125 Courses in Cardiff delivered Live Online

About this Virtual Instructor Led Training (VILT) This Virtual Instructor Led Training (VILT) course presents advanced methodologies that implement demand response and energy conservation programs in light of the integration of new technologies, regulatory changes and the accelerated penetration of renewable energy resources. This VILT course provides examples and case studies from North American and European jurisdictions covering the operational flexibilities on the demand side including requirements for new building codes to achieve zero net energy. The course describes a public agency's goals and objectives for conserving and otherwise reducing energy consumption and managing its demand for energy. This course presents the demand response implemented for economics and system security such as system balancing and relieving transmission congestion, or for system adequacy. The course also presents the principal attributes of conservation programs and the associated success criteria. In a system with increased penetration of renewable resources, demand response provides flexibility to system operators, helping them to maintain the reliability and the security of supply. Demand response is presented as a competitive alternative to additional power sources, enhancing competition and liquidity in electricity markets. The unique characteristics are discussed from a local, consumer centric and also from a system perspective bringing to life the ever changing paradigm for delivery energy to customers. Interoperability aspects and standards are discussed, as well as the consumer centric paradigm of Transactive Energy with IOT enabled flexibilities at system level, distribution networks and microgrids. The VILT course introduces the blockchain as a new line of defense against cyber threats and its increasing application in P2P transactions and renewable certificates. Our trainer's industry experience spans three decades with one of the largest Canadian utilities where she led or contributed to large operational studies and energy policies and decades of work with IEEE, NSERC and CIGRE. Our key expert also approaches to the cross sectional, interdisciplinary state of the art methodologies brings real life experience of recent industry developments. Training Objectives Innovative Digital Technologies How systems Facilitate Operational Flexibility on the Demand Side The Ecosystem of Demand Side Management Programs Advanced Machine Learning techniques with examples from CAISO Regulatory Policy Context and how to reduce regulatory barriers Industry Examples from NERC and ENTSO Relevant Industry standards: IEEE and IEC Manage Congestion with Distributed Operational Flexibilities: Grid to Distribution Controls; examples from NERC (NA) and ENTSO (Europe) Grid solutions with IEC 61850 communication protocols Decentralized grid controls The New Grid with accelerated V2G and Microgrids How DSM is and will be applied in Your System: Examples and discussions Target Audience Regulators and government agencies advising on public energy conservation programs All professionals interested in expanding their expertise, or advancing their career, or take on management and leadership roles in the rapidly evolving energy sector Energy professionals implementing demand side management, particularly in power systems with increased renewable penetration, to allow the much needed operational flexibility paramount to maintaining the reliability and stability of the power system and in the same time offering all classes of customers flexible and economical choices Any utility professional interested in understanding the new developments in the power industry Course Level Basic or Foundation Training Methods The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, with 2 x 10 minutes break per day, including time for lectures, discussion, quizzes and short classroom exercises. Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total). Trainer Your first expert course leader is a Utility Executive with extensive global experience in power system operation and planning, energy markets, enterprise risk and regulatory oversight. She consults on energy markets integrating renewable resources from planning to operation. She led complex projects in operations and conducted long term planning studies to support planning and operational reliability standards. Specializing in Smart Grids, Operational flexibilities, Renewable generation, Reliability, Financial Engineering, Energy Markets and Power System Integration, she was recently engaged by the Inter-American Development Bank/MHI in Guyana. She was the Operations Expert in the regulatory assessment in Oman. She is a registered member of the Professional Engineers of Ontario, Canada. She is also a contributing member to the IEEE Standards Association, WG Blockchain P2418.5. With over 25 years with Ontario Power Generation (Revenue $1.2 Billion CAD, I/S 16 GW), she served as Canadian representative in CIGRE, committee member in NSERC (Natural Sciences and Engineering Research Council of Canada), and Senior Member IEEE and Elsevier since the 90ties. Our key expert chaired international conferences, lectured on several continents, published a book on Reliability and Security of Nuclear Power Plants, contributed to IEEE and PMAPS and published in the Ontario Journal for Public Policy, Canada. She delivered seminars organized by the Power Engineering Society, IEEE plus seminars to power companies worldwide, including Oman, Thailand, Saudi Arabia, Malaysia, Indonesia, Portugal, South Africa, Japan, Romania, and Guyana. Your second expert course leader is the co-founder and Director of Research at Xesto Inc. Xesto is a spatial computing AI startup based in Toronto, Canada and it has been voted as Toronto's Best Tech Startup 2019 and was named one of the top 10 'Canadian AI Startups to Watch' as well as one of 6th International finalists for the VW Siemens Startup Challenge, resulting in a partnership. His latest app Xesto-Fit demonstrates how advanced AI and machine learning is applied to the e-commerce industry, as a result of which Xesto has been recently featured in TechCrunch. He specializes in both applied and theoretical machine learning and has extensive experience in both industrial and academic research. He is specialized in Artificial Intelligence with multiple industrial applications. At Xesto, he leads projects that focus on applying cutting edge research at the intersection of spatial analysis, differential geometry, optimization of deep neural networks, and statistics to build scalable rigorous and real time performing systems that will change the way humans interact with technology. In addition, he is a Ph.D candidate in the Mathematics department at UofT, focusing on applied mathematics. His academic research interests are in applying advanced mathematical methods to the computational and statistical sciences. He earned a Bachelor's and MSc in Mathematics, both at the University of Toronto. Having presented at research seminars as well as instructing engineers on various levels, he has the ability to distill advanced theoretical concept to diverse audiences on all levels. In addition to research, our key expert is also an avid traveler and plays the violin. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information about post training coaching support and fees applicable for this. Accreditions And Affliations

