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This course will show you why Hadoop is one of the best tools to work with big data. With the help of some real-world data sets, you will learn how to use Hadoop and its distributed technologies, such as Spark, Flink, Pig, and Flume, to store, analyze, and scale big data.

If you know various DevOps tools such as Git, Jenkins, Maven, Ansible, Docker, and Kubernetes, and are not sure how they work collaboratively, then this course is for you. Learn to create a simple DevOps project using Git as the local version control system, GitHub as the distributor version control system, Jenkins as the continuous integration tool, Maven as a build tool, Ansible as configuration management and deployment tool, Docker for containerization, Kubernetes as a container management tool, and all this environment is set up on AWS.

Duration 2 Days 12 CPD hours This course is intended for This in an introductory-level course geared for QA, Test team members and others who want to use the Python testing framework PyTest to implement code testing strategies. Attendees should have prior basic Python scripting experience. Students should have some familiarity with tools to be used in this course: PyCharm, Jupyter Notebook and basic GIT. Overview Working within in a hands-on learning environment students will learn to: Become proficient with pytest from day one by solving real-world testing problems Use pytest to write tests more efficiently Scale from simple to complex and functional testing Write and run simple and complex tests Organize tests in fles and directories Find out how to be more productive on the command line Markers and how to skip, xfail and parametrize tests Explore fxtures and techniques to use them effectively, such as tmpdir, pytestconfg, and monkeypatch Convert unittest suites to pytest using little-known techniques The pytest framework is simple to use but powerful enough to cover complex testing integration scenarios. PyTest is considered by many to be the true Pythonic approach to testing in Python. Geared for QA, Test team members and others who want to use the Python testing framework PyTest to implement code testing strategies, Test Automation with Python is a hands-on, two day Python testing course that provides students with the skills required to get started with PyTest right away. Participnats will learn how to get the most out of it in their daily workflow, exploring powerful mechanisms and plugins to facilitate many common testing tasks. Students will also learn how to use pytest in existing unittestbased test suites and will learn some tricks to make the jump to a pytest-style test suite quickly and easily. Python Refresher Python Overview Python Basics Python Lab Introducing PyTest Why Spend time writing test UnitTest Module Why PyTest? Introductory Lab Writing and Running Test Installing PyTest Writing and Running Tests Organizing files and packages Command Line options Configure pytest.ini Install and Config Lab Markers and Parameters Mark Basics Built-in marks Parameterization Markers and Parameters Lab Fixtures Introduction to Fixtures Sharing fixtures with conftest.py files Scopes Autouse Parameterizing fixtures Using marks from fixtures Built-in fixtures Best Practices Fixtures Lab Fixtures Lab 2 Plugins Finding and installing plugins Overview of plugins Plugin Lab From UnitTest to PyTest Use PyTest as a Test Runner Convert asserts with unitest2pytest Handling setup/teardown Managing test hierarchies Refactoring test utilities Migration strategies Additional course details: Nexus Humans Test Automation with Python (TTPS4832) 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 Test Automation with Python (TTPS4832) 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.

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Course Overview It is important that all children are given equal opportunities to reach their full potential, and it all starts with education, which is why the role of a Teaching assistant is so rewarding. Teaching Assistants help support the learning and development of children and young people, including those with special educational needs such as autism, ADHD and dyslexia. In this Complete Teaching Assistant Diploma, you will gain a wide range of skills and knowledge to provide quality learning support to children. Taking an in-depth look at the classroom environment and the role of the Teaching Assistant, it covers safeguarding duties, classroom management, the SEN code of practice and a variety of teaching methodologies. Not only that, but you will gain excellent time management, communication and organisation skills, which can be applied to any teaching role and learning environment. By the end of the course, you will also have an excellent understanding of the national curriculum and teaching codes of practice in the UK. Enrol in this Teaching Assistant Complete Course today and accelerate your career online! What You Will Learn This Complete Teaching Assistant Diploma is suitable for anyone looking for a pathway to a career in teaching support. It includes essential information on teaching practices and methodologies, as well as SEN learning support strategies for pupils with autism, dyslexia and ADHD. Understanding the Role of the Teaching Assistant Skills & Career Requirements for Teaching Support Roles The Development of Children and Young People Building Professional Relationships With Pupils & Family Safeguarding the Welfare of Children & Young People An Overview of Teaching Methodologies An Introduction to SEN Teaching An Overview of Learning Difficulties in Children Classroom Management Strategies for SEN Career Path, Salary and Opportunities Why You Should Choose This Course From iStudy Study at your own pace Full Tutor support on weekdays (Monday - Friday) Fully compatible with any device Free Printable PDF Certificate immediately after completion No prior qualifications are needed to take this course No hidden fees or exam charges CPD Qualification Standards and IAO accredited Efficient exam system, assessment, and instant results Our customer support team is always ready to help you Gain professional skills and better earning potential Enrol today and learn something new with iStudy. You'll find a full breakdown of the course curriculum down below, take a look and see just how much this course offers. We're sure you'll be satisfied with this course.

