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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 Software developers, system administrators, and IT professionals who are focused on Microsoft Windows Overview Configuring Microsoft Windows and Microsoft SQL Server in Google Compute Engine. Deploying ASP.NET MVC applications to Google Compute Engine. Deploying .NET Core applications to Google Compute Engine, Google Compute Engine, and Google Container Engine Learn how to create Windows virtual machines on Google Cloud so that you can deploy and run Microsoft Windows applications. In this course, you'll learn how to run SQL Server in Compute Engine, how to deploy instances across Google Cloud zones, and how to get more out of ASP.NET on Compute Engine, Google Container Engine, and App Engine. Introduction to Google Cloud Platform Scope and structure of GCP. Options for Windows deployment on GCP. GCP interfaces. Windows Workloads on Google Compute Engine Google Compute Engine virtual machine options. Integrating Active Directory with Google Compute Engine virtual machines. Options for running SQL Server in Google Compute Engine. Configuring SQL Server for high availability. Developing ASP.NET MVC applications Model-view-controller structure. Using Microsoft Visual Studio?s Web Project template to develop in ASP.NET. Deploying applications to Microsoft Internet Information Server (IIS) in GCE. Configuring Resilient Workloads Deploying instances across GCP zones. Using instance groups to create pools of virtual machines. Load balancing Windows applications. Delivering Next-Generation ASP.NET Core on GCP Understanding .NET Core and EF Core. Options for deploying ASP.NET Core applications on Google Cloud Platform. Deploying ASP.NET Core applications on Google Compute Engine. Deploying ASP.NET Core applications on Google Container Engine. Deploying ASP.NET Core applications on Google App Engine. Additional course details: Nexus Humans Develop and Deploy Windows Applications on Google Cloud Platform 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 Develop and Deploy Windows Applications on Google Cloud Platform 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 video course takes you through the basic and advanced concepts of penetration testing. From setting up your own virtual lab to developing brute force attacking tools using Python, you'll learn it all with the help of engaging activities.

Advance your data skills by mastering Spark programming in Python. This beginner's level course will help you understand the core concepts related to Apache Spark 3 and provide you with knowledge of applying those concepts to build data engineering solutions.

This course provides complete coverage of C# programming fundamentals and how to create an executable windows form project. You will be digging into the structures of Windows Forms to gain a detailed understanding and then sharpen your skills with the help of a variety of projects.

Unlock your potential as a Business Intelligence Analyst! Master data analysis, reporting, and visualisation skills to drive informed business decisions.

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Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

The Business Intelligence Analyst Course aligns with current industry trends and market needs, addressing emerging opportunities and challenges in data-driven decision-making. As businesses increasingly rely on data for strategic planning, this course provides the foundational knowledge and skills required to interpret and leverage data effectively. It prepares learners to meet the growing demand for data analysis and interpretation in various sectors. The primary problem this course addresses is the gap in practical, accessible knowledge for analysing and interpreting business data. By bridging this gap, the course enables learners to transform raw data into meaningful insights. This ability is crucial for driving business growth, improving operational efficiency, and enhancing strategic decision-making processes across industries. In today's data-centric business environment, possessing skills in business intelligence and data analysis is not just advantageous but essential. This course equips learners with the knowledge to analyse data accurately, leading to informed decision-making and competitive advantage in various business contexts. Learning Outcomes Understand the basics and importance of Business Intelligence in modern business. Grasp real-time applications of Business Intelligence in industry scenarios. Learn statistical and data science fundamentals crucial for analysis. Gain proficiency in SQL for effective data management and querying. Master essential aspects of database management and operations. Explore data warehouse concepts and their relevance in data storage. Develop skills in data visualisation techniques for business insights. Acquire basic Python knowledge for data analysis applications. Why choose this Business Intelligence Analyst Course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Business Intelligence Analyst Course for? Individuals aspiring to become Business Intelligence Analysts. Professionals seeking to enhance their data analysis skills. Entrepreneurs needing to understand data for better business decisions. Managers and team leaders requiring data-driven decision-making abilities. Anyone interested in pursuing a career in data analysis and business intelligence. Career path Business Intelligence Analyst: £30,000 - £50,000 Data Analyst: £27,000 - £40,000 Database Administrator: £30,000 - £45,000 Data Scientist: £35,000 - £60,000 Business Analyst: £32,000 - £52,000 Prerequisites This Business Intelligence Analyst Course does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Business Intelligence Analyst Course was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module-1: An Introduction to Business Intelligence An Introduction to Business Intelligence 00:46:00 Module-2: Real-time Business Intelligence Real-time Business Intelligence 00:35:00 Module-3: Fundamentals of Statistics and Data Science Fundamentals of Statistics and Data Science 00:50:00 Module-4: Fundamentals of SQL Fundamentals of SQL 00:40:00 Module-5: Database Management Essentials Database Management Essentials 00:50:00 Module-6: Data Warehouse Concepts Data Warehouse Concepts 00:38:00 Module-7: Data Visualisation in Business Intelligence Data Visualisation in Business Intelligence 00:35:00 Module-8: Basics of Python Basics of Python 00:27:00

Duration 1 Days 6 CPD hours This course is intended for This course is intended for networking and security professionals involved in the day-to-day management of a FortiAnalyzer appliance and FortiGate security information.. Overview Describe key features and concepts of FortiAnalyzer Deploy an appropriate architecture Manage ADOMs on both FortiAnalyzer and the devices that log to it Configure RAID Register supported devices Encrypt log transmission (SSL / IPSec) View & analyze current and historical logs (FortiView) Monitor events Apply disk quotas to log data from devices Backup, restore, and forward log data Use content archiving (summary and full) Understand the different stages of data processing, from receiving logs to compiling reports Understand SQL queries and datasets used by FortiAnalyzer reports Design datasets, charts, and custom reports Generate reports by schedule or on demand. In this 1-day class, you will learn how to use FortiAnalyzer. You will explore setup, registering supported devices and securing communications, managing logs and archives, and configuring both predefined and customized reports. Introduction to FortiAnalyzer Key features Key concepts Different FortiAnalyzer models Configuration & Administration Deployment requirements Configuration tools Configuring network settings Backing up system configuration Configuring administrative users Configuring, enabling, and assigning ADOMs Configuring RAID Device Registration Registered and unregistered devices Device registration methods Modifying options of a registered device Methods available to secure communication Configuring SSL encryption and encryption levels Configuring an IPsec tunnel Logs & Archives Logging basics The FortiView tab Configuring log arrays and event handlers Reports Reports and functionality Relationship between reports, charts, and datasets Effect of ADOMs on report settings SQL SELECT queries and clauses SQL functions and operators FortiAnalyzer-specific functions and macros Building or customizing charts Report features--creating, cloning, configuring