37 Courses delivered Live Online

OVERVIEW This official Microsoft Power BI training course will teach you how to connect to data from many sources, clean and transform it using Power Query, create a data model consisting of multiple tables connected with relationships and build visualisations and reports to show the patterns in the data. The course will explore formulas created using the DAX language, including the use of advanced date intelligence calculations. Additional visualisation features including interactivity between the elements of a report page are covered as well as parameters and row-level security, which allows a report to be tailored according to who is viewing it. The course will also show how to publish reports and dashboards to a workspace on the Power BI Service. COURSE BENEFITS: Learn how to clean, transform, and load data from many sources Use database queries in Power Query to combine tables using append and merge Create and manage a data model in Power BI consisting of multiple tables connected with relationships Build Measures and other calculations in the DAX language to plot in reports Manage advanced time calculations using date tables Optimise report calculations using the Performance Analyzer Manage and share report assets to the Power BI Service Prepare for the official Microsoft PL-300 exam using Microsoft Official Courseware WHO IS THE COURSE FOR? Data Analysts with little or no experience of Power BI who wish to upgrade their knowledge to include Business Intelligence Management Consultants who need to conduct rapid analysis of their clients’ data to answer specific business questions Analysts who need to upgrade their organisation from a simple Excel or SQL-based management reporting system to a dynamic BI system Data Analysts who wish to develop organisation-wide reporting in the form of web reports or phone apps Marketers in data-intensive organisations who wish to build visually appealing, dynamic charts for their stakeholders to use COURSE OUTLINE Module 1 Getting Started With Microsoft Data Analytics Data analytics and Microsoft Getting Started with Power BI Module 2 Get Data In Power BI Get data from various data sources Optimize performance Resolve data errors Lab: Prepare Data in Power BI Desktop Module 3 Clean, Transform And Load Data In Power BI Data shaping Data profiling Enhance the data structure Lab: Load Data in Power BI Desktop Module 4 Design A Data Model In Power BI Introduction to data modelling Working with Tables Dimensions and Hierarchies Lab: Model Data in Power BI Desktop Module 5 Create Model Calculations Using DAX In Power BI Introduction to DAX Real-time Dashboards Advanced DAX Lab 1: Create DAX Calculations in Power BI Desktop, Part 1 Lab 2: Create DAX Calculations in Power BI Desktop, Part 2 Module 6 Optimize Model Performance Optimize the data model for performance Optimize DirectQuery models Module 7 Create Reports Design a Report Enhance the Report Lab 1: Design a Report in Power BI Desktop, Part 1 Lab 2: Design a Report in Power BI Desktop, Part 2 Module 8 Create Dashboards Create a Dashboard Real-time Dashboards Enhance a Dashboard Lab: Create a Power BI Dashboard Module 9 Perform Advanced Analytics Advanced analytics Data Insights through AI Visuals Lab: Perform Data Analysis in Power BI Desktop Module 10 Create And Manage Workspaces Creating Workspaces Sharing and managing assets Module 11 Manage Datasets In Power BI Parameters Datasets Module 12 Row-Level Security Security in Power BI Lab: Enforce Row-Level Security

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.

SQL for Data Science, Data Analytics and Data Visualization Course Overview: This course offers a comprehensive introduction to SQL, designed for those looking to enhance their skills in data science, data analytics, and data visualisation. Learners will develop the ability to work with SQL databases, efficiently query and manage data, and apply these techniques for data analysis in both SQL Server and Azure Data Studio. By mastering SQL statements, aggregation, filtering, and advanced commands, learners will be equipped with the technical skills required to manage large datasets and extract meaningful insights. The course provides a solid foundation in data structures, user management, and working with multiple tables, culminating in the ability to perform complex data analysis and visualisation tasks. Course Description: This course covers a broad range of topics essential for anyone working with data in a professional capacity. From setting up SQL servers to mastering database management tools like SQL Server Management Studio (SSMS) and SQL Azure Data Studio, the course provides a thorough grounding in SQL. Learners will gain expertise in data structure statements, filtering data, and applying aggregate functions, as well as building complex SQL queries for data analysis. The course also includes instruction on SQL user management, group by statements, and JOINs for multi-table analysis. Key topics such as SQL constraints, views, stored procedures, and database backup and restore are also explored. The course incorporates SQL visualisation tools in Azure Data Studio, empowering learners to visualise data effectively. By the end of the course, learners will be proficient in SQL queries, data manipulation, and using Azure for data analysis. SQL for Data Science, Data Analytics and Data Visualization Curriculum: Module 01: Getting Started Module 02: SQL Server Setting Up Module 03: SQL Azure Data Studio Module 04: SQL Database Basic SSMS Module 05: SQL Statements for DATA Module 06: SQL Data Structure Statements Module 07: SQL User Management Module 08: SQL Statement Basic Module 09: Filtering Data Rows Module 10: Aggregate Functions Module 11: SQL Query Statements Module 12: SQL Group By Statement Module 13: JOINS for Multiple Table Data Analysis Module 14: SQL Constraints Module 15: Views Module 16: Advanced SQL Commands Module 17: SQL Stored Procedures Module 18: Azure Data Studio Visualisation Module 19: Azure Studio SQL for Data Analysis Module 20: Import & Export Data Module 21: Backup and Restore Database (See full curriculum) Who is this course for? Individuals seeking to enhance their data management and analysis skills. Professionals aiming to progress in data science, data analytics, or database administration. Beginners with an interest in data analysis and SQL databases. Anyone looking to gain expertise in SQL for Azure and SQL Server environments. Career Path: Data Analyst Data Scientist Database Administrator SQL Developer Business Intelligence Analyst Data Visualisation Specialist

