1694 Computing courses delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for This course is intended for: Solutions Architects who are new to designing and building cloud architectures Data Center Architects who are migrating from on-premises environment to cloud architectures Other IT/cloud roles who want to understand how to design and build cloud architectures Overview In this course, you will learn how to: Make architectural decisions based on AWS architectural principles and best practices Use AWS services to make your infrastructure scalable, reliable, and highly available Use AWS Managed Services to enable greater flexibility and resiliency in an infrastructure Make an AWS-based infrastructure more efficient to increase performance and reduce costs Use the Well Architected Framework to improve architectures with AWS solutions This course covers all aspects of how to architect for the cloud over four-and-a-half-days. It covers topics from Architecting on AWS and Advanced Architecting on AWS to offer an immersive course in cloud architecture. You will learn how to design cloud architectures, starting small and working to large-scale enterprise level designs-and everything in between. Starting with the Well-Architected Framework, you will learn important architecting information for AWS services including: compute, storage, database, networking, security, monitoring, automation, optimization, benefits of de-coupling applications and serverless, building for resilience, and understanding costs Module 1: Introduction The real story of AWS Well-Architected Framework Six advantages of the cloud Global infrastructure Module 2: The Simplest Architectures S3 Glacier Choosing your regions Hands-on lab: Static Website Module 3: Adding a Compute Layer EC2 Storage solutions for instances Purchasing options such as dedicated host vs instances Module 4: Adding a Database Layer Relational vs non-relational Managed databases RDS Dynamo DB Neptune Hands-on lab: Deploying a web application on AWS Module 5: Networking in AWS Part 1 VPC CIDR and subnets Public vs private subnets NAT and internet gateway Security groups Module 6: Networking in AWS Part 2 Virtual Private Gateway VPN Direct Connect VPC peering Transit Gateway VPC Endpoints Elastic Load Balancer Route 53 Hands-on lab: Creating a VPC Module 7: AWS Identity and Access Management (IAM) IAM Identity federation Cognito Module 8: Organizations Organizations Multiple account management Tagging strategies Module 9: Elasticity, High Availability, and Monitoring Elasticity vs inelasticity Monitoring with CloudWatch, CloudTrail, and VPC Flow Logs Auto scaling Scaling databases Hands-on lab: Creating a highly available environment Module 10: Automation Why automate? CloudFormation AWS Quick Starts AWS Systems Manager AWS OpsWorks AWS Elastic Beanstalk Module 11: Deployment Methods Why use a deployment method? Blue green and canary deployment Tools to implement your deployment methods CI/CD Hands-on lab: Automating infrastructure deployment Module 12: Caching When and why you should cache your data Cloudfront Elasticache (Redis/Memcached) DynamoDB Accelerator Module 13: Security of Your Data Shared responsibility model Data classification Encryption Automatic data security Module 14: Building Decoupled Architecture Tight coupling vs loose coupling SQS SNS Module 15: Optimizations and Review Review questions Best practices Activity: Design and architecture - two trues and one lie Module 16: Microservices What is a microservice? Containers ECS Fargate EKS Module 17: Serverless Why use serverless? Lambda API Gateway AWS Step Functions Hands-on lab: Implementing a serverless architecture with AWS Managed Services Module 18: Building for Resilience Using managed services greatly increases resiliency Serverless for resiliency Issues with microservices to be aware of DDoS Hands-on lab: Amazon CloudFront content delivery and automating WAF rules Module 19: Networking in AWS Part 3 Elastic Network Adapter Maximum transmission units Global Accelerator Site to site VPN Transit Gateway Module 20: Understanding Costs Simple monthly calculator Right sizing your instances Price sensitive architecture examples Module 21: Migration Strategies Cloud migration strategies Planning Migrating Optimizing Hands-on lab: Application deployment using AWS Fargate Module 22: RTO/RPO and Backup Recovery Setup Disaster planning Recovery options Module 23: Final Review Architecting advice Service use case questions Example test questions Additional course details: Nexus Humans Architecting on AWS - Accelerator 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 Architecting on AWS - Accelerator 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: Solutions architects, security DevOps, and security engineers Overview In this course, you will learn to: Establish a landing zone with AWS Control Tower Configure AWS Organizations to create a multi-account environment Implement identity management using AWS Single Sign-On users and groups Federate access using AWS SSO Enforce policies using prepackaged guardrails Centralize logging using AWS CloudTrail and AWS Config Enable cross-account security audits using AWS Identity and Access Management (IAM) Define workflows for provisioning accounts using AWS Service Catalog and AWS Security Hub Security is foundational to AWS. Governance at scale is a new concept for automating cloud governance that can help companies retire manual processes in account management, budget enforcement, and security and compliance. By automating common challenges, companies can scale without inhibiting agility, speed, or innovation. In addition, they can provide decision makers with the visibility, control, and governance necessary to protect sensitive data and systems.In this course, you will learn how to facilitate developer speed and agility, and incorporate preventive and detective controls. By