92 Scientist courses delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. Prerequisites Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services. Specifically: * Experience using computers and the internet. * Interest in use cases for AI applications and machine learning models. * A willingness to learn through hands-on exp... 1 - FUNDAMENTAL AI CONCEPTS * Understand machine learning * Understand computer vision * Understand natural language processing * Understand document intelligence and knowledge mining * Understand generative AI * Challenges and risks with AI * Understand Responsible AI 2 - FUNDAMENTALS OF MACHINE LEARNING * What is machine learning? * Types of machine learning * Regression * Binary classification * Multiclass classification * Clustering * Deep learning * Azure Machine Learning 3 - FUNDAMENTALS OF AZURE AI SERVICES * AI services on the Azure platform * Create Azure AI service resources * Use Azure AI services * Understand authentication for Azure AI services 4 - FUNDAMENTALS OF COMPUTER VISION * Images and image processing * Machine learning for computer vision * Azure AI Vision 5 - FUNDAMENTALS OF FACIAL RECOGNITION * Understand Face analysis * Get started with Face analysis on Azure 6 - FUNDAMENTALS OF OPTICAL CHARACTER RECOGNITION * Get started with Vision Studio on Azure 7 - FUNDAMENTALS OF TEXT ANALYSIS WITH THE LANGUAGE SERVICE * Understand Text Analytics * Get started with text analysis 8 - FUNDAMENTALS OF QUESTION ANSWERING WITH THE LANGUAGE SERVICE * Understand question answering * Get started with the Language service and Azure Bot Service 9 - FUNDAMENTALS OF CONVERSATIONAL LANGUAGE UNDERSTANDING * Describe conversational language understanding * Get started with conversational language understanding in Azure 10 - FUNDAMENTALS OF AZURE AI SPEECH * Understand speech recognition and synthesis * Get started with speech on Azure 11 - FUNDAMENTALS OF AZURE AI DOCUMENT INTELLIGENCE * Explore capabilities of document intelligence * Get started with receipt analysis on Azure 12 - FUNDAMENTALS OF KNOWLEDGE MINING WITH AZURE COGNITIVE SEARCH * What is Azure Cognitive Search? * Identify elements of a search solution * Use a skillset to define an enrichment pipeline * Understand indexes * Use an indexer to build an index * Persist enriched data in a knowledge store * Create an index in the Azure portal * Query data in an Azure Cognitive Search index 13 - FUNDAMENTALS OF GENERATIVE AI * What is generative AI? * Large language models * What is Azure OpenAI? * What are copilots? * Improve generative AI responses with prompt engineering 14 - FUNDAMENTALS OF AZURE OPENAI SERVICE * What is generative AI * Describe Azure OpenAI * How to use Azure OpenAI * Understand OpenAI's natural language capabilities * Understand OpenAI code generation capabilities * Understand OpenAI's image generation capabilities * Describe Azure OpenAI's access and responsible AI policies 15 - FUNDAMENTALS OF RESPONSIBLE GENERATIVE AI * Plan a responsible generative AI solution * Identify potential harms * Measure potential harms * Mitigate potential harms * Operate a responsible generative AI solution ADDITIONAL COURSE DETAILS: Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals 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 AI-900T00 - Microsoft Azure AI Fundamentals 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.

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. -------------------------------------------------------------------------------- Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: * We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project * We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.  Course Outline: Supervised Machine Learning: * Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine * Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: * Clustering Algorithms: K-means clustering, Hierarchical Clustering * Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) * Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: * Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting * Reinforcement learning Algorithms: Q-Learning * Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: * The first part of a Machine Learning project understands the data and the problem at hand. * Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: * Python Machine Learning Certificate on completion   * Python Machine Learning notes * Practical Python Machine Learning exercises and code examples * After the course, 1 free, online session for questions or revision Python Machine Learning. * Max group size on this Python Machine Learning is 4. -------------------------------------------------------------------------------- REFUND POLICY No Refunds

