelling Using R Training Course. However, having some knowledge of the R programming language will be helpful.
Decision Tree Modelling Using R is a formidable analytical technique with wide-ranging applications across diverse industries, including finance, automotive, and telecommunications. It serves as a powerful tool for making data-driven decisions, enabling businesses to navigate complex scenarios and optimise outcomes.
Proficiency in Decision Tree Modelling Using R is essential for professionals seeking to enhance their data science skills and make informed decisions rooted in data analysis. Data scientists, business analysts, financial experts, and professionals in various industries can greatly benefit from mastering this technique. In an era where data-driven decision-making is paramount, Decision Tree Modelling equips professionals with the ability to dissect data, identify patterns, and derive actionable insights.
This intensive 1-day Decision Tree Modelling Using R Certification course immerses delegates in a comprehensive exploration of Decision Tree Modelling concepts. They will embark on a journey starting from the fundamentals of Decision Trees, progressing to advanced topics such as data design for modelling, algorithm details, industry best practices, validation techniques, and practical applications using the R programming language.
Course Objectives:
By the end of this course, delegates will emerge with expertise in Decision Tree Modelling using R, empowering them to leverage this powerful tool for data analysis and informed decision-making.
Module 1: Introduction to PySpark
Module 2: Installation
Module 3: DataFrame
Module 4: Setting Up a Spark Virtual Environment
Module 5: Building Batch and Streaming Apps with Spark
Module 6: Learning from Data Using Spark
(344 remaining)
This PySpark Course covers the fundamentals of Spark, its architecture, and how to use the PySpark API for Data Processing, Analytics, and Machine Learning tasks. This course can be beneficial for various professionals, including:
There are no formal prerequisites required for attending this PySpark Course.
PySpark Course is a crucial component in the arsenal of data scientists, business analysts, and professionals across various industries. PySpark, a Python API for Apache Spark, is a powerful framework for Big Data processing and analytics. Its relevance lies in its ability to handle large-scale data processing tasks efficiently, making it an essential skill for those navigating the dynamic landscape of data science.
Professionals aiming to master PySpark include Data Scientists, Data Engineers, and Analysts dealing with Big Data. In an era where large datasets are the norm, the capability to leverage PySpark for data processing, machine learning, and analytics is paramount. This course is tailored to empower individuals with the skills needed to harness the potential of PySpark, making it an indispensable asset for professionals seeking to stay ahead in this domain.
This 1-day training by the Knowledge Academy provides delegates with a deep dive into PySpark, covering fundamentals, advanced topics, and practical applications. From understanding the basics of PySpark to exploring its capabilities in Big Data analytics, delegates will gain hands-on experience. This training aims to equip professionals with the knowledge and skills needed to efficiently process large-scale data using PySpark, enabling them to make informed decisions and contribute effectively to data-driven initiatives in their respective fields.
Course Objectives:
Upon completion of this course, the delegates will possess the skills to effectively utilise PySpark for Big Data processing and analytics. They will have hands-on experience in applying PySpark for machine learning applications, enhancing their proficiency in handling large-scale data tasks.
Module 1: Introduction to R
Module 2: Data Structures in R
Module 3: Working with Data in R
Module 4: Data Manipulation in R
Module 5: Data Visualisations in R
Module 6: Statistics in R
Module 7: Machine Learning
(344 remaining)
The Data Science with R Training Course is designed for professionals, analysts, and individuals who are interested in acquiring skills and knowledge in data analysis, visualisation, and modeling using the R programming language. This course is beneficial for various professionals including:
There are no formal prerequisites for attending this Data Science with R Training Course.
Data Science and the R programming language collaborate seamlessly to dissect and manipulate data. R serves as the preferred programming language for data scientists, enabling them to preprocess data, construct predictive models, perform statistical analyses, and craft insightful data visualisations. This training equips individuals with the skills to leverage the R language effectively, transforming raw data into valuable insights and actionable recommendations.
Mastering Data Science with R enables effective data preprocessing, statistical analysis, and predictive modelling. This proficiency is especially valuable for data scientists, analysts, and researchers working with complex datasets. The versatility of R empowers professionals to extract meaningful insights and contribute to data-driven decision-making in various industries, making it an essential skill set for career advancement.
The Knowledge Academy's 2-day Data Science with R Training course delivers comprehensive knowledge of R programming and its mastery. Participants will delve into working with vectors, lists, metrics, arrays, and data frames, in addition to acquiring expertise in data manipulation, the art of refining and adapting data for enhanced analytical capabilities.
Course Objectives:
Upon completion of this Data Science with R Training, delegates will possess comprehensive knowledge and mastery of the R programming language. They will be equipped with the skills to preprocess data, construct predictive models, and perform statistical analyses, enhancing their data science capabilities.
Module 1: Introduction to Data Science
Module 2: Blockchain Overview
Module 3: Implications of Blockchain in Data Science
Module 4: Blockchain in Big Data Transformation
Module 5: Blockchain Storage
(344 remaining)
The Data Science and Blockchain Training Course is designed for those who want to get better at understanding the intricacies surrounding Data Science and Blockchain for complex problem-solving and application development. The Data Science and Blockchain Training Course can benefit individuals such as:
There are no formal prerequisites for this Data Science and Blockchain Training Course.
Data science stands as the discipline dedicated to extracting valuable insights from data to inform business decisions, strategic planning, and a myriad of other purposes. It relies on cutting-edge analytical techniques and scientific principles to achieve its objectives. In parallel, blockchain technology emerges as a digital ledger system for transactions, fortified by encryption and digital signatures that ensure their integrity and authenticity.
Mastery of Data Science and Blockchain is vital for technology, data science, and blockchain development professionals. Proficiency in these domains is particularly valuable in the dynamic landscape of data-driven industries. It empowers professionals to contribute innovatively, making it a crucial skill set for career growth and leadership roles. Integrating data science and blockchain expertise positions individuals at the front of technological advancements and industry demands.
The 1-day Data Science and Blockchain Training course is meticulously crafted to provide delegates with a deep understanding of blockchain's pivotal role in empowering data scientists to address a wide array of intriguing challenges. Throughout this training course, participants will delve into the roles and responsibilities integral to a data scientist's domain, mastering various modeling techniques to unlock blockchain's solutions and applications.
Course Objectives:
Upon completion of this 1-day course, delegates will gain a deep understanding of the synergies between data science and blockchain technology. They will be equipped with practical skills to navigate complex data segments, predict outcomes, and unlock blockchain's solutions. The training enhances career prospects by providing essential knowledge and experience in roles integral to a data scientist's domain.
Speak to a training expert for advice if you are unsure of what course is right for you. Give us a call on +44 1344 203 999 or Enquire.
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Resources are included for a comprehensive learning experience.
"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"
Joshua Davies, Thames Water