127 Data Analysis courses in Edinburgh delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Data Data Everywhere – For what purpose? Which data is crucial to driving your organisation? How do we Analyse data to drive improvements? Course Overview Organisations generally collect enormous amounts of data. However, what data or information is really needed? How do we present the data that we have collected so that it is openly available and can be understood and used to drive the business? Is the data collected driving change? Structure of the Course This one-day workshop will enable participants to gain the necessary skills to collect, analyse and present data in an understanding and meaningful way and assist the decision-making process. It looks at how to translate data into useful and meaningful information that can contribute towards real problem solving, effective performance indicators, leading to the development of effective KPI’s, among others. Alternatively, if an organisation is in the process of selecting data collection methods and appropriate analysis, then this workshop will also help. Data analysis skills are essential to providing honest and accurate analysis, determining statistical significance, reliability, and validity on which to base their decisions, whether it is to improve quality, profitability, efficiency, or competitiveness. Improper statistical analyses distort findings and can mislead or negatively influence decision-making and the perception of the data collected. The correct analysis of data is a process of systematically applying statistical and / or logical techniques to describe and illustrate, condense, and recap, and evaluate data so that it can be used effectively. This workshop provides essential learning for staff at all levels of the organisation. Course content: 1. Data Types Discrete data Continuous data 2. Data Collection Sheet design Testing, prior to full-scale data collection 3. Data Input into Spreadsheet 4. Determination of Basic Descriptive Statistics Mean Median Mode Minimum and maximum values Range Standard deviation 5. Graphical Analysis Bar charts Line graphs Pie charts Scatter diagrams 6. Determination of Relationships between Factors Relationship between discrete factors Relationship between continuous factors 7. Use of Data in Decision Making 8. Establishment of Key Performance Indicators 9. Determination of Data Reliability 10. Summary Who would benefit from this Approach? Anyone who collects, manages, analyses and uses data to drive business performance. Delivery The course is delivered through virtual, tutor-led classes as structured above. The platform used is Adobe Connect which utilizes e-work rooms, video and streamed trainers. Cost £200 + VAT If you are not yet a member but are already thinking about joining CforC, you can find more information on how to become a member and the benefits by clicking here.

Duration 4 Days 24 CPD hours This course is intended for Successful students have experience and knowledge in IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, and governance. Students also have experience designing and architecting solutions. Before attending this course, students must have previous experience deploying or administering Azure resources and strong conceptual knowledge of: Azure compute technologies such as VMs, containers and serverless solutions Azure virtual networking to include load balancers Azure Storage technologies (unstructured and databases) General application design concepts such as messaging and high availability This course teaches Azure Solution Architects how to design infrastructure solutions. Course topics cover governance, compute, application architecture, storage, data integration, authentication, networks, business continuity, and migrations. The course combines lecture with case studies to demonstrate basic architect design principles. Prerequisites Before attending this course, students must have previous experience deploying or administering Azure resources and conceptual knowledge of: Azure Active Directory Azure compute technologies such as VMs, containers and serverless solutions Azure virtual networking to include load balancers Azure Storage technologies (unstructured and databases) General application design concepts such as messaging and high availability AZ-104T00 - Microsoft Azure Administrator 1 - Design governance Design for governance Design for management groups Design for subscriptions Design for resource groups Design for resource tags Design for Azure Policy Design for role-based access control (RBAC) Design for Azure landing zones 2 - Design an Azure compute solution Choose an Azure compute service Design for Azure Virtual Machines solutions Design for Azure Batch solutions Design for Azure App Service solutions Design for Azure Container