Duration 2 Days 12 CPD hours This course is intended for This course is suited to marketeers, business analysts, and researchers who are interested in increasing their statistical knowledge. Overview After attending this course, delegates will understand how statistics can be used to provide valuable insight into their business, and be able to apply statistical methods to solve business problems. On returning to work delegates will immediately be able to make a difference to the way that their organisations make decisions. This course covers the statistical methods that analysts need to move from simple reporting on business problems to extracting insight to solve business problems. Course Outline The course will explore the following topics through a series of lectures and workshops: Summary statistics for both continuous data and categorical data Using and reporting confidence intervals Using hypothesis tests to answer business questions Using correlations to explore data relationships Simple prediction models Analysing categorical data Additional course details: Nexus Humans Data-driven Business Using Statistical Analysis 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 Data-driven Business Using Statistical Analysis 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.

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 This course is designed for technical professionals who require the skills to administer IBM© MQ queue managers on distributed operating systems, in the Cloud, or on the IBM© MQ Appliance. Overview Describe the IBM© MQ deployment optionsPlan for the implementation of IBM© MQ on-premises or in the CloudUse IBM© MQ commands and the IBM© MQ Explorer to create and manage queue managers, queues, and channelsUse the IBM© MQ sample programs and utilities to test the IBM© MQ networkEnable a queue manager to exchange messages with another queue managerConfigure client connections to a queue managerUse a trigger message and a trigger monitor to start an application to process messagesImplement basic queue manager restart and recovery proceduresUse IBM© MQ troubleshooting tools to identify the cause of a problem in the IBM© MQ networkPlan for and implement basic IBM© MQ security featuresUse accounting and statistics messages to monitor the activities of an IBM© MQ systemDefine and administer a simple queue manager cluster This course provides technical professionals with the skills that are needed to administer IBM© MQ queue managers on distributed operating systems and in the Cloud. In addition to the instructor-led lectures, you participate in hands-on lab exercises that are designed to reinforce lecture content. The lab exercises use IBM© MQ V9.0, giving you practical experience with tasks such as handling queue recovery, implementing security, and problem determination. Describe the IBM© MQ deployment optionsPlan for the implementation of IBM© MQ on-premises or in the CloudUse IBM© MQ commands and the IBM© MQ Explorer to create and manage queue managers, queues, and channelsUse the IBM© MQ sample programs and utilities to test the IBM© MQ networkEnable a queue manager to exchange messages with another queue managerConfigure client connections to a queue managerUse a trigger message and a trigger monitor to start an application to process messagesImplement basic queue manager restart and recovery proceduresUse IBM© MQ troubleshooting tools to identify the cause of a problem in the IBM© MQ networkPlan for and implement basic IBM© MQ security featuresUse accounting and statistics messages to monitor the activities of an IBM© MQ systemDefine and administer a simple queue manager cluster

