555 Courses delivered Online

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

This comprehensive course on AWS Certified Cloud Practitioner (CLF-C01) empowers you to fast-track your IT career. Gain in-depth knowledge of cloud computing, AWS services, and architectural concepts. With hands-on labs, quizzes, and real practice exams, you will confidently build cost-effective, fault-tolerant IT solutions on the AWS Cloud.

This comprehensive course covers all Scrum principles and frameworks necessary to help participants understand how to guide a team and manage projects in a fast-paced agile environment. The course is meant for professionals who want to attain the certification of Scrum Master with deep insight into how AI can be utilized in increasing the effectiveness of agile practices. In addition to mastery of the core Scrum methodology, participants will be taken through state-of-the-art advancements in AI and machine learning in order to understand how these technologies can automate routine tasks, enhance decision-making, and continuous improvement. Real-world case studies and hands-on exercises will illustrate how to practically apply AI within Scrum to realize high efficiency and innovation for teams. Whether for enhancing one's career as a Scrum Master or the integration of AI into Agile practices, this course provides that ideal combination of conceptual theory and practical skills, assuring success in today's technology-driven world. Key Highlights: Certified Scrum Master training with AI applications Case studies in the real world about integrating AI in Scrum Hands-on projects to implement AI-driven tools and methodologies Workflow optimization techniques that ensure better collaboration of agile teams, with speeding up project delivery by the power of AI. Ideal for Scrum Masters, Agile Coaches, Product Owners, and tech pros looking to stay ahead.

Learn to effectively monitor the performance, health, and availability of Azure Services and your cloud resources

Duration 1 Days 6 CPD hours This course is intended for This course does not have any technical knowledge prerequisites for the learners, besides being proficient in using a computer and the Internet. IT and/or AI knowledge is a benefit but not a hard requirement. Given the rapid development of AI and the broad range of its applications in everyday life, it is crucial for anyone to attend this course to update their digital skills in an ever-changing world. It is expected that all learners have registered for a free account of OpenAI ChatGPT at https://chat.openai.com. Overview Discover how AI relates to other 4th industrial revolution technologies Learn about AI, ML, and associated cognitive services Overview of AI development frameworks, tools and services Evaluate the OpenAI ChatGPT4 / ChatGPT3.5 model features in more detail The core aim of this ?AI for beginners? course is to introduce its audience to Artificial Intelligence (AI) and Machine Learning (ML) technologies and allow them to understand the practical applications of AI in their everyday personal and professional life. Moreover, the course aims to provide a handful of demos and hands-on exercises to allow the learners to familiarize themselves with usage scenarios of OpenAI ChatGPT and other Generative AI (GenAI) models. The content of this course has been created primarily by using the OpenAI ChatGPT model. AI theoretical concepts. Introduction to AI, ML, and associated cognitive services (Computer vision, Natural language processing, Speech analysis, Decision making). How AI relates to other 4th industrial revolution technologies (cloud computing, edge computing, internet of things, blockchain, metaverse, robotics, quantum computing). AI model classification by utilizing mind maps and the distinctive role of Gen AI models. Introduction to the OpenAI ChatGPT model and alternative generative AI models. Familiarization with the basics of the ChatGPT interface (https://chat.openai.com). Talking about Responsible AI: Security, privacy, compliance, copyright, legal challenges, and ethical implications. AI practical applications Overview of AI development frameworks, tools and services. AI aggregators review. Hand-picked AI tool demos: a.Workplace productivity and the case of Microsoft 365 Copilot. b.The content creation industry. Create text, code, images, audio and video with Gen AI. c.Redefining the education sector with AI-powered learning. Evaluate the OpenAI ChatGPT4 / ChatGPT3.5 model features in more detail: a.Prompting and plugin demos. b.Code interpreter demos. Closing words. Discussion with an AI model on the future of AI. Additional course details: Nexus Humans AI for beginners training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AI for beginners 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 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Science for Marketing Analytics 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.

Learn to build a complete Internet of Things solution with Arduino, a SQL Database, and a web server