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization. Overview After completing this course delegates will be capable of writing effective R code to manipulate, analyse and visualise data to enable their organisations make better, data-driven decisions. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Course Outline Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. The R programming language is one of the most powerful and flexible tools in the data analytics toolkit. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Mastery of these techniques will allow delegates to immediately add value in their work place by extracting valuable insight from company data to allow better, data-driven decisions. The course will explore the following topics through a series of interactive workshop sessions: What is R? Basic R programming conventions Data structures in R Accessing data in R Descriptive statistics in R Statistical analysis in R Data manipulation in R Data visualisation in R Additional course details: Nexus Humans Beginning Data Analytics With R 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 Beginning Data Analytics With R 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 intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a data warehouse analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

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

Course Overview: The "FinTech and Big Data Analytics" course provides an in-depth exploration of the dynamic intersection between financial technology (FinTech) and big data. Learners will gain essential knowledge about the innovative solutions disrupting the financial services industry, such as cryptocurrencies, insurtech, and regtech. The course offers insights into the tools, technologies, and trends shaping the future of finance, with a specific focus on how big data analytics is transforming business models and decision-making. By the end of the course, learners will have a comprehensive understanding of FinTech's growth and its applications, enabling them to make informed decisions in this rapidly evolving field. Course Description: This course delves deeper into the core concepts of financial technology and big data, exploring the impact of FinTech innovations on traditional financial systems. Topics covered include the rise of cryptocurrencies, regulatory technology (RegTech), the development of insurance technologies (InsurTech), and the use of big data in reshaping business strategies. Learners will explore the key technologies that drive FinTech, such as blockchain, artificial intelligence (AI), and machine learning, and learn how they enable data-driven decision-making in finance. The course prepares learners for the evolving future of FinTech, equipping them with the necessary skills to understand and navigate this transformative landscape. Course Modules: Module 01: Introduction to Financial Technology – FinTech Module 02: Exploring Cryptocurrencies Module 03: RegTech Module 04: Rise of InsurTechs Module 05: Big Data Basics: Understanding Big Data Module 06: The Future of FinTech (See full curriculum) Who is this course for? Individuals seeking to understand the financial technology landscape. Professionals aiming to advance their careers in the rapidly evolving FinTech sector. Beginners with an interest in emerging financial technologies and data analytics. Entrepreneurs looking to innovate within the financial services industry. Career Path: Financial Analyst FinTech Specialist Data Analyst in Financial Services Blockchain Developer RegTech Consultant InsurTech Specialist Big Data Analyst in Finance

Learn to Drive Traffic into Sales through Digital Marketing Course Overview This course on "Learn to Drive Traffic into Sales through Digital Marketing" provides learners with the essential skills to effectively use digital marketing strategies to attract, engage, and convert online traffic into sales. Covering key areas such as SEO, social media marketing, content creation, and email marketing, the course equips learners with the knowledge to optimise digital campaigns and enhance business visibility. The course is designed for individuals seeking to master digital marketing techniques to increase online sales and drive business growth. By the end of the course, learners will have a comprehensive understanding of traffic generation strategies and how to convert them into tangible business results. Course Description In this course, learners will explore various digital marketing techniques aimed at driving traffic to websites and converting that traffic into sales. Topics include SEO fundamentals, social media marketing strategies, email marketing campaigns, and creating compelling content. The course provides a structured approach to understanding how to leverage these strategies effectively, using data analytics to track and improve performance. Learners will gain the ability to create digital marketing plans, monitor campaign success, and optimise strategies to enhance online sales. The course is designed for those who want to advance their digital marketing skills and improve conversion rates, whether for personal projects or within their professional roles. Learn to Drive Traffic into Sales through Digital Marketing Curriculum Module 01: Introduction to Digital Marketing Module 02: SEO Strategies for Traffic Growth Module 03: Social Media Marketing for Engagement Module 04: Email Marketing to Convert Leads Module 05: Creating Content that Converts Module 06: Analytics and Campaign Optimisation (See full curriculum) Who is this course for? Individuals seeking to increase their online sales through digital marketing. Professionals aiming to enhance their digital marketing knowledge and career prospects. Beginners with an interest in driving business growth via online channels. Entrepreneurs wanting to optimise their digital marketing efforts. Career Path Digital Marketing Specialist SEO Manager Social Media Manager Content Marketing Strategist Email Marketing Coordinator E-commerce Manager

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