Duration 4 Days 24 CPD hours This course is intended for The Microsoft Security Operations Analyst collaborates with organizational stakeholders to secure information technology systems for the organization. Their goal is to reduce organizational risk by rapidly remediating active attacks in the environment, advising on improvements to threat protection practices, and referring violations of organizational policies to appropriate stakeholders. Responsibilities include threat management, monitoring, and response by using a variety of security solutions across their environment. The role primarily investigates, responds to, and hunts for threats using Microsoft Sentinel, Microsoft Defender for Cloud, Microsoft 365 Defender, and third-party security products. Since the Security Operations Analyst consumes the operational output of these tools, they are also a critical stakeholder in the configuration and deployment of these technologies. Learn how to investigate, respond to, and hunt for threats using Microsoft Sentinel, Microsoft Defender for Cloud, and Microsoft 365 Defender. In this course you will learn how to mitigate cyberthreats using these technologies. Specifically, you will configure and use Microsoft Sentinel as well as utilize Kusto Query Language (KQL) to perform detection, analysis, and reporting. The course was designed for people who work in a Security Operations job role and helps learners prepare for the exam SC-200: Microsoft Security Operations Analyst. Prerequisites Basic understanding of Microsoft 365 Fundamental understanding of Microsoft security, compliance, and identity products Intermediate understanding of Windows 10 Familiarity with Azure services, specifically Azure SQL Database and Azure Storage Familiarity with Azure virtual machines and virtual networking Basic understanding of scripting concepts. 1 - Introduction to Microsoft 365 threat protection Explore Extended Detection & Response (XDR) response use cases Understand Microsoft Defender XDR in a Security Operations Center (SOC) Explore Microsoft Security Graph Investigate security incidents in Microsoft Defender XDR 2 - Mitigate incidents using Microsoft 365 Defender Use the Microsoft Defender portal Manage incidents Investigate incidents Manage and investigate alerts Manage automated investigations Use the action center Explore advanced hunting Investigate Microsoft Entra sign-in logs Understand Microsoft Secure Score Analyze threat analytics Analyze reports Configure the Microsoft Defender portal 3 - Protect your identities with Microsoft Entra ID Protection Microsoft Entra ID Protection overview Detect risks with Microsoft Entra ID Protection policies Investigate and remediate risks detected by Microsoft Entra ID Protection 4 - Remediate risks with Microsoft Defender for Office 365 Automate, investigate, and remediate Configure, protect, and detect Simulate attacks 5 - Safeguard your environment with Microsoft Defender for Identity Configure Microsoft Defender for Identity sensors Review compromised accounts or data Integrate with other Microsoft tools 6 - Secure your cloud apps and services with Microsoft Defender for Cloud Apps Understand the Defender for Cloud Apps Framework Explore your cloud apps with Cloud Discovery Protect your data and apps with Conditional Access App Control Walk through discovery and access control with Microsoft Defender for Cloud Apps Classify and protect sensitive information Detect Threats 7 - Respond to data loss prevention alerts using Microsoft 365 Describe data loss prevention alerts Investigate data loss prevention alerts in Microsoft Purview Investigate data loss prevention alerts in Microsoft Defender for Cloud Apps 8 - Manage insider risk in Microsoft Purview Insider risk management overview Create and manage insider risk policies Investigate insider risk alerts Take action on insider risk alerts through cases Manage insider risk management forensic evidence Create insider risk management notice templates 9 - Investigate threats by using audit features in Microsoft Defender XDR and Microsoft Purview Standard Explore Microsoft Purview Audit solutions Implement Microsoft Purview Audit (Standard) Start recording activity in the Unified Audit Log Search the Unified Audit Log (UAL) Export, configure, and view audit log records Use audit log searching to investigate common support issues 10 - Investigate threats using audit in Microsoft Defender XDR and Microsoft Purview (Premium) Explore Microsoft Purview Audit (Premium) Implement Microsoft Purview Audit (Premium) Manage audit log retention policies Investigate compromised email accounts using Purview Audit (Premium) 11 - Investigate threats with Content search in Microsoft Purview Explore Microsoft Purview eDiscovery solutions Create a content search View the search results and statistics