2378 Programming courses delivered Online

Duration 2 Days 12 CPD hours This course is intended for This class assumes some prior experience with Git, plus basic coding or programming knowledge. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment led by our expert team, students will explore: Getting Started with Collaboration Understanding the GitHub Flow Branching with Git Local Git Configuration Working Locally with Git Collaborating on Your Code Merging Pull Requests Viewing Local Project History Streaming Your Workflow with Aliases Workflow Review Project: GitHub Games Resolving Merge Conflicts Working with Multiple Conflicts Searching for Events in Your Code Reverting Commits Helpful Git Commands Viewing Local Changes Creating a New Local Repository Fixing Commit Mistakes Rewriting History with Git Reset Merge Strategies: Rebase This is a fast-paced hands-on course that provides you with a solid overview of Git and GitHub, the web-based version control repository hosting service. While the examples in this class are related to computer code, GitHub can be used for other content. It offers the complete distributed version control and source code management (SCM) functionality of Git as well as adding its own features. It provides access control and several collaboration features such as bug tracking, feature requests, task management, and wikis for every project. Getting Started with The GitHub Ecosystem What is Git? Exploring a GitHub Repository Using GitHub Issues Activity: Creating A GitHub Issue Using Markdown Understanding the GitHub Flow The Essential GitHub Workflow Branching with Git Branching Defined Activity: Creating a Branch with GitHub Introduction Class Diagram Interaction Diagrams Sequence Diagrams Communication Diagrams State Machine Diagrams Activity Diagram Implementation Diagrams Local Git Configuration Checking your Git version Git Configuration Levels Viewing your configurations Configuring your username and email Configuring autocrif Working Locally with Git Creating a Local copy of the repo Our favorite Git command: git status Using Branches locally Switching branches Activity: Creating a New File The Two Stage Commit Collaborating on Your Code Collaboration Pushing your changes to GitHub Activity: Creating a Pull Request Exploring a Pull Request Activity: Code Review Merging Pull Requests Merge Explained Merging Your Pull Request Updating Your Local Repository Cleaning Up the Unneeded Branches Viewing Local Project History Using Git Log Streaming Your Workflow with Aliases Creating Custom Aliases Workflow Review Project: GitHub Games User Accounts vs. Organization Accounts Introduction to GitHub Pages What is a Fork? Creating a Fork Workflow Review: Updating the README.md Resolving Merge Conflicts Local Merge Conflicts Working with Multiple Conflicts Remote Merge Conflicts Exploring Searching for Events in Your Code What is GitHub? What is Git bisect? Finding the bug in your project Reverting Commits How Commits are made Safe operations Reverting Commits Helpful Git Commands Moving and Renaming Files with Git Staging Hunks of Changes Viewing Local Changes Comparing changes with the Repository Creating a New Local Repository Initializing a new local repository Fixing Commit Mistakes Revising your last commit Rewriting History with Git Reset Understanding reset Reset Modes Reset Soft Reset Mixed Reset Hard Does gone really mean gone? Getting it Back You just want that one commit Oops, I didn?t mean to reset Merge Strategies: Rebase About Git rebase Understanding Git Merge Strategies Creating a Linear History Additional course details: Nexus Humans Introduction to GITHub for Developers (TTDV7551) 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 Introduction to GITHub for Developers (TTDV7551) 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.