Instances solutions Design for Azure Kubernetes Service solutions Design for Azure Functions solutions Design for Azure Logic Apps solutions 3 - Design a data storage solution for non-relational data Design for data storage Design for Azure storage accounts Design for data redundancy Design for Azure Blob Storage Design for Azure Files Design for Azure managed disks Design for storage security 4 - Design a data storage solution for relational data Design for Azure SQL Database Design for Azure SQL Managed Instance Design for SQL Server on Azure Virtual Machines Recommend a solution for database scalability Recommend a solution for database availability Design security for data at rest, data in motion, and data in use Design for Azure SQL Edge Design for Azure Cosmos DB and Table Storage 5 - Design data integration Design a data integration solution with Azure Data Factory Design a data integration solution with Azure Data Lake Design a data integration and analytic solution with Azure Databricks Design a data integration and analytic solution with Azure Synapse Analytics Design strategies for hot, warm, and cold data paths Design an Azure Stream Analytics solution for data analysis 6 - Design an application architecture Describe message and event scenarios Design a messaging solution Design an Azure Event Hubs messaging solution Design an event-driven solution Design a caching solution Design API integration Design an automated app deployment solution Design an app configuration management solution 7 - Design authentication and authorization solutions Design for identity and access management (IAM) Design for Microsoft Entra ID Design for Microsoft Entra business-to-business (B2B) Design for Azure Active Directory B2C (business-to-customer) Design for conditional access Design for identity protection Design for access reviews Design service principals for applications Design managed identities Design for Azure Key Vault 8 - Design a solution to log and monitor Azure resources Design for Azure Monitor data sources Design for Azure Monitor Logs (Log Analytics) workspaces Design for Azure Workbooks and Azure insights Design for Azure Data Explorer 9 - Design network solutions Recommend a network architecture solution based on workload requirements Design patterns for Azure network connectivity services Design outbound connectivity and routing Design for on-premises connectivity to Azure Virtual Network Choose an application delivery service Design for application delivery services Design for application protection services 10 - Design a solution for backup and disaster recovery Design for backup and recovery Design for Azure Backup Design for Azure blob backup and recovery Design for Azure files backup and recovery Design for Azure virtual machine backup and recovery Design for Azure SQL backup and recovery Design for Azure Site Recovery 11 - Design migrations Evaluate migration with the Cloud Adoption Framework Describe the Azure migration framework Assess your on-premises workloads Select a migration tool Migrate your structured data in databases Select an online storage migration tool for unstructured data Migrate offline data 12 - Introduction to the Microsoft Azure Well-Architected Framework Azure Well-Architected Framework pillars Cost optimization Operational excellence Performance efficiency Reliability Security 13 - Microsoft Azure Well-Architected Framework - Cost Optimization Develop cost-management discipline Design with a cost-efficiency mindset Design for usage optimization Design for rate optimization Monitor and optimize over time 14 - Microsoft Azure Well-Architected Framework - Operational excellence Embrace DevOps culture Establish development standards Evolve operations with observability Deploy with confidence Automate for efficiency Adopt safe deployment practices 15 - Microsoft Azure Well-Architected Framework - Performance efficiency Negotiate realistic performance targets Design to meet capacity requirements Achieve and sustain performance Improve efficiency through optimization 16 - Microsoft Azure Well-Architected Framework - Reliability Design for business requirements Design for resilience Design for recovery Design for operations Keep it simple 17 - Microsoft Azure Well-Architected Framework - Security Plan your security readiness Design to protect confidentiality Design to protect integrity Design to protect availability Sustain and evolve your security posture 18 - Getting started with the Microsoft Cloud Adoption Framework for Azure Customer narrative Common blockers 19 - Prepare for successful cloud adoption with a well-defined strategy Customer narrative Capture strategic motivation Define objectives and key results Evaluate financial considerations Understand technical considerations Create a business case 20 - Prepare for cloud adoption