the end of this course, you will be able to apply governance best practices. Course Introduction Instructor introduction Learning objectives Course structure and objectives Course logistics and agenda Module 1: Governance at Scale Governance at scale focal points Business and Technical Challenges Module 2: Governance Automation Multi-account strategies, guidance, and architecture Environments for agility and governance at scale Governance with AWS Control Tower Use cases for governance at scale Module 3: Preventive Controls Enterprise environment challenges for developers AWS Service Catalog Resource creation Workflows for provisioning accounts Preventive cost and security governance Self-service with existing IT service management (ITSM) tools Module 4: Detective Controls Operations aspect of governance at scale Resource monitoring Configuration rules for auditing Operational insights Remediation Clean up accounts Module 5: Resources Explore additional resources for security governance at scale Additional course details: Nexus Humans AWS Security Governance at Scale 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 AWS Security Governance at Scale 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 This course is intended for new and experienced programmers that want to learn how to write and troubleshoot Python code. This is the Microsoft recommended course for preparing for the 98-381 test. Previous programming experience is not required but recommended. Overview By the end of this course, you will be able to: Create Operations using Data Types and Operators Create Control Flow Operations Create Input and Output Operations Write and Document code to solve a specified problem Troubleshoot Problems and Write Error Handling Operations Perform Operations Using Modules and Tools This five-day instructor-led course (three-day boot camp) is intended for students who want to learn how to write, debug and document Python code Module 1: Perform Operations Using Data Types and Operators Assign data types to variables Perform data and data type operations Perform Arithmetic, Comparison and Logical Operations Review Module 2: Control Flow with Decisions and Loops Construct and analyze code segments that use branching statements Construct and analyze code segments that perform iterations Review Module 3: Perform Input and Output Operations Create Python code segments that perform file input and output operations Create Python code segments that perform console input and output operations Review Module 4: Document and Structure Code Construct and analyze code segments Document code segments using comments and documentation strings Review Module 5: Perform Troubleshooting and Error Handling Analyze, Detect and Fix code segments that have errors Analyze and construct code segments that handle exceptions Review Module 6: Perform Operations Using Modules and Tools Use Built-In Modules to perform basic operations Use Built-In Modules to perform complex operations Review

ITIL® 4 Foundation: Virtual In-House Training ITIL® 4 is built on the established core of best practice in the ITIL® guidance. ITIL® 4 provides a practical and flexible approach to move to the new world of digital transformation and embrace an end-to-end operating model for the delivery and operation of products and services. ITIL® 4 also provides a holistic end-to-end picture that integrates frameworks such as Lean IT, Agile, and DevOps. The ITIL® 4 Foundation is based on the exam specifications specified by AXELOS for the ITIL® 4 Foundation certification. The fundamental objective of this course is to help the participants understand the key concepts of service management and the ITIL® 4 service management framework and prepare for the ITIL® 4 Foundation exam. In addition, this course offers a rich learning experience that helps the participants relate ITIL® to their own work environment. The course includes a case study (based on a fictitious organization, 'Axle Car Hire') that will help the participants understand and experience the ITIL® guiding principles, service value, practices through real-world challenges and opportunities. The rich learning experience is supported by additional learning tools such as pre-course reading materials, post-course reading material, and a set of quick reference cards. What You Will Learn At the end of this program, you will be able to: Understand the key concepts of ITIL® service management Understand how ITIL® guiding principles can help an organization to adopt and adapt ITIL® service management Understand the four dimensions of ITIL® service management Understand the purpose and components of the ITIL® service value system, and activities of the service value chain, and how they interconnect Understand the key concepts of continual improvement Learn the various ITIL® practices and how they contribute to value chain activities Course Introduction Let's Get to Know Each Other Course Overview Course Learning Objectives Course Structure Course Agenda Introduction to IT Service Management in the Modern World Introduction to ITIL® 4 Structure and Benefits of ITIL® 4 Case Study: Axle Car Hire Case Study: Meet the Key People at Axle Case Study: The CIOs Vision for Axle Exam Details ITIL® 4 Certification Scheme Service Management - Key Concepts Intent and Context Key Terms Covered in the Module Module Learning Objectives Value and Value Co-Creation Value: Service, Products, and Resources Service Relationships Value: Outcomes, Costs, and Risks Exercise: Multiple-Choice Questions The Guiding Principles Intent and Context Identifying Guiding Principles Key Terms Covered in the Module Module Learning Objectives The Seven Guiding Principles Applying the Guiding Principles Exercise: Multiple-Choice Questions The Four Dimensions of Service Management Intent and Context The Four Dimensions Key Terms Covered in the Module The Four Dimensions and Service