Would You Like To Master Sales, Influence & Persuasion And Make More Money? Did you know that there are some secret NLP techniques that are used by the top salespeople for sales, influence & persuasion? Would you like to learn them and become a master at sales, persuasion & influence? The ability to sell and persuade your clients will give you the success you want faster than ever! Learning how to sell and persuade people is a skill that everyone can learn. The problem is that most people learn sales, influence and persuasion the wrong way. In this course, I'll share with you the most effective NLP techniques for Sales, Persuasion & Influence. These NLP techniques I'll be sharing are only known by the top salespeople and some of these techniques are so effective, that they should be forbidden. Here is what you'll learn in this course: The Most Powerful NLP Techniques For Sales, Influence & Persuasion The Right Mindset To Have Unlimited Confidence At Selling How To Develop Lasting Rapport With Anyone Instantly The Art Of Asking Questions For Sales, Influence & Persuasion How To Close How to Deal With Sales Objections Anything Else You Need To Master Sales, Persuasion & Influence NLP, Sales, Influence & Persuasion Tactics And so much more! You'll learn: sales techniques, sales strategies, sales questions, sales structure, sales methods,... This course is for you if you'd like to learn the most powerful NLP, sales and persuasion techniques. Go ahead enroll now, This course is different because you'll have the most practical NLP techniques for sales, influence and persuasion. That's why I want you to try the course risk free, you can enroll now and watch the course. If you don't like it you can ask your money back within 30 days. Go ahead and enroll now. You can only gain NLP, sales, influence and persuasion skills! What you'll learn : The Most Powerful NLP Techniques For Sales, Influence & Persuasion The Right Mindset To Have Unlimited Confidence At Selling How To Develop Lasting Rapport With Anyone Instantly The Art Of Asking Questions For Sales, Influence & Persuasion How To Close How to Deal With Sales Objections Anything Else You Need To Master Sales, Persuasion & Influence NLP, Sales, Influence & Persuasion Tactics Requirements : A willingness to apply the NLP techniques Who is the target audience ? Anyone interested in learning the most powerful NLP techniques for sales, persuasion and influence Sales people, marketer, coaches, speakers, consultants and any profession that needs sales, influence or persuasion Anyone interested in NLP, sales, influence & persuasion NLP For Sales, Persuasion & Influence What You Will Learn In This Course FREE 00:01:00 Positive Mind Pictures- Hack Your Brain 00:02:00 Assume The Sale- The Sales Mindset 00:02:00 Create Powerful Anchors- Never Second Guess Yourself Again! 00:05:00 Mindset For Sales- Unlimited Confidence & Certainty 00:02:00 Pace & Lead Technique- Instant Rapport & Communication Skills 00:02:00 Non Verbal Mirroring- The Unfair Advantage To Build Rapport 00:03:00 The Similar Words Technique- Hace Their Brain So Your Customers Like You! 00:02:00 The Commond Denominator Technique- Create a Feeling Of Trust Instantly 00:01:00 Using V, A, K Words- A Powerful Technique To Sell Effortlessly 00:05:00 Using Eye Cues- Read Their Subconscious & That At Your Advantage 00:05:00 Build Yes Sets- Make the Sale Easy! 00:01:00 The Art Of Asking Questions + What You Need To Ask 00:02:00 Discover Their Vaues- Want to Sell Effortlessly_ You Need This Technique! 00:02:00 Buiyng Patterns- Dark Psychology 00:03:00 Action Verbs + Language Patterns- Speak Directly To The Subconscious 00:03:00 Establish A Need + Value It- Know If They Are Interested! 00:02:00 Presuppositions- Hack Their Perceptions About Your Prodcut 00:03:00 Link Your Offer To Their Need- How To Do It The Right Way 00:01:00 Repeat Client's Values- Influence Them Instantly To Buy 00:02:00 Use Their Own Buying Strategy So They Buy! Use Their Own Buying Strategy So They Buy! 00:02:00 Closing Techniques- Want The Best Ways To Close The Sale 00:05:00 Most Common Objections- How To Deal With Them 00:02:00 Pace & Lead To Destroy Objections 00:03:00 Context Reframing- The Art Of Handling Objections 00:02:00 Future Pacing- Make The Customer Experience The Results In Advance 00:01:00 Motivate Them By Negative States 00:02:00 3rd Party Authority 00:01:00 Incremental Persuasion 00:01:00

A beginner's level course that will help you learn data engineering techniques for building metadata-driven frameworks with Azure data engineering tools such as Data Factory, Azure SQL, and others. You need not have any prior experience in Azure Data Factory to take up this course.

