10166 Courses

Duration 3 Days 18 CPD hours This course is intended for If you have worked in C++ but want to learn how to make the most of this language, especially for large projects, this course is for you. Overview By the end of this course, you'll have developed programming skills that will set you apart from other C++ programmers. After completing this course, you will be able to: Delve into the anatomy and workflow of C++ Study the pros and cons of different approaches to coding in C++ Test, run, and debug your programs Link object files as a dynamic library Use templates, SFINAE, constexpr if expressions and variadic templates Apply best practice to resource management This course begins with advanced C++ concepts by helping you decipher the sophisticated C++ type system and understand how various stages of compilation convert source code to object code. You'll then learn how to recognize the tools that need to be used in order to control the flow of execution, capture data, and pass data around. By creating small models, you'll even discover how to use advanced lambdas and captures and express common API design patterns in C++. As you cover later lessons, you'll explore ways to optimize your code by learning about memory alignment, cache access, and the time a program takes to run. The concluding lesson will help you to maximize performance by understanding modern CPU branch prediction and how to make your code cache-friendly. Anatomy of Portable C++ Software Managing C++ Projects Writing Readable Code No Ducks Allowed ? Types and Deduction C++ Types Creating User Types Structuring our Code No Ducks Allowed ? Templates and Deduction Inheritance, Polymorphism, and Interfaces Templates ? Generic Programming Type Aliases ? typedef and using Class Templates No Leaks Allowed ? Exceptions and Resources Exceptions in C++ RAII and the STL Move Semantics Name Lookup Caveat Emptor Separation of Concerns ? Software Architecture, Functions, and Variadic Templates Function Objects and Lambda Expressions Variadic Templates The Philosophers' Dinner ? Threads and Concurrency Synchronous, Asynchronous, and Threaded Execution Review Synchronization, Data Hazards, and Race Conditions Future, Promises, and Async Streams and I/O File I/O Implementation Classes String I/O Implementation I/O Manipulators Making Additional Streams Using Macros Everybody Falls, It's How You Get Back Up ? Testing and Debugging Assertions Unit Testing and Mock Testing Understanding Exception Handling Breakpoints, Watchpoints, and Data Visualization Need for Speed ? Performance and Optimization Performance Measurement Runtime Profiling Optimization Strategies Cache Friendly Code

Duration 2 Days 12 CPD hours This course is intended for This course is for analysts, developers, and administrators of IBM Watson Explorer Deep Analytics Edition oneWEX. Overview Identify oneWEX platformsIdentify the process and data flows of oneWEX projectsExplore the oneWEX user interfaceExplain ingestion and conversionUtilize Content MinerDefine enrichmentIdentify advanced features of oneWEX This course is designed to teach students core concepts of IBM Watson Explorer Deep Analytics Edition oneWEX. Students will learn to identify the oneWEX platforms as well as the process flow and data flow of oneWEX projects. Students will explore oneWEX tools, such as Content Miner and the Admin Console, while gaining hands-on experience in data acquisition and enrichment. Finally, students will be exposed to more advanced topics, such as Application Builder, Content Analytics Studio, and API usage. Overview of oneWEX Introduction to oneWEX Explore oneWEX architecture Identify installation options Navigation in oneWEX Explore the Admin Console Explore navigation using Content Miner The Collection detail view The REST API Data flow Explore the data flow of oneWEX Search and Analytics collection templates Identify data acquisition Data ingestion Work with datasets Work with crawlers Use an importer Explore conversion Data ingestion log files Analysis using oneWEX Content Miner Explore analysis using Content Miner The Guided Analysis Experience The Guided Analysis view Explore Annotators Enrichment using Annotators Annotator types Enrichment using Labeler Identify enrichment Identify document classification Classify using training data Classification versus clustering The document classification process Enrichment using Ranker Identify enrichment using Ranker The ranking process Migrate annotators from Content Analytics Studio Migrate Content Analytics Studio annotators Identify the UIMA pipeline configuration for oneWEX Update annotators Using Application Builder with oneWEX Application Builder and user roles Explore Application Builder Set up a oneWEX data source Functionality for oneWEX data sources Additional course details: Nexus Humans O3201 Fundamentals of IBM Watson Explorer Deep Analytics Edition oneWEX (V12.0.x) 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 O3201 Fundamentals