Duration 1 Days 6 CPD hours This course is intended for The audience for this course includes software developers and data scientists who need to use large language models for generative AI. Some programming experience is recommended, but the course will be valuable to anyone seeking to understand how the Azure OpenAI service can be used to implement generative AI solutions. Note Generative AI is a fast-evolving field of artificial intelligence, and the Azure OpenAI service is subject to frequent changes. The course materials are maintained to reflect the latest version of the service at the time of writing. Azure OpenAI Service provides access to OpenAI's powerful large language models such as GPT; the model behind the popular ChatGPT service. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio. In this course, you'll learn how to provision Azure OpenAI service, deploy models, and use them in generative AI applications. Prerequisites * Familiarity with Azure and the Azure portal. * Experience programming with C# or Python. 1 - GET STARTED WITH AZURE OPENAI SERVICE * Access Azure OpenAI Service * Use Azure OpenAI Studio * Explore types of generative AI models * Deploy generative AI models * Use prompts to get completions from models * Test models in Azure OpenAI Studio's playgrounds 2 - BUILD NATURAL LANGUAGE SOLUTIONS WITH AZURE OPENAI SERVICE * Integrate Azure OpenAI into your app * Use Azure OpenAI REST API * Use Azure OpenAI SDK 3 - APPLY PROMPT ENGINEERING WITH AZURE OPENAI SERVICE * Understand prompt engineering * Write more effective prompts * Provide context to improve accuracy 4 - GENERATE CODE WITH AZURE OPENAI SERVICE * Construct code from natural language * Complete code and assist the development process * Fix bugs and improve your code 5 - GENERATE IMAGES WITH AZURE OPENAI SERVICE * What is DALL-E? * Explore DALL-E in Azure OpenAI Studio * Use the Azure OpenAI REST API to consume DALL-E models 6 - USE YOUR OWN DATA WITH AZURE OPENAI SERVICE * Understand how to use your own data * Add your own data source * Chat with your model using your own data ADDITIONAL COURSE DETAILS: Nexus Humans AI-050T00: Develop Generative AI Solutions with Azure OpenAI Service 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 AI-050T00: Develop Generative AI Solutions with Azure OpenAI Service 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.

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.

Duration 2 Days 12 CPD hours Overview Identify and configure basic functions of Tableau. Connect to data sources, import data into Tableau, and save Tableau files Create views and customize data in visualizations. Manage, sort, and group data. Save and share data sources and workbooks. Filter data in views. Customize visualizations with annotations, highlights, and advanced features. Create and enhance dashboards in Tableau. Create and enhance stories in Tableau As technology progresses and becomes more interwoven with our businesses and lives, more and more data is collected about business and personal activities. This era of "big data" has exploded due to the rise of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantage. The creation of data-backed visualizations is a key way data scientists, or any professional, can explore, analyze, and report insights and trends from data. Tableau© software is designed for this purpose. Tableau was built to connect to a wide range of data sources and allows users to quickly create visualizations of connected data to gain insights, show trends, and create reports. Tableau's data connection capabilities and visualization features go far beyond those that can be found in spreadsheets, allowing users to create compelling and interactive worksheets, dashboards, and stories that bring data to life and turn data into thoughtful action. Prerequisites To ensure your success in this course, you should have experience managing data with Microsoft© Excel© or Google Sheets?. LESSON 1: TABLEAU FUNDAMENTALS * Topic A: Overview of Tableau * Topic B: Navigate and Configure Tableau LESSON 2: CONNECTING TO AND PREPARING DATA * Topic A: Connect to Data * Topic B: Build a Data Model * Topic C: Save Workbook Files * Topic D: Prepare Data for Analysis LESSON 3: EXPLORING DATA * Topic A: Create Views * Topic B: Customize Data in Visualizations LESSON 4: MANAGING, SORTING, AND GROUPING DATA * Topic A: Adjust Fields * Topic B: Sort Data * Topic C: Group Data LESSON 5: SAVING, PUBLISHING, AND SHARING DATA * Topic A: Save Data Sources * Topic B: Publish Data Sources and Visualizations * Topic C: Share Workbooks for Collaboration LESSON 6: FILTERING DATA * Topic A: Configure Worksheet Filters * Topic B: Apply Advanced Filter Options * Topic C: Create Interactive Filters LESSON 7: CUSTOMIZING VISUALIZATIONS * Topic A: Format and Annotate Views * Topic B: Emphasize Data in Visualizations * Topic C: Create Animated Workbooks * Topic D: Best Practices for Visual Design LESSON 8: CREATING DASHBOARDS IN TABLEAU * Topic A: Create Dashboards * Topic B: Enhance Dashboards with Actions * Topic C: Create Mobile Dashboards LESSON 9: CREATING STORIES IN TABLEAU * Topic A: Create Stories * Topic B: Enhance Stories with Tooltips