Want to learn how to use Maven and SonarQube effectively for code building and code quality analysis as a DevOps engineer? Then you are in the right place. This learner-centered hands-on course will help you gain confidence in using important DevOps tools such as SVN, Maven, Jenkins, Chef, Puppet, Nagios, Splunk, Selenium, and more. Some basic knowledge of Linux, Git, and AWS EC2 will help you get the most out of this course.

Pre-process and Analyze Satellite Remote Sensing Data with Free Software

Data scraping is used to get the data available on different websites and APIs. This also involves automating the web flows to extract the data from different web pages. Data Scraping and Data Mining with Python is a well-designed course for beginners to develop a solid groundwork for the skills necessary.

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.

Learn how create and structure enterprise projects and programmes. Course overview Duration: 2 days (13 hours) Our P6 Project Planning and Controls Fundamentals course is an intensive two day course aimed at experienced planners and project controllers who need to use Primavera to create and manage detailed plans. It includes creating EPS levels, projects, WBS levels and detailed activity and resource planning. Experience of project planning and scheduling techniques is essential. Objectives  By the end of the course you will be able to: Create a programme structure Create projects and set project properties Create programme milestones Create a Work Breakdown Structure (WBS) Create detailed plans including activities, links and resources Progress the schedule Manage actuals Customise layouts Use the reporting tools in Primavera Content Programme Management Creating EPS elements Defining the programme structure Navigating the EPS structure Finding programmes Project Management and WBS Creating projects Setting project properties Validating projects Assigning project codes Building a work breakdown structure Creating a WBS structure Creating WBS elements Work package management Top Down budgets Allocating top down budgets Budget change Programming milestones and activity planning Creating programme milestones Setting constraints Linking milestones Scheduling Using the schedule function Detailed activity planning Creating activities Relationship types Creating relationships Adding milestones Assigning activity codes Resourcing, workloads and baselining Resource types Creating resources Resource attributes Assigning resources Switching resources Split load resource assignment Reduced hours resource assignment Checking workload Reviewing workload Dealing with resource conflicts Assignments view Baselining Creating baselines Assigning baselines Working with layouts Creating layouts Customising columns Setting filters Sorting and grouping Changing the timescale Customising the Gantt Creating activity code breakdown structures Progressing the schedules Updating task status and remaining duration Setting the data date Monitoring and reporting Exporting and importing information Primavera standard reports Creating custom reports Creating portfolios Printing Printing your schedule Printing to other packages

