123 Scientist courses in Cardiff delivered Live Online

This half-day course is for anyone who wants to learn how to use AI and specifically ChatGPT to improve their marketing and social media.

This module aims to develop knowledge from research activities to gain an understanding of international trade using Marketing , Social Media and how AI plays a role in International Marketing

This award introduces the critical concepts associated with AI and explores its relationship with the systems and processes that make up the digital ecosystem. It explores how AI can empower organisations to utilise Big Data through the use of Business Analysis and Machine Learning, and encourages candidates to consider a future vision of the world that is powered by AI.

Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure. In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage. Prerequisites Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions. AZ-900T00 Microsoft Azure Fundamentals DP-900T00 Microsoft Azure Data Fundamentals 1 - Introduction to data engineering on Azure What is data engineering Important data engineering concepts Data engineering in Microsoft Azure 2 - Introduction to Azure Data Lake Storage Gen2 Understand Azure Data Lake Storage Gen2 Enable Azure Data Lake Storage Gen2 in Azure Storage Compare Azure Data Lake Store to Azure Blob storage Understand the stages for processing big data Use Azure Data Lake Storage Gen2 in data analytics workloads 3 - Introduction to Azure Synapse Analytics What is Azure Synapse Analytics How Azure Synapse Analytics works When to use Azure Synapse Analytics 4 - Use Azure Synapse serverless SQL pool to query files in a data lake Understand Azure Synapse serverless SQL pool capabilities and use cases Query files using a serverless SQL pool Create external database objects 5 - Use Azure Synapse serverless SQL pools to transform data in a data lake Transform data files with the CREATE EXTERNAL TABLE AS SELECT statement Encapsulate data transformations in a stored procedure Include a data transformation stored procedure in a pipeline 6 - Create a lake database in Azure Synapse Analytics Understand lake database concepts Explore database templates Create a lake database Use a lake database 7 - Analyze data with Apache Spark in Azure Synapse Analytics Get to know Apache Spark Use Spark in Azure Synapse Analytics Analyze data with Spark Visualize data with Spark 8 - Transform data with Spark in Azure Synapse Analytics Modify and save dataframes Partition data files Transform data with SQL 9 - Use Delta Lake in Azure Synapse Analytics Understand Delta Lake Create Delta Lake tables Create catalog tables Use Delta Lake with streaming data Use Delta Lake in a SQL pool 10 - Analyze data in a relational data warehouse Design a data warehouse schema Create data warehouse tables Load data warehouse tables Query a data warehouse 11 - Load data into a relational data warehouse Load staging tables Load dimension tables Load time dimension tables Load slowly changing dimensions Load fact tables Perform post load optimization 12 - Build a data pipeline in Azure Synapse Analytics Understand pipelines in Azure Synapse Analytics Create a pipeline in Azure Synapse Studio Define data flows Run a pipeline 13 - Use Spark Notebooks in an Azure Synapse Pipeline Understand Synapse Notebooks and Pipelines Use a Synapse notebook activity in a pipeline Use parameters in a notebook 14 - Plan hybrid transactional and analytical processing using Azure Synapse Analytics Understand hybrid transactional and analytical processing patterns Describe Azure Synapse Link 15 - Implement Azure Synapse Link with Azure Cosmos DB Enable Cosmos DB account to use Azure Synapse Link Create an analytical store enabled container Create a linked service for Cosmos DB Query Cosmos DB data with Spark Query Cosmos DB with Synapse SQL 16 - Implement Azure Synapse Link for SQL What is Azure Synapse Link for SQL? Configure Azure Synapse Link for Azure SQL Database Configure Azure Synapse Link for SQL Server 2022 17 - Get started with Azure Stream Analytics Understand data streams Understand event processing Understand window functions 18 - Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics Stream ingestion scenarios Configure inputs and outputs Define a query to select, filter, and aggregate data Run a job to ingest data 19 - Visualize real-time data with Azure Stream Analytics and Power BI Use a Power BI output in Azure Stream Analytics Create a query for real-time visualization Create real-time data visualizations in Power BI 20 - Introduction to Microsoft Purview What is Microsoft Purview? How Microsoft Purview works When to use Microsoft Purview 21 - Integrate Microsoft Purview and Azure Synapse Analytics Catalog Azure Synapse Analytics data assets in Microsoft Purview Connect Microsoft Purview to an Azure Synapse Analytics workspace Search a Purview catalog in Synapse Studio Track data lineage in pipelines 22 - Explore Azure Databricks Get started with Azure Databricks Identify Azure Databricks workloads Understand key concepts 23 - Use Apache Spark in Azure Databricks Get to know Spark Create a Spark cluster Use Spark in notebooks Use Spark to work with data files Visualize data 24 - Run Azure Databricks Notebooks with Azure Data Factory Understand Azure Databricks notebooks and pipelines Create a linked service for Azure Databricks Use a Notebook activity in a pipeline Use parameters in a notebook Additional course details: Nexus Humans DP-203T00 Data Engineering on Microsoft Azure training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the DP-203T00 Data Engineering on Microsoft Azure course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Course Overview This comprehensive Data Analytics course provides an in-depth exploration of data analysis, covering the essential principles and techniques used to extract valuable insights from data. Learners will engage with core concepts, such as data mining, statistical analysis, and visualisation, enabling them to make informed decisions and drive business outcomes. By the end of the course, participants will have the skills to analyse and interpret data, apply analytical tools effectively, and present their findings clearly. This course equips learners with the necessary tools to understand and leverage data in various professional settings, adding significant value to their career prospects. Course Description The Data Analytics course covers a wide range of topics, including the fundamentals of data analysis, statistical methods, and various data visualisation techniques. Learners will explore essential tools such as Excel and specialised software, while gaining a deep understanding of how to collect, store, and process data effectively. Emphasis is placed on developing the analytical mindset required to interpret data accurately and draw actionable insights. This course is designed to ensure learners can confidently navigate the world of data analytics and apply their knowledge in diverse industries, enhancing their problem-solving and decision-making abilities. Course Modules Module 01: Introduction to the World of Data Module 02: Basics of Data Analytics Module 03: Statistics for Data Analytics Module 04: Actions Taken in the Data Analysis Process Module 05: Gathering the Right Information Module 06: Storing Data Module 07: Data Mining Module 08: Excel for Data Analytics Module 09: Tools for Data Analytics Module 10: Data-Analytic Thinking Module 11: Data Visualisation That Clearly Describes Insights Module 12: Data Visualisation Tools (See full curriculum) Who is this course for? Individuals seeking to enhance their analytical skills for data-driven decision-making. Professionals aiming to transition into data analytics or enhance their data-related roles. Beginners with an interest in understanding data and its applications across industries. Business professionals seeking to leverage data for strategic growth. Career Path Data Analyst Business Intelligence Analyst Data Scientist Market Research Analyst Operations Analyst Financial Analyst Business Analyst Data Visualisation Specialist