of IBM Watson Explorer Deep Analytics Edition oneWEX (V12.0.x) 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 2.75 Days 16.5 CPD hours This course is intended for Complete beginners who have never programmed before to experienced developers coming from another programming language. Overview You will learn how to leverage the power of Python to solve tasks. You will build games and programs that use Python libraries. You will be able to use Python for your own work problems or personal projects. You will create a portfolio of Python based projects you can share. Learn to use Python professionally, learning both Python 2 and Python 3! Create games with Python, like Tic Tac Toe and Blackjack! Learn advanced Python features, like the collections module and how to work with timestamps! Learn to use Object Oriented Programming with classes! Understand complex topics, like decorators. Understand how to use both the Jupyter Notebook and create .py files Get an understanding of how to create GUIs in the Jupyter Notebook system! Build a complete understanding of Python from the ground up! Our Introduction to Python course is designed to take complete beginners or experienced developers up to speed on Python?s capabilities, setting up students for success in using Python for their specific field of expertise. Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. Learn how to use Python for real-world tasks, such as working with PDF Files, sending emails, reading Excel files, scraping websites for information, working with image files, and much more! This course will teach you Python in a practical manner and provides a full coding screencast and a corresponding code notebook to review the concepts and exercises conducted in class. Please note, this course is able to be offered in either 3 full day sessions or 5 partial day sessions. See the schedule below. This course includes 6-months access to the full course content in on-demand format to support post-class reference and review. Command Line Basics Python System Setup Jupyter Notebooks Python Data Types Key Data Structures Logic and Control Flow Functions Debugging Modules Object Oriented Programming File I/O Testing Decorators Generators Automation of Tasks Web Scraping Graphical User Interfaces Additional course details: Nexus Humans Introduction to 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 Introduction to 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.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for IT professionals with experience or interest in delivering Field Service solutions for large-scale customers. Overview Identify the key components involved in Field Service Implementations. Define the products and services that will be delivered to customers. Determine which pricing options to use in specific scenarios. Determine which resources are required. Dynamics 365 Field Service helps organizations better position themselves in the market by providing a variety of tools that assist in identifying and scheduling resources and managing workloads for mobile workers. This course will equip students with the skills necessary to identify and configure the key components that are used to deliver Field Service and mobile solutions. Key topics include identifying the organizational considerations that will drive configuration decisions and common configuration aspects. This course helps students better understand the bigger picture and end goals focused around implementations that aid in designing more efficient solutions that align with customer and organizational goals. Configure Field Service Introduction to configuring Field Service Defining products and services Defining tax codes Resource Scheduling Configuration Mapping and location information Configuring resource components Defining account preferences Defining and Configuring Bookable Resources Defining bookable resources Resource pools, crews and facilities Configure Incidents Creating an incident Using service tasks Inventory and Work Order Management Configure Field Service work orders Creating work orders Managing work orders Field Service Agreements Using Field Service agreements Set up bookings Set up invoices Inventory and Purchasing Manage customer assets Manage inventory Purchasing and returns Field Service Mobile Mobile client overview Install and deploy Field Service mobile projects Manage mobile projects Deploy the mobile client Universal Resource Scheduling URS overview and configuration Enabling entities for URS Customize entities for URS Managing Scheduling Options Using the schedule board Schedule items Rescheduling and substituting resources Crew and pool scheduling Customizing the Schedule Board Configure the board Create additional schedule boards Use views to enhance the schedule board Configuring schedule board queries and filters Working with requirement groups Advanced Scheduling Options Working with resource scheduling optimization Defining optimization goals Defining optimization scopes Defining optimization profiles Single resource scheduling Additional course details: Nexus Humans MB-240T00 Dynamics 365 for Field Service 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 MB-240T00 Dynamics 365 for Field Service 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 This is an Introductory level course for experienced Linux system administrators, DevOps engineers, infrastructure automation engineers, and systems design engineers. Ideally students should have familiarity with basic Python scripting. Attendees without programming skills can follow along with the scripting portion of the labs. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert practitioner attendees will explore how to: Describe Ansible concepts and install Red Hat Ansible Engine (optional - we can pre-install is as well if desired, depending on the audience) Deploy Ansible and Configure Ansible to manage hosts and run ad hoc Ansible commands. Implement playbooks Write a simple Ansible playbook and run it to automate tasks on multiple managed hosts. Manage variables and facts Write playbooks that use variables to simplify management of the playbook and facts to reference information about managed hosts. Implement task control; Manage task control, handlers, and task errors in Ansible playbooks. Deploy files to managed hosts Deploy, manage, and adjust files on hosts managed by Ansible. Manage large projects Write playbooks that are optimized for larger, more complex projects. Simplify playbooks with roles Use Ansible roles to develop playbooks more quickly and to reuse Ansible code. Troubleshoot Ansible Troubleshoot playbooks and managed hosts. Automate Linux administration tasks Automate common Linux system administration tasks with Ansible This lab-intensive course is geared toward those responsible for automation of configuration management; consistent and repeatable application deployment; provisioning and deployment of development, testing, and production servers; and integration with DevOps CI/CD workflows. Throughout the course you will explore core Ansible features such as automatic provisioning, configuration management, service deployment and operational processes. Ansible Overview Overview of Architecture Overview of Deployments Inventory Deploying Ansible Installing Configuration Files Running Ad Hoc Commands Dynamic Inventory Playbooks Writing YAML Files Modules Variables and Inclusions Variables Facts Inclusions Task Control Constructing Flow Control Handlers Tags Handling Errors Jinja2 Templates Jinja2 Templates Jinja2 Templates Roles Role Structure Creating Roles Deploying Roles with Ansible Galaxy Optimizing Ansible Configuring Connection Types Configuring Delegation Configuring Parallelism Ansible Vault Configuring Ansible Vault Executing with Ansible Vault Troubleshooting Ansible Troubleshooting Playbooks Troubleshooting Managed Hosts Ansible Tower Ansible Tower overview Installing Account management Hosts Jobs Optional: Ansible in a DevOps Environment Provisioning Vagrant Machines Deploying Vagrant in a DevOps Environment Deploying Docker in a DevOps Environment Additional course details: Nexus Humans Introduction to Ansible: Automation with Ansible (TTDV7580) 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 Ansible: Automation with Ansible (TTDV7580) 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 5 Days 30 CPD hours This course is intended for Audience for this course This course is designed for system administrators responsible for creating OpenShift Enterprise instances, deploying applications, creating process customizations, managing instances and projects. Prerequisites for this course Have taken Red Hat Enterprise Linux Administration I and II (RH124 and RH134), or equivalent Red Hat Enterprise Linux© system administration experience Be certified as a Red Hat Certified System Administrator (RHCSA), or equivalent Red Hat Enterprise Linux system administration experience Be certified as a Red Hat Certified Engineer (RHCE©) Overview Learn to install, configure, and manage OpenShift Enterprise by Red Hat instances - OpenShift Enterprise Administration (DO280) prepares the system administrator to install, configure, and manage OpenShift Enterprise by Red Hat© instances. OpenShift Enterprise, Red Hat's platform-as-a-service (PaaS) offering, provides pre-defined deployment environments for applications of all types through its use of container technology. This creates an environment that supports DevOps principles such as reduced time to market and continuous delivery. - In this course, students will learn how to install and configure an instance of OpenShift Enterprise, test the instance by deploying a real world application, and manage projects/applications through hands-on labs. - Course content summary - Container concepts - Configuring resources with the command line interface - Building a pod - Enabling services for a pod - Creating routes - Downloading and configuring