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 5 Days 30 CPD hours This course is intended for Data center administrators Data center engineers Systems engineers Server administrators Network managers Cisco integrators and partners Data center designers Technical solutions architects Network architects Overview After taking this course, you should be able to: Describe the foundations of data center networking Describe Cisco Nexus products and explain the basic Cisco NX-OS functionalities and tools Describe Layer 3 first-hop redundancy Describe Cisco FEX connectivity Describe Ethernet port channels and vPCs Introduce switch virtualization, machine virtualization, and describe network virtualization Compare storage connectivity options in the data center Describe Fibre Channel communication between the initiator server and the target storage Describe Fibre Channel zone types and their uses Describe NPV and NPIV Describe data center Ethernet enhancements that provide a lossless fabric Describe FCoE Describe data center server connectivity Describe Cisco UCS Manager Describe the purpose and advantages of APIs Describe Cisco ACI Describe the basic concepts of cloud computing The Understanding Cisco Data Center Foundations (DCFNDU) v1.1 course helps you prepare for entry-level data center roles. In this course, you will learn the foundational knowledge and skills you need to configure Cisco© data center technologies including networking, virtualization, storage area networking, and unified computing. You will get an introduction to Cisco Application Centric Infrastructure (Cisco ACI), automation and cloud computing. You will get hands-on experience with configuring features on Cisco Nexus Operating System (Cisco NX-OS) and Cisco Unified Computing System (Cisco UCS). This course does not lead directly to a certification exam, but it does cover foundational knowledge that can help you prepare for several CCNP and other professional-level data center courses and exams. Describing the Data Center Network Architectures Cisco Data Center Architecture Overview Three-Tier Network: Core, Aggregation, and Access Spine-and-Leaf Network Two-Tier Storage Network Describing the Cisco Nexus Family and Cisco NX-OS Software Cisco Nexus Data Center Product Overview Cisco NX-OS Software Architecture Cisco NX-OS Software CLI Tools Cisco NX-OS Virtual Routing and Forwarding Describing Layer 3 First-Hop Redundancy Default Gateway Redundancy Hot Standby Router Protocol Virtual Router Redundancy Protocol Gateway Load Balancing Protocol Describing Cisco FEX Server Deployment Models Cisco FEX Technology Cisco FEX Traffic Forwarding Cisco Adapter FEX Describing Port Channels and vPCs Ethernet Port Channels Virtual Port Channels Supported vPC Topologies Describing Switch Virtualization Cisco Nexus Switch Basic Components Virtual Routing and Forwarding Cisco Nexus 7000 VDCs VDC Types VDC Resource Allocation VDC Management Describing Machine Virtualization Virtual Machines Hypervisor VM Manager Describing Network Virtualization Overlay Network Protocols VXLAN Overlay VXLAN BGP EVPN Control Plane VXLAN Data Plane Cisco Nexus 1000VE Series Virtual Switch VMware vSphere Virtual Switches Introducing Basic Data Center Storage Concepts Storage Connectivity Options in the Data Center Fibre Channel Storage Networking VSAN Configuration and Verification Describing Fibre Channel Communication Between the Initiator Server and the Target Storage Fibre Channel Layered Model FLOGI Process Fibre Channel Flow Control Describing Fibre Channel Zone Types and Their Uses Fibre Channel Zoning Zoning Configuration Zoning Management Describing Cisco NPV Mode and NPIV Cisco NPV Mode NPIV Mode Describing Data Center Ethernet Enhancements IEEE Data Center Bridging Priority Flow Control Enhanced Transmission Selection DCBX Protocol Congestion Notification Describing FCoE Cisco Unified Fabric FCoE Architecture FCoE Initialization Protocol FCoE Adapters Describing Cisco UCS Components Physical Cisco UCS Components Cisco Fabric Interconnect Product Overview Cisco IOM Product Overview Cisco UCS Mini Cisco IMC Supervisor Cisco Intersight Describing Cisco UCS Manager Cisco UCS Manager Overview Identity and Resource Pools for Hardware Abstraction Service Profiles and Service Profile Templates Cisco UCS Central Overview Cisco HyperFlex Overview Using APIs Common Programmability Protocols and Methods How to Choose Models and Processes Describing Cisco ACI Cisco ACI Overview Multitier Applications in Cisco ACI Cisco ACI Features VXLAN in Cisco ACI Unicast Traffic in Cisco ACI Multicast Traffic in Cisco ACI Cisco ACI Programmability Common Programming Tools and Orchestration Options Describing Cloud Computing Cloud Computing Overview Cloud Deployment Models Cloud Computing Services Lab outline Explore the Cisco NX-OS CLI Explore Topology Discovery Configure HSRP Configure vPCs Configure VRF Explore the VDC Elements Install ESXi and vCenter Configure VSANs Validate FLOGI and FCNS Configure Zoning Configure Unified Ports on a Cisco Nexus Switch and Implement FCoE Explore the Cisco UCS Server Environment Configure a Cisco UCS Service Profile Configure Cisco NX-OS with APIs Explore the Cisco UCS Manager XML API Management Information Tree Explore Cisco ACI