Export the search results and search report Configure search permissions filtering Search for and delete email messages 12 - Protect against threats with Microsoft Defender for Endpoint Practice security administration Hunt threats within your network 13 - Deploy the Microsoft Defender for Endpoint environment Create your environment Understand operating systems compatibility and features Onboard devices Manage access Create and manage roles for role-based access control Configure device groups Configure environment advanced features 14 - Implement Windows security enhancements with Microsoft Defender for Endpoint Understand attack surface reduction Enable attack surface reduction rules 15 - Perform device investigations in Microsoft Defender for Endpoint Use the device inventory list Investigate the device Use behavioral blocking Detect devices with device discovery 16 - Perform actions on a device using Microsoft Defender for Endpoint Explain device actions Run Microsoft Defender antivirus scan on devices Collect investigation package from devices Initiate live response session 17 - Perform evidence and entities investigations using Microsoft Defender for Endpoint Investigate a file Investigate a user account Investigate an IP address Investigate a domain 18 - Configure and manage automation using Microsoft Defender for Endpoint Configure advanced features Manage automation upload and folder settings Configure automated investigation and remediation capabilities Block at risk devices 19 - Configure for alerts and detections in Microsoft Defender for Endpoint Configure advanced features Configure alert notifications Manage alert suppression Manage indicators 20 - Utilize Vulnerability Management in Microsoft Defender for Endpoint Understand vulnerability management Explore vulnerabilities on your devices Manage remediation 21 - Plan for cloud workload protections using Microsoft Defender for Cloud Explain Microsoft Defender for Cloud Describe Microsoft Defender for Cloud workload protections Enable Microsoft Defender for Cloud 22 - Connect Azure assets to Microsoft Defender for Cloud Explore and manage your resources with asset inventory Configure auto provisioning Manual log analytics agent provisioning 23 - Connect non-Azure resources to Microsoft Defender for Cloud Protect non-Azure resources Connect non-Azure machines Connect your AWS accounts Connect your GCP accounts 24 - Manage your cloud security posture management? Explore Secure Score Explore Recommendations Measure and enforce regulatory compliance Understand Workbooks 25 - Explain cloud workload protections in Microsoft Defender for Cloud Understand Microsoft Defender for servers Understand Microsoft Defender for App Service Understand Microsoft Defender for Storage Understand Microsoft Defender for SQL Understand Microsoft Defender for open-source databases Understand Microsoft Defender for Key Vault Understand Microsoft Defender for Resource Manager Understand Microsoft Defender for DNS Understand Microsoft Defender for Containers Understand Microsoft Defender additional protections 26 - Remediate security alerts using Microsoft Defender for Cloud Understand security alerts Remediate alerts and automate responses Suppress alerts from Defender for Cloud Generate threat intelligence reports Respond to alerts from Azure resources 27 - Construct KQL statements for Microsoft Sentinel Understand the Kusto Query Language statement structure Use the search operator Use the where operator Use the let statement Use the extend operator Use the order by operator Use the project operators 28 - Analyze query results using KQL Use the summarize operator Use the summarize operator to filter results Use the summarize operator to prepare data Use the render operator to create visualizations 29 - Build multi-table statements using KQL Use the union operator Use the join operator 30 - Work with data in Microsoft Sentinel using Kusto Query Language Extract data from unstructured string fields Extract data from structured string data Integrate external data Create parsers with functions 31 - Introduction to Microsoft Sentinel What is Microsoft Sentinel? How Microsoft Sentinel works When to use Microsoft Sentinel 32 - Create and manage Microsoft Sentinel workspaces Plan for the Microsoft Sentinel workspace Create a Microsoft Sentinel workspace Manage workspaces across tenants using Azure Lighthouse Understand Microsoft Sentinel permissions and roles Manage Microsoft Sentinel settings Configure logs 33 - Query logs in Microsoft Sentinel Query logs in the logs page Understand Microsoft Sentinel tables Understand common tables Understand Microsoft Defender XDR tables 34 - Use watchlists in Microsoft Sentinel Plan for watchlists Create a watchlist Manage watchlists 35 - Utilize threat intelligence in Microsoft Sentinel Define