images - Rolling back and activating deployments - Creating custom S2I images This course will empower you to install and administer the Red Hat© OpenShift© Container Platform, with hands-on, lab-based materials that show you how to install, configure, and manage OpenShift clusters and deploy sample applications to further understand how developers will use the platform. This course is based on Red Hat© Enterprise Linux© 7.5 and Openshift Container Platform 3.9. OpenShift is a containerized application platform that allows your enterprise to manage container deployments and scale your applications using Kubernetes. OpenShift provides predefined application environments and builds upon Kubernetes to provide support for DevOps principles such as reduced time to market, infrastructure-as-code, continuous integration (CI), and continuous delivery (CD). 1 - INTRODUCTION TO RED HAT OPENSHIFT ENTERPRISE Review features and architecture of OpenShift Enterprise. 2 - INSTALL OPENSHIFT ENTERPRISE Install OpenShift Enterprise and configure a master and node. 3 - EXECUTE COMMANDS Execute commands using the command line interface. 4 - BUILD APPLICATIONS Create, build, and deploy applications to an OpenShift Enterprise instance. 5 - PERSISTENT STORAGE Provision persistent storage and use it for the internal registry. 6 - BUILD APPLICATIONS WITH SOURCE-TO-IMAGE (S2I) Create and build applications with S2I and templates. 7 - MANAGE THE SYSTEM Use OpenShift Enterprise components to manage deployed applications. 8 - CUSTOMIZE OPENSHIFT ENTERPRISE Customize resources and processes used by OpenShift Enterprise. 9 - COMPREHENSIVE REVIEW Practice and demonstrate knowledge and skills learned in the course. 10 - NOTE: Course outline is subject to change with technology advances and as the nature of the underlying job evolves. For questions or confirmation on a specific objective or topic, please contact us. Additional course details: Nexus Humans Red Hat OpenShift Administration II: Operating a Production Kubernetes Cluster (DO280) 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 Red Hat OpenShift Administration II: Operating a Production Kubernetes Cluster (DO280) 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 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Individuals planning to deploy applications and create application environments on Google Cloud Platform Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs. Overview This course teaches participants the following skills: Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing,storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences This 1-day instructor led course introduces Azure professionals to the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, Solution Architects and SysOps Administrators familiar with Azure features and setup; and want to gain experience configuring Google Cloud products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. Introducing Google Cloud Explain the advantages of Google Cloud. Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Getting Started with Google Cloud Identify the purpose of projects on Google Cloud. Understand how Azure's resource hierarchy differs from Google Cloud's Understand the purpose of and use cases for Identity and Access Management. Understand how Azure AD differs from Google Cloud IAM. List the methods of interacting with Google Cloud. Launch a solution using Cloud Marketplace. Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine Understand the basics of networking in Google Cloud. Understand how Azure VPC differs from Google VPC. Understand the similarities and differences between Azure VM and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in Azure. Deploy applications using Google Compute Engine Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Azure Blob compares to Cloud Storage. Compare Google Cloud?s managed database services with Azure SQL. Learn how to choose among the various storage options on Google Cloud. Load data from Cloud Storage into BigQuery Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Azure Kubernetes Service differs from from Google Kubernetes Engine. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Azure App Service. Understand the purpose of and use cases for Google Cloud Endpoints. Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Google Cloud Deployment Manager differs from Azure Resource Manager. Understand the purpose of integrated monitoring, alerting, and debugging Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from Azure Data Lake. Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus. Understand how Google Cloud?s machine-learning APIs differ from Azure's. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data Summary and Review Review the products that make up Google Cloud and remember how to choose among them Understand next steps for training and certification Understand, at a high level, the process of migrating from Azure to Google Cloud.