Value System Module Learning Objectives Organizations and People Information and Technology Partners and Suppliers Value Streams and Processes External Factors and Pestle Model Exercise: Multiple-Choice Questions Service Value System Intent and Context Service Value System and Service Value Chain Module Learning Objectives Overview of Service Value System Overview of the Service Value Chain Exercise: Multiple-Choice Questions Continual Improvement Intent and Context Key Terms Covered in the Module Introduction to Continual Improvement Module Learning Objectives The Continual Improvement Model Relationship between Continual Improvement and Guiding Principles Exercise: Multiple-Choice Questions The ITIL® Practices Intent and Context ITIL® Management Practices Key Terms Covered in the Module Module Learning Objectives The Continual Improvement Practice The Change Control Practice The Incident Management Practice The Problem Management Practice The Service Request Management Practice The Service Desk Practice The Service Level Management Practice Purpose of ITIL® Practices Exercise: Crossword Puzzle

Duration 4 Days 24 CPD hours This course is intended for This is an introductory-level C++ programming course designed for developers with experience programming in C or other languages. Practical hands-on prior programming experience and knowledge is required. Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in basic coding with C++, coupling the most current, effective techniques with the soundest industry practices. 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, guided by our expert team, attendees will learn: Writing procedural programs using C++ Using private, public and protected keywords to control access to class members Defining a class in C++ Writing constructors and destructors Writing classes with const and static class members Overloading operators Implementing polymorphic methods in programs Writing programs using file I/O and string streams Using manipulators and stream flags to format output Using the keyword template to write generic functions and classes Writing programs that use generic classes and functions Writing programs that use algorithms and containers of the Standard Library Apply object-oriented design techniques to real-world programming problems Using algorithms and containers of the Standard Library to manipulate string data Understand how C++ protects the programmer from implementation changes in other modules of an application Using try() blocks to trap exceptions Using catch() blocks to handle exceptions Defining exceptions and using throw to trigger them Introduction to C++ Programming / C++ Essentials is a skills-focused, hands-on C++ training course geared for experienced programmers who need to learn C++ coupled with sounds coding skills and best practices for OO development. Students will leave this course armed with the required skills to put foundation-level C++ programming skills right to work in a practical environment. The central concepts of C++ syntax and style are taught in the context of using object-oriented methods to achieve reusability, adaptability and reliability. Emphasis is placed on the features of C++ that support abstract data types, inheritance, and polymorphism. Students will learn to apply the process of data abstraction and class design. Practical aspects of C++ programming including efficiency, performance, testing, and reliability considerations are stressed throughout. Comprehensive hands on exercises are integrated throughout to reinforce learning and develop real competency Moving from C to C++ (Optional) New Compiler Directives Stream Console I/O Explicit Operators Standard Libraries Data Control Capabilities Handling Data New Declaration Features Initialization and Assignment Enumerated Types The bool Type Constant Storage Pointers to Constant Storage Constant Pointers References Constant Reference Arguments Volatile Data Global Data Functions Function Prototypes and Type Checking Default Function Data Types Function Overloading Problems with Function Overloading Name Resolution Promotions and Conversions Call by Value Reference Declarations Call-by-Reference and Reference Types References in Function Return Constant Argument Types Conversion of Parameters Using Default Initializers Providing Default Arguments Inline Functions Operator Overloading Advantages and Pitfalls of Overloading Member Operator Syntax and Examples Class Assignment Operators Class Equality Operators Non-Member Operator Overloading Member and Non-Member Operator Functions Operator Precedence This Pointer Overloading the Assignment Operator Overloading Caveats Creating and Using Objects Creating Automatic Objects Creating Dynamic Objects Calling Object Methods Constructors Initializing Member consts Initializer List Syntax Allocating Resources in Constructor Destructors Block and Function Scope File and Global Scope Class Scope Scope Resolution Operator :: Using Objects as Arguments Objects as Function Return Values Constant Methods Containment Relationships Dynamic Memory Management Advantages of Dynamic Memory Allocation Static, Automatic, and Heap Memory Free Store Allocation with new and delete Handling Memory Allocation Errors Controlling Object Creation Object Copying and Copy Constructor Automatic Copy Constructor Conversion Constructor Streaming I/O Streams and the iostream Library Built-in Stream Objects Stream Manipulators Stream Methods Input/Output Operators Character Input String Streams Formatted I/O File Stream I/O Overloading Stream Operators Persistent Objects Introduction to Object Concepts The Object Programming Paradigm Object-Orientated Programming Definitions Information Hiding and Encapsulation Separating Interface and Implementation Classes and Instances of Objects Overloaded Objects and Polymorphism Declaring and Defining Classes Components of a Class Class Structure