threat intelligence Manage your threat indicators View your threat indicators with KQL 36 - Connect data to Microsoft Sentinel using data connectors Ingest log data with data connectors Understand data connector providers View connected hosts 37 - Connect Microsoft services to Microsoft Sentinel Plan for Microsoft services connectors Connect the Microsoft Office 365 connector Connect the Microsoft Entra connector Connect the Microsoft Entra ID Protection connector Connect the Azure Activity connector 38 - Connect Microsoft Defender XDR to Microsoft Sentinel Plan for Microsoft Defender XDR connectors Connect the Microsoft Defender XDR connector Connect Microsoft Defender for Cloud connector Connect Microsoft Defender for IoT Connect Microsoft Defender legacy connectors 39 - Connect Windows hosts to Microsoft Sentinel Plan for Windows hosts security events connector Connect using the Windows Security Events via AMA Connector Connect using the Security Events via Legacy Agent Connector Collect Sysmon event logs 40 - Connect Common Event Format logs to Microsoft Sentinel Plan for Common Event Format connector Connect your external solution using the Common Event Format connector 41 - Connect syslog data sources to Microsoft Sentinel Plan for syslog data collection Collect data from Linux-based sources using syslog Configure the Data Collection Rule for Syslog Data Sources Parse syslog data with KQL 42 - Connect threat indicators to Microsoft Sentinel Plan for threat intelligence connectors Connect the threat intelligence TAXII connector Connect the threat intelligence platforms connector View your threat indicators with KQL 43 - Threat detection with Microsoft Sentinel analytics What is Microsoft Sentinel Analytics? Types of analytics rules Create an analytics rule from templates Create an analytics rule from wizard Manage analytics rules 44 - Automation in Microsoft Sentinel Understand automation options Create automation rules 45 - Threat response with Microsoft Sentinel playbooks What are Microsoft Sentinel playbooks? Trigger a playbook in real-time Run playbooks on demand 46 - Security incident management in Microsoft Sentinel Understand incidents Incident evidence and entities Incident management 47 - Identify threats with Behavioral Analytics Understand behavioral analytics Explore entities Display entity behavior information Use Anomaly detection analytical rule templates 48 - Data normalization in Microsoft Sentinel Understand data normalization Use ASIM Parsers Understand parameterized KQL functions Create an ASIM Parser Configure Azure Monitor Data Collection Rules 49 - Query, visualize, and monitor data in Microsoft Sentinel Monitor and visualize data Query data using Kusto Query Language Use default Microsoft Sentinel Workbooks Create a new Microsoft Sentinel Workbook 50 - Manage content in Microsoft Sentinel Use solutions from the content hub Use repositories for deployment 51 - Explain threat hunting concepts in Microsoft Sentinel Understand cybersecurity threat hunts Develop a hypothesis Explore MITRE ATT&CK 52 - Threat hunting with Microsoft Sentinel Explore creation and management of threat-hunting queries Save key findings with bookmarks Observe threats over time with livestream 53 - Use Search jobs in Microsoft Sentinel Hunt with a Search Job Restore historical data 54 - Hunt for threats using notebooks in Microsoft Sentinel Access Azure Sentinel data with external tools Hunt with notebooks Create a notebook Explore notebook code

Duration 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

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

Sage 50 Training: Overview Starting our Sage 50 Accounting courses will enhance your career potentials and give you the skills and knowledge you need to get started in Finance and Accountancy Industry. In Addition, our courses are designed to comply with AAT and Sage certification exams. Why wait, start a new direction to your career in Accountancy. According to statistics, the average salary for Accountants is over £50,000 (Source: Reed Salary Checker). In this sector, the employability rate is higher than in any other sector. Professional or Industry specific qualification

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.

Sage 50 Courses: Overview Starting our Sage 50 Accounting courses will enhance your career potentials and give you the skills and knowledge you need to get started in Finance and Accountancy Industry. In Addition, our courses are designed to comply with AAT and Sage certification exams. Why wait, start a new direction to your career in Accountancy. According to statistics, the average salary for Accountants is over £50,000 (Source: Reed Salary Checker). In this sector, the employability rate is higher than in any other sector. Professional or Industry specific qualification