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. OVERVIEW OF DATA SCIENCE AND MACHINE LEARNING AT SCALE OVERVIEW OF THE HADOOP ECOSYSTEM WORKING WITH HDFS DATA AND HIVE TABLES USING HUE INTRODUCTION TO CLOUDERA DATA SCIENCE WORKBENCH OVERVIEW OF APACHE SPARK 2 READING AND WRITING DATA INSPECTING DATA QUALITY CLEANSING AND TRANSFORMING DATA SUMMARIZING AND GROUPING DATA COMBINING, SPLITTING, AND RESHAPING DATA EXPLORING DATA CONFIGURING, MONITORING, AND TROUBLESHOOTING SPARK APPLICATIONS OVERVIEW OF MACHINE LEARNING IN SPARK MLLIB EXTRACTING, TRANSFORMING, AND SELECTING FEATURES BUILDING AND EVAUATING REGRESSION MODELS BUILDING AND EVALUATING CLASSIFICATION MODELS BUILDING AND EVALUATING CLUSTERING MODELS CROSS-VALIDATING MODELS AND TUNING HYPERPARAMETERS BUILDING MACHINE LEARNING PIPELINES DEPLOYING MACHINE LEARNING MODELS ADDITIONAL COURSE DETAILS: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.

Duration 1 Days 6 CPD hours This course is intended for This course is primarily designed for business leaders, consultants, product and project managers, and other decision makers who are interested in unlocking new business opportunities and augmenting existing business processes using generative AI. It's also a great starting point for any business professionals who want to investigate generative AI solutions for their own individual tasks and workflows. This course is also designed to assist students in preparing for the CertNexus GenAIBIZ (Exam GAZ-110) credential. Overview In this course, you will identify ways in which generative AI can bring significant value to the organization. You will: Describe the fundamentals of AI and generative AI. Generate text using AI. Generate code using AI. Generate images and video using AI. Generate audio using AI. Identify the challenges of generative AI. Implement organizational strategies for generative AI. This course is designed to demystify generative AI for business professionals, as well as to trace its power to actionable, real-world business goals. It will give you the essential knowledge of generative AI you'll need to elevate the organization in these exciting times. GenAIBIZÿoffers business professionals with the ability to describe the fundamentals of AI and generative AI; generate text, code, images, video, and audio using generative AI; identify the challenges of generative AI; and implement organizational strategies for generative AI. Exam voucher is included.This course may earn you a Credly badge. LESSON 1: AI FUNDAMENTALS * Topic A: AI Concepts * Topic B: Generative AI Concepts LESSON 2: GENERATING TEXT USING AI * Topic A: Identify Text Generation Concepts * Topic B: Solve Business Problems Using Text Generation LESSON 3: GENERATING CODE USING AI * Topic A: Identify Code Generation Concepts * Topic B: Solve Business Problems Using Code Generation LESSON 4: GENERATING IMAGES AND VIDEO USING AI * Topic A: Identify Image and Video Generation Concepts * Topic B: Solve Business Problems Using Image and Video Generation LESSON 5: GENERATING AUDIO USING AI * Topic A: Identify Audio Generation Concepts * Topic B: Solve Business Problems Using Audio Generation LESSON 6: IDENTIFYING CHALLENGES OF GENERATIVE AI * Topic A: Identify Shortcomings of Generative AI * Topic B: Identify Ethical Risks of Generative AI * Topic C: Identify Business Concerns of Generative AI LESSON 7: IMPLEMENTING BUSINESS STRATEGIES FOR GENERATIVE AI * Topic A: Apply Best Practices for Generative AI in the Organization * Topic B: Evaluate the Results of Generative AI Projects