Duration 5 Days 30 CPD hours This course is intended for This course is intended for administrators in a Microsoft-centric environment who want to build reusable units of automation, automate business processes, and enable less-technical colleagues to accomplish administrative tasks. Overview Describe the correct patterns for building modularized tools in Windows PowerShell Build highly modularized functions that comply with native PowerShell patterns Build controller scripts that expose user interfaces and automate business processes Manage data in a variety of formats Write automated tests for tools Debug tools This course covers advanced Windows PowerShell topics, with an emphasis on building reusable tools. Students are introduced to workflow, engage in best practices, and learn a variety of script development and toolmaking techniques. Tool Design Tools do one thing Tools are flexible Tools look native Start with a Command Why start with a command? Discovery and experimentation Build a Basic Function and Module Start with a basic function Create a script module Check prerequisites Run the new command Adding CmdletBinding and Parameterizing About CmdletBinding and common parameters Accepting pipeline input Mandatory-ness Parameter validation Parmeter aliases Emitting Objects as Output Assembling information Constructing and emitting output Quick tests An Interlude: Changing Your Approach Examining a script Critiquing a script Revising the script Using Verbose, Warning, and Informational Output Knowing the six channels Adding verbose and warning output Doing more with verbose output Informational output Comment-Based Help Where to put your help Getting started Going further with comment-based help Broken help Handling Errors Understanding errors and exceptions Bad handling Two reasons for exception handling Handling exceptions in our tool Capturing the actual exception Handling exceptions for non-commands Going further with exception handling Deprecated exception handling Basic Debugging Two kinds of bugs The ultimate goal of debugging Developing assumptions Write-Debug Set-PSBreakpoint The PowerShell ISE Going Deeper with Parameters Parameter positions Validation Multiple parameter sets Value from remaining arguments Help messages Aliases More CmdletBinding Writing Full Help External help Using PlatyPs Supporting online help ?About? topics Making your help updatable Unit Testing Your Code Sketching out the test Making something to test Expanding the test Going further with Pester Extending Output Types Understanding types The Extensible Type System Extending an object Using Update-TypeData Analyzing Your Script Performing a basic analysis Analyzing the analysis Publishing Your Tools Begin with a manifest Publishing to PowerShell Gallery Publishing to private repositories Basic Controllers: Automation Scripts and Menus Building a menu Using UIChoice Writing a process controller Proxy Functions A proxy example Creating the proxy base Modifying the proxy Adding or removing parameters Working with XML Data Simple: CliXML Importing native XML ConvertTo-XML Creating native XML from scratch Working with JSON Data Converting to JSON Converting from JSON Working with SQL Server Data SQL Server terminology and facts Connecting to the server and database Writing a query Running a query Invoke-SqlCmd Thinking about tool design patterns Design tools that use SQL Server for data storage Final Exam Lab problem Break down the problem Do the design Test the commands Code the tool

Learn and master HTML, CSS, Bootstrap, and SASS. Starts right from the basics and increases its level step by step by understanding various concepts, implementing them with different exercises, practicing with games, and building 6 real-world projects.

Build a strong foundation in intermediate Android development to leverage what you already know about Android. If you are looking for a course that takes your pre-existing Android knowledge to the next level, then this is definitely the course for you. We'll take you through all you need to know in order to become an intermediate to advanced Android developer.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

Overview This comprehensive course on Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Machine Learning with Python comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 4 sections • 21 lectures • 01:34:00 total length •Introduction to types of ML algorithm: 00:02:00 •SVM - Python Implementation: 00:06:00 •Introduction to types of ML algorithm: 00:02:00 •Importing a dataset in python: 00:02:00 •Resolving Missing Values: 00:06:00 •Managing Category Variables: 00:04:00 •Training and Testing Datasets: 00:07:00 •Normalizing Variables: 00:02:00 •Normalizing Variables - Python Code: 00:03:00 •Summary: 00:01:00 •Simple Linear Regression - How it works?: 00:04:00 •Simple Linear Regreesion - Python Implementation: 00:07:00 •Multiple Linear Regression - How it works?: 00:01:00 •Multiple Linear Regression - Python Implementation: 00:09:00 •Decision Trees - How it works?: 00:05:00 •Random Forest - How it works?: 00:03:00 •Decision Trees and Random Forest - Python Implementation: 00:04:00 •kNN - How it works?: 00:02:00 •kNN - Python Implementation: 00:10:00 •Decision Tree Classifier and Random Forest Classifier in Python: 00:10:00 •SVM - How it works?: 00:04:00