with a data-driven plan Customer narrative 21 - Choose the best Azure landing zone to support your requirements for cloud operations Customer narrative Common operating models Design areas for Azure landing zones Design principles for Azure landing zones Journey to the target architecture Choose an Azure landing zone option Deploy the Azure landing zone accelerator Enhance your landing zone 22 - Migrate to Azure through repeatable processes and common tools Customer narrative Migration process Migration tools Common tech platforms 23 - Address tangible risks with the Govern methodology of the Cloud Adoption Framework for Azure Customer narrative Govern methodology Corporate policies Governance disciplines Deploy a cloud governance foundation The Cost Management discipline 24 - Ensure stable operations and optimization across all supported workloads deployed to the cloud Establish business commitments Deploy an operations baseline Protect and recover Enhance an operations baseline Manage platform and workload specialization 25 - Innovate applications by using Azure cloud technologies Follow the innovation lifecycle Azure technologies for the build process Infuse your applications with AI Azure technologies for measuring business impact Azure technologies for the learn process 26 - Prepare for cloud security by using the Microsoft Cloud Adoption Framework for Azure Customer narrative Methodology Security roles and responsibilities Simplify compliance and security Simplify security implementation Security tools and policies Additional course details: Nexus Humans AZ-305T00: Designing Microsoft Azure Infrastructure Solutions 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 AZ-305T00: Designing Microsoft Azure Infrastructure Solutions 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 This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Overview Skills gained in this training include:The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysisThe fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with HadoopHow Pig, Hive, and Impala improve productivity for typical analysis tasksJoining diverse datasets to gain valuable business insightPerforming real-time, complex queries on datasets Cloudera University?s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. Hadoop Fundamentals The Motivation for Hadoop Hadoop Overview Data Storage: HDFS Distributed Data Processing: YARN, MapReduce, and Spark Data Processing and Analysis: Pig, Hive, and Impala Data Integration: Sqoop Other Hadoop Data Tools Exercise Scenarios Explanation Introduction to Pig What Is Pig? Pig?s Features Pig Use Cases Interacting with Pig Basic Data Analysis with Pig Pig Latin Syntax Loading Data Simple Data Types Field Definitions Data Output Viewing the Schema Filtering and Sorting Data Commonly-Used Functions Processing Complex Data with Pig Storage Formats Complex/Nested Data Types Grouping Built-In Functions for Complex Data Iterating Grouped Data Multi-Dataset Operations with Pig Techniques for Combining Data Sets Joining Data Sets in Pig Set Operations Splitting Data Sets Pig Troubleshoot & Optimization Troubleshooting Pig Logging Using Hadoop?s Web UI Data Sampling and Debugging Performance Overview Understanding the Execution Plan Tips for Improving the Performance of Your Pig Jobs Introduction to Hive & Impala What Is Hive? What Is Impala? Schema and Data Storage Comparing Hive to Traditional Databases Hive Use Cases Querying with Hive & Impala Databases and Tables Basic Hive and Impala Query Language Syntax Data Types Differences Between Hive and Impala Query Syntax Using Hue to Execute Queries Using the Impala Shell Data Management Data Storage Creating Databases and Tables Loading Data Altering Databases and Tables Simplifying Queries with Views Storing Query Results Data Storage & Performance Partitioning Tables Choosing a File Format Managing Metadata Controlling Access to Data Relational Data Analysis with Hive & Impala Joining Datasets Common Built-In Functions Aggregation and Windowing Working with Impala How Impala Executes Queries Extending Impala with User-Defined Functions Improving Impala Performance Analyzing Text and Complex Data with Hive Complex Values in Hive Using Regular Expressions in Hive Sentiment Analysis and N-Grams Conclusion Hive Optimization Understanding Query Performance Controlling Job Execution Plan Bucketing Indexing Data Extending Hive SerDes Data Transformation with Custom Scripts User-Defined Functions Parameterized Queries Choosing the Best Tool for the Job Comparing MapReduce, Pig, Hive, Impala, and Relational Databases Which to Choose?

This week long CPD accredited training is ideal for those Real Estate professionals, who are looking to bolster the DCF modelling skills. This course covers Real Estate Investment, Development, Finance & Risk.