COURSE INFORMATION This highly interactive course will provide guidance on why and how to implement a quality system successfully into the research laboratory. By doing so, you will position your innovation for the success it deserves. But leave things as they are and there is a good chance that your science will not realise its full potential should success, and its consequences, come your way. A quality system in your research laboratory is the most effective and efficient way to: * Help scientists work more efficiently * Ensure discoveries can be defended * Protect the value of intellectual property. This course is particularly aimed at those working in early phase research environments which are not constrained by the regulatory requirements of the Good Practice regulations but are producing intellectual property, testing and/or products for the therapeutic market. For organisational reasons, rather than regulatory ones, this is a place where you need to get it right. The programme is delivered by leaders in the field who, quite simply, ‘have done it’. Whether delegates are at senior management level seeking strategic direction, a laboratory head wishing to deliver science that will stand the test of time or a quality professional thrown in at the deep end, this course will provide key insight and practical guidance to underpin future success. Based on risk based systems, tried and tested over many years in the workplace, the programme will help delegates to define, train, implement and monitor the quality of their research, irrespective of field or discipline. Delegates will learn how to help position their organisation for success. COURSE CONTENT: Delegates will be guided thoughtfully through each key component of the process in a stimulating learning environment. The course probes all avenues of the research quality arena, from an initial understanding of the cultural aspects of the scientific discovery environment, to managing quality in outsourced research programmes. Computer systems and e-data security in the research environment will be discussed and pragmatic solutions described to help manage the ballooning cloud of e-data. In addition, the ever blurring boundary between the regulated and non-regulated research environments will be discussed and delegates given perspective on future developments in the field. With this knowledge, delegates will be able to get it ‘right first time’. IS THIS COURSE FOR YOU? The course is designed for all those involved in the research laboratory quality arena and it has been tailored to meet the needs of scientific management, bench scientists and quality professionals alike. Delegates get immediate access to highly experienced tutors who will share their wisdom and insights in an area where few others have been successful. The course is linked with the RQA guidance which builds on years of experience and forms the foundation of the programme. -------------------------------------------------------------------------------- TUTORS Tutors will be comprised of (click the photos for biographies): LOUISE HANDY Director, Handy Consulting Ltd SANDRINE BONGIOVANNI Associate Director in Research and Quality Compliance, Novartis -------------------------------------------------------------------------------- PROGRAMME Please note timings may be subject to alteration. DAY 1 09:00 REGISTRATION 09:10 WELCOME AND INTRODUCTIONS 09:20 HISTORY AND OVERVIEW OF THE FIELD Examples of business and regulatory risks and the consequences of low quality in research. A look at the standards and guidelines that exist. 10:00 THE CULTURE, THE POLITICS AND THE SCIENTIST'S PERSPECTIVE Understanding research environments, the drivers and the challenges. 10:30 BREAK 10:45 WORKSHOP - RISK MANAGEMENT Thinking about risk management and prioritisation. Looking at the critical factors for the implementations of a successful quality system. 12:15 WORKSHOP - FEEDBACK 12:45 LUNCH 13:45 PERSONNEL, PLANS, PROCEDURES, FACILITIES, EQUIPMENT, MATERIALS AND REAGENTS Looking at planning the work, defining procedures in a way which promotes robust science without compromising brilliance and ensuring that all these elements are demonstrably fit for their intended purpose. 14:30 WORKSHOP - ASSAY VALIDATION How much validation is required at what stage? What do we need to validate an assay? 15:00 WORKSHOP - FEEDBACK 15:15 RESEARCH, WORK RECORDS, ARCHIVES AND RESEARCH REVIEW Data and records which are accurate, attributable, legally attestable and safe to permit reconstruction experiments and studies. Looking at aspects of the work where there is a chance to review, correct or improve the science, the data and the processes. 16:15 CONTINUAL IMPROVEMENT AND QUALITY SYSTEMS Reviewing implementation of a quality system, finding opportunities for improvement, understanding culture change. 16:45 QUESTIONS AND ANSWERS 17:00 CLOSE OF COURSE -------------------------------------------------------------------------------- EXTRA INFORMATION COURSE MATERIAL This course will be run completely online. You will receive an email with a link to our online system, which will house your licensed course materials and access to the remote event. Please note this course will run in UK timezone. THE ADVANTAGES OF THIS INCLUDE:   * Ability for delegates to keep material on a mobile device * Ability to review material at any time pre and post course * Environmental benefits – less paper being used per course Access to an online course group to enhance networking. You will need a stable internet connection, a microphone and a webcam. CPD POINTS 7 Points   DEVELOPMENT LEVEL Develop