Duration 3 Days 18 CPD hours This course is intended for This course is designed for existing Python programmers who have at least one year of Python experience and who want to expand their programming proficiency in Python 3. Overview In this course, you will expand your Python proficiencies. You will: Select an object-oriented programming approach for Python applications. Create object-oriented Python applications. Create a desktop application. Create a data-driven application. Create and secure web service-connected applications. Program Python for data science. Implement unit testing and exception handling. Package an application for distribution.   Python continues to be a popular programming language, perhaps owing to its easy learning curve, small code footprint, and versatility for business, web, and scientific uses. Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you'll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, creating web service-connected apps, performing data science tasks, unit testing, and creating and installing packages and executable applications. Selecting an Object-Oriented Programming Approach for Python Applications Topic A: Implement Object-Oriented Design Topic B: Leverage the Benefits of Object-Oriented Programming Creating Object-Oriented Python Applications Topic A: Create a Class Topic B: Use Built-in Methods Topic C: Implement the Factory Design Pattern Creating a Desktop Application Topic A: Design a Graphical User Interface (GUI) Topic B: Create Interactive Applications Creating Data-Driven Applications Topic A: Connect to Data Topic B: Store, Update, and Delete Data in a Database Creating and Securing a Web Service-Connected App Topic A: Select a Network Application Protocol Topic B: Create a RESTful Web Service Topic C: Create a Web Service Client Topic D: Secure Connected Applications Programming Python for Data Science Topic A: Clean Data with Python Topic B: Visualize Data with Python Topic C: Perform Linear Regression with Machine Learning Implementing Unit Testing and Exception Handling Topic A: Handle Exceptions Topic B: Write a Unit Test Topic C: Execute a Unit Test Packaging an Application for Distribution Topic A: Create and Install a Package Topic B: Generate Alternative Distribution Files Additional course details: Nexus Humans Advanced Programming Techniques with Python 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 Advanced Programming Techniques with Python 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.

Master MongoDB, an open-source document database and leading NoSQL database that provides high performance, high availability, and automatic scaling. This course covers the MongoDB Community version for beginners and provides over 50 live-running queries, including creating new databases and tables.

Duration 3 Days 18 CPD hours This course is intended for This course is for Network, IT security, and systems administration professionals in a Security Operations position who are tasked with configuring optimum security settings for endpoints protected by Symantec Endpoint Protection 14. Overview At the completion of the course, you will be able to: Protect against Network Attacks and Enforcing Corporate Policies using the Firewall Policy. Blocking Threats with Intrusion Prevention. Introducing File-Based Threats. Preventing Attacks with SEP. Layered Security. Securing Windows Clients. Secure Mac Clients. Secure Linux Clients. Controlling Application and File Access. Restricting Device Access for Windows and Mac Clients. Hardening Clients with System Lockdown. Customizing Policies based on Location. Managing Security Exceptions. This course is designed for the network, IT security, and systems administration professionals in a Security Operations position who are tasked with configuring optimum security settings for endpoints protected by Symantec Endpoint Protection 14. Introduction Course environment Lab environment Introducing Network Threats Describing how Symantec Endpoint Protection protects each layer of the network stack Discovering the tools and methods used by attackers Describing the stages of an attack Protecting against Network Attacks and Enforcing Corporate Policies using the Firewall Policy Preventing network attacks Examining Firewall Policy elements Evaluating built-in rules Creating custom firewall rules Enforcing corporate security policy with firewall rules Blocking network attacks using protection and stealth settings Configuring advanced firewall feature Blocking Threats with Intrusion Prevention Introducing Intrusion Prevention technologies Configuring the Intrusion Prevention policy Managing custom signatures Monitoring Intrusion Prevention events Introducing File-Based Threats Describing threat types Discovering how attackers disguise their malicious applications Describing threat vectors Describing Advanced Persistent Threats and a typical attack scenario Following security best practices to reduce risks Preventing Attacks with SEP Layered Security Virus and Spyware protection needs and solutions Describing how Symantec Endpoint Protection protects each layer of the network stack Examining file reputation scoring Describing how SEP protects against zero-day threats and threats downloaded through files and email Describing how endpoints are protected with the Intelligent Threat Cloud Service Describing how the emulator executes a file in a sandbox and the machine learning engine?s role and function Securing Windows Clients Platform and Virus and Spyware Protection policy overview Tailoring scans to meet an environment?s needs Ensuring real-time protection for clients Detecting and remediating risks in downloaded files Identifying zero-day and unknown threats Preventing email from downloading malware Configuring advanced options Monitoring virus and spyware activity Securing Mac Clients Touring the SEP for Mac client Securing Mac clients Monitoring Mac clients Securing Linux Clients Navigating the Linux client Tailoring Virus and Spyware settings for Linux clients Monitoring Linux clients Providing Granular Control with Host Integrity Ensuring client compliance with Host Integrity Configuring Host Integrity Troubleshooting Host Integrity Monitoring Host Integrity Controlling Application and File Access Describing Application Control and concepts Creating application rulesets to restrict how applications run Monitoring Application Control events Restricting Device Access for Windows and Mac Clients Describing Device Control features and concepts for Windows and Mac clients Enforcing access to hardware using Device Control Discovering hardware access policy violations with reports, logs, and notifications Hardening Clients with System Lockdown What is System Lockdown? Determining to use System Lockdown in Whitelist or Blacklist mode Creating whitelists for blacklists Protecting clients by testing and Implementing System Lockdown Customizing Policies based on Location Creating locations to ensure the appropriate level of security when logging on remotely Determining the criteria and order of assessment before assigning policies Assigning policies to locations Monitoring locations on the SEPM and SEP client Managing Security Exceptions Creating file and folder exceptions for different scan types Describing the automatic exclusion created during installation Managing Windows and Mac exclusions Monitoring security exceptions