Enhance your knowledge in coal power plant life cycle management and flexible operations with EnergyEdge. Learn about decommissioning, preservation, repurposing, and recommissioning.

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.

his course covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom. This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language. These concepts are daily used by programmers and is your first step to working as a programmer. By the end, you'll be comfortable in programming Python code. You will have done small projects. This will serve for you as examples and samples that you can use to build larger projects.

Develop Big Data Pipelines with R, Sparklyr & Power BI Course Overview: This course offers a comprehensive exploration of building and managing big data pipelines using R, Sparklyr, and Power BI. Learners will gain valuable insight into the entire process, from setting up and installing the necessary tools to creating effective ETL pipelines, implementing machine learning techniques, and visualising data with Power BI. The course is designed to provide a strong foundation in data engineering, enabling learners to handle large datasets, optimise data workflows, and communicate insights clearly using visual tools. By the end of this course, learners will have the expertise to work with big data, manage ETL pipelines, and use Sparklyr and Power BI to drive data-driven decisions in various professional settings. Course Description: This course delves into the core concepts and techniques for managing big data using R, Sparklyr, and Power BI. It covers a range of topics including the setup and installation of necessary tools, building ETL pipelines with Sparklyr, applying machine learning models to big data, and using Power BI for creating powerful visualisations. Learners will explore how to extract, transform, and load large datasets, and will develop a strong understanding of big data architecture. They will also gain proficiency in visualising complex data and presenting findings effectively. The course is structured to enhance learners' problem-solving abilities and their competence in big data environments, equipping them with the skills needed to manage and interpret vast amounts of information. Develop Big Data Pipelines with R, Sparklyr & Power BI Curriculum: Module 01: Introduction Module 02: Setup and Installations Module 03: Building the Big Data ETL Pipeline with Sparklyr Module 04: Big Data Machine Learning with Sparklyr Module 05: Data Visualisation with Power BI (See full curriculum) Who is this course for? Individuals seeking to understand big data pipelines. Professionals aiming to expand their data engineering skills. Beginners with an interest in data analytics and big data tools. Anyone looking to enhance their ability to analyse and visualise data. Career Path: Data Engineer Data Analyst Data Scientist Business Intelligence Analyst Machine Learning Engineer Big Data Consultant

Particle Physics Course Overview This Particle Physics course offers a comprehensive introduction to the fundamental constituents of matter and the forces governing their interactions. Learners will explore the nature of elementary particles, the structure of the atomic nucleus, and the principles behind nuclear physics. The course also covers the technology behind particle accelerators and radiation detectors, providing insight into modern experimental methods. By completing this course, learners will gain a solid understanding of the Standard Model of particle physics and the key concepts that underpin contemporary physics research. This knowledge will equip students with the theoretical and analytical skills essential for further study or careers in physics, research, and related scientific fields. Course Description Delving deeply into the world of subatomic particles, this course covers the classification and properties of elementary particles, the composition and behaviour of atomic nuclei, and the foundational aspects of nuclear physics. Learners will study the design and function of particle accelerators and radiation detectors, essential tools in particle physics experiments. The course elaborates on the Standard Model, explaining its role in unifying particle interactions and forces. Through engaging lectures and detailed explanations, students will develop critical thinking and analytical skills necessary to understand cutting-edge physics. Suitable for those with a keen interest in physics, the course fosters a clear understanding of complex concepts and prepares learners for advanced academic or professional pathways within scientific research and technology sectors. Particle Physics Curriculum Module 01: Introduction to Particle Physics Module 02: Elementary Particles Module 03: The Nucleus Module 04: Nuclear Physics Module 05: Particle Accelerators Module 06: Radiation Detectors Module 07: The Standard Model (See full curriculum) Who is this course for? Individuals seeking to understand the fundamental principles of particle physics. Professionals aiming to develop their knowledge for careers in physics or scientific research. Beginners with an interest in the composition of matter and the universe. Students preparing for advanced study in physics or related scientific disciplines. Career Path Research Scientist in Particle or Nuclear Physics Laboratory Technician in Physics or Engineering Science Communicator or Educator specialising in Physics Technical Specialist in Particle Accelerator Facilities Analyst in High-Energy Physics or Related Industries