Duration 2 Days 12 CPD hours This course is intended for This course can be valuable for anyone who needs to write basic SQL or Transact-SQL queries. This includes anyone working with data as a data analyst, a data engineer, a data scientist, a database administrator or a database developer. It can also be useful for others peripherally involved with data, or wanting to learn more about working with data such as solution architects, students and technology managers. This course will teach the basics of Microsoft's dialect of the standard SQL language: Transact-SQL. Topics include both querying and modifying data in relational databases that are hosted in Microsoft SQL Server-based database systems, including: Microsoft SQL Server, Azure SQL Database and, Azure Synapse Analytics. 1 - INTRODUCTION TO TRANSACT-SQL * Work with schemas * Explore the structure of SQL statements * Examine the SELECT statement * Work with data types * Handle NULLs 2 - SORT AND FILTER RESULTS IN T-SQL * Sort your results * Limit the sorted results * Page results * Remove duplicates * Filter data with predicates 3 - COMBINE MULTIPLE TABLES WITH JOINS IN T-SQL * Understand joins concepts and syntax * Use inner joins * Use outer joins * Use cross joins * Use self joins 4 - WRITE SUBQUERIES IN T-SQL * Understand subqueries * Use scalar or multi-valued subqueries * Use self-contained or correlated subqueries 5 - USE BUILT-IN FUNCTIONS AND GROUP BY IN TRANSACT-SQL * Categorize built-in functions * Use scalar functions * Use ranking and rowset functions * Use aggregate functions * Summarize data with GROUP BY * Filter groups with HAVING 6 - MODIFY DATA WITH T-SQL * Insert data * Generate automatic values * Update data * Delete data * Merge data based on multiple tables ADDITIONAL COURSE DETAILS: Nexus Humans DP-080T00 Querying Data with Microsoft Transact-SQL 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-080T00 Querying Data with Microsoft Transact-SQL 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.

Duration 1 Days 6 CPD hours This course is intended for The primary audience for this course is data professionals who are familiar with data modeling, extraction, and analytics. It is designed for professionals who are interested in gaining knowledge about Lakehouse architecture, the Microsoft Fabric platform, and how to enable end-to-end analytics using these technologies. Job role: Data Analyst, Data Engineer, Data Scientist Overview Describe end-to-end analytics in Microsoft Fabric Describe core features and capabilities of lakehouses in Microsoft Fabric Create a lakehouse Ingest data into files and tables in a lakehouse Query lakehouse tables with SQL Configure Spark in a Microsoft Fabric workspace Identify suitable scenarios for Spark notebooks and Spark jobs Use Spark dataframes to analyze and transform data Use Spark SQL to query data in tables and views Visualize data in a Spark notebook Understand Delta Lake and delta tables in Microsoft Fabric Create and manage delta tables using Spark Use Spark to query and transform data in delta tables Use delta tables with Spark structured streaming Describe Dataflow (Gen2) capabilities in Microsoft Fabric Create Dataflow (Gen2) solutions to ingest and transform data Include a Dataflow (Gen2) in a pipeline This course is designed to build your foundational skills in data engineering on Microsoft Fabric, focusing on the Lakehouse concept. This course will explore the powerful capabilities of Apache Spark for distributed data processing and the essential techniques for efficient data management, versioning, and reliability by working with Delta Lake tables. This course will also explore data ingestion and orchestration using Dataflows Gen2 and Data Factory pipelines. This course includes a combination of lectures and hands-on exercises that will prepare you to work with lakehouses in Microsoft Fabric. INTRODUCTION TO END-TO-END ANALYTICS USING MICROSOFT FABRIC * Explore end-to-end analytics with Microsoft Fabric * Data teams and Microsoft Fabric * Enable and use Microsoft Fabric * Knowledge Check GET STARTED WITH LAKEHOUSES IN MICROSOFT FABRIC * Explore the Microsoft Fabric Lakehouse * Work with Microsoft Fabric Lakehouses * Exercise - Create and ingest data with a Microsoft Fabric Lakehouse USE APACHE SPARK IN MICROSOFT FABRIC * Prepare to use Apache Spark * Run Spark code * Work with data in a Spark dataframe * Work with data using Spark SQL * Visualize data in a Spark notebook * Exercise - Analyze data with Apache Spark WORK WITH DELTA LAKE TABLES IN MICROSOFT FABRIC * Understand Delta Lake * Create delta tables * Work with delta tables in Spark * Use delta tables with streaming data * Exercise - Use delta tables in Apache Spark INGEST DATA WITH DATAFLOWS GEN2 IN MICROSOFT FABRIC * Understand Dataflows (Gen2) in Microsoft Fabric * Explore Dataflows (Gen2) in Microsoft Fabric * Integrate Dataflows (Gen2) and Pipelines in Microsoft Fabric * Exercise - Create and use a Dataflow (Gen2) in Microsoft Fabric