Class Declaration Syntax Member Data Built-in Operations Constructors and Initialization Initialization vs. Assignment Class Type Members Member Functions and Member Accessibility Inline Member Functions Friend Functions Static Members Modifying Access with a Friend Class Templates Purpose of Template Classes Constants in Templates Templates and Inheritance Container Classes Use of Libraries Strings in C++ Character Strings The String Class Operators on Strings Member Functions of the String Class Inheritance Inheritance and Reuse Composition vs. Inheritance Inheritance: Centralized Code Inheritance: Maintenance and Revision Public, Private and Protected Members Redefining Behavior in Derived Classes Designing Extensible Software Systems Syntax for Public Inheritance Use of Common Pointers Constructors and Initialization Inherited Copy Constructors Destructors and Inheritance Public, Protected, Private Inheritance Exceptions Types of Exceptions Trapping and Handling Exceptions Triggering Exceptions Handling Memory Allocation Errors C++ Program Structure Organizing C++ Source Files Integrating C and C++ Projects Using C in C++ Reliability Considerations in C++ Projects Function Prototypes Strong Type Checking Constant Types C++ Access Control Techniques Polymorphism in C++ Definition of Polymorphism Calling Overridden Methods Upcasting Accessing Overridden Methods Virtual Methods and Dynamic Binding Virtual Destructors Abstract Base Classes and Pure Virtual Methods Multiple Inheritance Derivation from Multiple Base Classes Base Class Ambiguities Virtual Inheritance Virtual Base Classes Virtual Base Class Information The Standard Template Library STL Containers Parameters Used in Container Classes The Vector Class STL Algorithms Use of Libraries

Duration 3 Days 18 CPD hours This course is intended for This is an intermediate course for architects, system integrators, security administrators, network administrators, software engineers, technical support individuals, and IBM Business Partners who implement LPARs on IBM Power Systems. Overview Describe important concepts associated with managing POWER7 processor-based systems, such as Logical partitioning (LPAR), dynamic partitioning, virtual devices, virtual processors, virtual consoles, virtual Local Area Network (VLAN), and shared processors Describe the features of the PowerVM Editions. Use the System Planning Tool to plan an LPAR configuration Describe the functions of the HMC Configure and manage the HMC, including users and permissions, software, startup and shutdown, remote access features, network configuration, security features, HMC backup and restore options, and the HMC reload procedure Describe the rules associated with allocating resources, including dedicated processors, processing units for Micro-Partitions, memory, physical I/O for AIX and Linux partitions Configure and manage LPARs using the HMC Graphical User Interface (GUI) and HMC commands Interpret physical and AIX location codes and relate to the key hardware components Power on and power off the POWER7 system Use the HMC to back up and restore partition data In this course, students will learn the skills needed to become an effective administrator on IBM's POWER7-based systems that support Logical Partitioning (LPAR). Day 1 Introduction to partitioning Hardware system overview Hardware Management Console Day 2 Hardware Management Console (cont.) System Planning Tool HMC and managed system maintenance System power management Planning and configuring logical partitions Day 3 Planning and configuring logical partitions (cont.) Partition operations Dynamic resource allocation Exercise 9 Additional course details: Nexus Humans AN110 IBM Power Systems for AIX I - LPAR Configuration and Planning 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 AN110 IBM Power Systems for AIX I - LPAR Configuration and Planning 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 IS Security Officers IS Managers Risk Managers Auditors Information Systems Owners IS Control Assessors System Managers Government Employees Overview Upon completion, Certified Threat Intelligence Analyst students will be able to proactively collect threat data and implement strategies to limit exposure to those threats. Additionally, they will be prepared to take the C)TIA exam Everywhere you turn today, you hear about the need for threat intelligence analysis! However, in some cases, it is just a buzzword, while in other cases, threat intelligence is being touted as the remedy toward advanced persistent threats. The real question is, how do we leverage threat intelligence to reduce network vulnerabilities without wasting time and money? The answer is simple, Mile2?s Certified Threat Intelligence Analyst course. Mile2?s CTIA course will help security professionals learn how to make good use of the many sources of threat intelligence. It will aid an individual to understand what threat sources are helpful, which specific threats are targeted and which ones may need minor adjustments to monitor within your organization. Mile2?s CTIA course focuses heavily on hands-on labs, concentrating on discerning and interpreting threats and responding to them.ÿ The CTIA course focuses overall on current significant threats, threat actors, and identification procedures so that cyber-security professionals can implement the best policies and procures for their organizational security posture. Once complete, the student will be competent toward improving a company?s existing security infrastructure. Policies and methodologies learned in the CTIA will allow the student to use threat intelligence concepts to decrease overall company risk. Course Outline Threat Intelligence Basics Cyber Threats Threat Actors Case Studies Threat Identification Proactive Approach

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

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.