Duration 3 Days 18 CPD hours This course is intended for This course is appropriate for advanced users, system administrators and web site administrators who want to use Python to support their server installations, as well as anyone else who wants to automate or simplify common tasks with the use of Python scripts. Students can apply the course skills to use Python in basic web development projects or automate or simplify common tasks with the use of Python scripts. Overview This skills-focused course is about 50% hands-on lab to lecture ratio, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert instructor, you'll learn how to: Create working Python scripts following best practices Use python data types appropriately Read and write files with both text and binary data Search and replace text with regular expressions Work with with the standard library and its work-saving modules Create 'real-world', professional Python applications Know when to use collections such as lists, dictionaries, and sets Work with Pythonic features such as comprehensions and iterators Write robust code using exception handling Introduction to Python Programming Basics is a hands-on Python programming course that teaches you the key skills you?ll need to get started with programming in Python to a solid foundational level. The start of the course will lead you through writing and running basic Python scripts, and then guide you through how to use more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting. This course provides you with an excellent kick start for users new to Python and scripting, enabling you to quickly use basic Python skills on the job in a variety of ways. You?ll be able use Python in basic web development projects, or use it to automate or simplify common tasks with the use of Python scripts. The course also serves as a solid primer course / foundation for continued Python study in support for next level web development with Python, using Python in DevOps, Python for data science / machine learning or Python for systems admin or networking support. Python Quick View What is Python? Python timeline Advantages/disadvantages Installing Python Getting help The Python Environment Starting Python Using the interpreter Running a Python script Editors and IDEs Getting Started with Python Using variables Builtin functions String data Numberic data Converting types Console input/output Command line parameters Flow Control About flow control The if statement Relational and Boolean operators while loops Exiting from loops Array Types About array types Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions and generators Working with Files File overview Opening a text file Reading a text file Writing to a text file Dictionaries and Sets About dictionaries Creating dictionaries Iterating through a dictionary About sets Creating sets Working with sets Functions Defining functions Returning values Parameters and arguments Variable scope Sorting The sorted() function Custom sort keys Lambda functions Sorting in reverse Using min() and max() Errors and Exception Handling Exceptions Using try/catch/else/finally Handling multiple exceptions Ignoring exceptions Modules and Packages Creating Modules The import statement Module search path Using packages Function and module aliases Getting Started with Object Oriented Programming and Classes About object-oriented programming Defining classes Constructors Understanding self Properties Instance Methods and data Class methods and data Inheritance Additional course details: Nexus Humans Introduction to Python Programming Basics (TTPS4800) 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 Python Programming Basics (TTPS4800) 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 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 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Individuals planning to deploy applications and create application environments on Google Cloud Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview This course teaches participants the following skills: Identify Google Cloud counterparts for AWS IaaS, AWS PaaS, AWS SQL, AWS Blob Storage, AWS Application Insights, and AWS Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences This course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants will get details of similarities, differences, and initial how-tos quickly. Introducing Google Cloud Explain the advantages of Google Cloud. Define the components of Google's network infrastructure,including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Getting Started with Google Cloud Identify the purpose of projects on Google Cloud Platform. Understand how AWS?s resource hierarchy differs from Google Cloud?s. Understand the purpose of and use cases for Identity and Access Management. Understand how AWS IAM differs from Google Cloud IAM. List the methods of interacting with Google Cloud Platform. Launch a solution using Cloud Marketplace. Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Understand how Amazon VPC differs from Google VPC. Understand the similarities and differences between Amazon EC2 and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in AWS. Deploy applications using Google Compute Engine. Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage,Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Amazon S3 and Amazon Glacier compare to Cloud Storage. Compare Google Cloud?s managed database services with Amazon RDS and Amazon Aurora. Learn how to choose among the various storage options on Google Cloud Platform. Load data from Cloud Storage into BigQuery. Perform a query on the data in BigQuery. Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) differ from GKE. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Amazon Elastic Beanstalk. Understand the purpose of and use cases for Google Cloud Endpoints. Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Cloud Deployment Manager differs from AWS CloudFormation. Understand the purpose of integrated monitoring, alerting, and debugging. Understand how Google Monitoring differs from Amazon CloudWatch and AWS CloudTrail. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from AWS Data Lake. Understand how Google Cloud Pub/Sub differs from AWS Event Hubs and Service Bus. Understand how Google Cloud?s machine-learning APIs differ from AWS's. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data. Summary and Review Review the products that make up Google Cloud and remember how to choose among them Understand next steps for training and certification Understand, at a high level, the process of migrating from AWS to Google Cloud. Additional course details: Nexus Humans Google Cloud Fundamentals for AWS Professionals 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 Google Cloud Fundamentals for AWS Professionals 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.