OVERVIEW Prerequisites—this is a day of supervised revision for anyone who wishes to take the PL-300 exam so we expect you to have attended a PL-300 course, either taught or on your own, before attending this day of revision. We are happy for attendees to join the course if you have taken the PL-300 with us or with another provider. In the morning we look at questions from the PL-300 exam, grouped by subject area. For each question we show you the correct answer and then explain why that is the correct answer. After lunch everyone tries a practice exam under exam conditions and we finish the day with clarification on the subjects or specific questions that the class is finding hard. The cost of the course includes a free voucher to take the exam, so please don’t buy your own separately. If you do so we cannot reimburse you with the cost of the exam. COURSE BENEFITS: Discover the parts of the course on which to focus most attention Become familiar with the styles and formats of the exam questions Alert yourself to some of the particularly tricky questions Familiarise yourself with the style of the 'case study' question Practice an exam under real exam conditions Get a feel for the time you need to devote to the normal questions and to the case study WHO IS THE COURSE FOR? Power BI report developers who need PL-300 Certification to apply for a job Business Intelligence specialists who wish to enhance their career prospects with a PL-300 Certification Anyone who wishes to maximise their chances of passing the PL-300 Power BI Data Analyst exam COURSE OUTLINE Module 1 Structure Of The Exam Registration for the exam and setup of your workspace The percentage of marks devoted to each subject area Types of questions used in the exam Advised timing for the normal questions and for the case study question Module 2 Individual Exam Question Practice The exam questions are divided into subject areas and for each question… We consider the question and try to answer it We view the answer Where necessary, we look at an in-depth explanation of why that is the correct answer Examine case study questions and how to treat these differently from the normal questions Module 3 Practice Exam Under Exam Conditions Attendees take a practice exam Attendees receive a mark, broken down into subject areas Module 4 Exam Question Practice Focussed On Tricky Areas Together, based on the results of attendees’ exam results we decide which areas on which to focus More practice on individual exam questions using the same style as in Module 2

Duration 2 Days 12 CPD hours This course is intended for This course is relevant to anyone who needs to work with and understand data including: Business Analysts, Data Analysts, Reporting and BI professionals Marketing and Digital Marketing professionals Digital, Web, e-Commerce, Social media and Mobile channel professionals Business managers who need to interpret analytical output to inform managerial decisions Overview This course will cover the basic theory of data visualization along with practical skills for creating compelling visualizations, reports and dashboards from data using Tableau. Outcome: After attending this course delegates will understand - How to move from business questions to great data visualizations and beyond How to apply the fundamentals of data visualization to create informative charts How to choose the right visualization type for the job at hand How to design and develop basic dashboards in Tableau that people will love to use by doing the following: Reading data sources into Tableau Setting up the roles and data types for your analysis Creating new data fields using a range of calculation types Creating the following types of charts - cross tabs, pie and bar charts, geographic maps, dual axis and combo charts, heat maps, highlight tables, tree maps and scatter plots Creating Dashboards that delight using the all of the features available in Tableau. The use of analytics, statistics and data science in business has grown massively in recent years. Harnessing the power of data is opening actionable insights in diverse industries from banking to tourism. From Business Questions to Data Visualisation and Beyond The first step in any data analysis project is to move from a business question to data analysis and then on to a complete solution. This section will examine this conversion emphasizing: The use of data visualization to address a business need The data analytics process ? from business questions to developed dashboards Introduction to Tableau ? Part 1 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Introduction to Tableau ? Part 2 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Key Components of Good Data Visualisation and The Visualisation Zoo In this section the following topics will be covered: Colour theory Graphical perception & communication Choosing the right chart for the right job Data Exploration with Tableau Exploring data to answer business questions is one of the key uses of applying good data visualization techniques within Tableau. In this section we will apply the data visualization theory from the previous section within Tableau to uncover trends within the data to answer specific business questions. The types of charts that will be covered are: Cross Tabs Pie and bar charts Geographic maps Dual axis and combo charts with different mark types Heat maps Highlight tables Tree maps Scatter plots Introduction to Building Dashboards with Tableau In this section, we will implement the full process from business question to final basic dashboard in Tableau: Introduction to good dashboard design Building dashboards in Tableau

Duration 3.5 Days 21 CPD hours This course is intended for Intermediate Users of Office 365 and Excel Overview Creating Advanced Formulas Analyzing Data with Logical and Lookup Functions Organizing Worksheet Data with Tables Visualizing Data with Charts Analyzing Data with PivotTables, Slicers, and PivotCharts Inserting Graphics Enhancing Workbooks This course builds upon the foundational Microsoft Office Excel 2016, you create advanced workbooks and worksheets using advanced formulas and organizing your data into tables. Excel Intermediate Learn how to navigate Pivot Tables, (for example, Create a Pivot Table/ add data/ Add calculations); Formulas, Data organization (for example, multiple worksheets) Excel Advanced Data Analysis (for example, sparklines) , Macros (making changes to macros) and Building A Fast Dashboard (PivotCharts, slicers, etc.) Office 365 Training Getting More with OneDrive Office 365 Training When is a Team a Team? Includes Using Video with Audio, Exploring Teams/Navigating among Teams etc.

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