983 Programming Languages courses

Welcome to the data manipulation in Python course. Our goal in this course is to provide you with all the tools and skills necessary to master Python, NumPy, and Pandas for data science. No previous skills or expertise are required. Only a drive to succeed!

Welcome to this dual-phase course. In the first segment, we delve into neural networks and deep learning. In the second, ascend to mastering Generative Adversarial Networks (GANs). No programming experience required. Begin with the fundamentals and progress to an advanced level.

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

Overview This comprehensive course on R Programming for Data Science will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This R Programming for Data Science comes with accredited certification from CPD, 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 R Programming for Data Science. It is available to all students, of all academic backgrounds. Requirements Our R Programming for Data Science 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 23 sections • 129 lectures • 06:25:00 total length •Introduction to Data Science: 00:01:00 •Data Science: Career of the Future: 00:04:00 •What is Data Science?: 00:02:00 •Data Science as a Process: 00:02:00 •Data Science Toolbox: 00:03:00 •Data Science Process Explained: 00:05:00 •What's Next?: 00:01:00 •Engine and coding environment: 00:03:00 •Installing R and RStudio: 00:04:00 •RStudio: A quick tour: 00:04:00 •Arithmetic with R: 00:03:00 •Variable assignment: 00:04:00 •Basic data types in R: 00:03:00 •Creating a vector: 00:05:00 •Naming a vector: 00:04:00 •Vector selection: 00:06:00 •Selection by comparison: 00:04:00 •What's a Matrix?: 00:02:00 •Analyzing Matrices: 00:03:00 •Naming a Matrix: 00:05:00 •Adding columns and rows to a matrix: 00:06:00 •Selection of matrix elements: 00:03:00 •Arithmetic with matrices: 00:07:00 •Additional Materials: 00:00:00 •What's a Factor?: 00:02:00 •Categorical Variables and Factor Levels: 00:04:00 •Summarizing a Factor: 00:01:00 •Ordered Factors: 00:05:00 •What's a Data Frame?: 00:03:00 •Creating Data Frames: 00:20:00 •Selection of Data Frame elements: 00:03:00 •Conditional selection: 00:03:00 •Sorting a Data Frame: 00:03:00 •Additional Materials: 00:00:00 •Why would you need lists?: 00:01:00 •Creating a List: 00:06:00 •Selecting elements from a list: 00:03:00 •Adding more data to the list: 00:02:00 •Additional Materials: 00:00:00 •Equality: 00:03:00 •Greater and Less Than: 00:03:00 •Compare Vectors: 00:03:00 •Compare Matrices: 00:02:00 •Additional Materials: 00:00:00 •AND, OR, NOT Operators: 00:04:00 •Logical operators with vectors and matrices: 00:04:00 •Reverse the result: (!): 00:01:00 •Relational and Logical Operators together: 00:06:00 •Additional Materials: 00:00:00 •The IF statement: 00:04:00 •IFELSE: 00:03:00 •The ELSEIF statement: 00:05:00 •Full Exercise: 00:03:00 •Additional Materials: 00:00:00 •Write a While loop: 00:04:00 •Looping with more conditions: 00:04:00 •Break: stop the While Loop: 00:04:00 •What's a For loop?: 00:02:00 •Loop over a vector: 00:02:00 •Loop over a list: 00:03:00 •Loop over a matrix: 00:04:00 •For loop with conditionals: 00:01:00 •Using Next and Break with For loop: 00:03:00 •Additional Materials: 00:00:00 •What is a Function?: 00:02:00 •Arguments matching: 00:03:00 •Required and Optional Arguments: 00:03:00 •Nested functions: 00:02:00 •Writing own functions: 00:03:00 •Functions with no arguments: 00:02:00 •Defining default arguments in functions: 00:04:00 •Function scoping: 00:02:00 •Control flow in functions: 00:03:00 •Additional Materials: 00:00:00 •Installing R Packages: 00:01:00 •Loading R Packages: 00:04:00 •Different ways to load a package: 00:02:00 •Additional Materials: 00:00:00 •What is lapply and when is used?: 00:04:00 •Use lapply with user-defined functions: 00:03:00 •lapply and anonymous functions: 00:01:00 •Use lapply with additional arguments: 00:04:00 •Additional Materials: 00:00:00 •What is sapply?: 00:02:00 •How to use sapply: 00:02:00 •sapply with your own function: 00:02:00 •sapply with a function returning a vector: 00:02:00 •When can't sapply simplify?: 00:02:00 •What is vapply and why is it used?: 00:04:00 •Additional Materials: 00:00:00 •Mathematical functions: 00:05:00 •Data Utilities: 00:08:00 •Additional Materials: 00:00:00 •grepl & grep: 00:04:00 •Metacharacters: 00:05:00 •sub & gsub: 00:02:00 •More metacharacters: 00:04:00 •Additional Materials: 00:00:00 •Today and Now: 00:02:00 •Create and format dates: 00:06:00 •Create and format times: 00:03:00 •Calculations with Dates: 00:03:00 •Calculations with Times: 00:07:00 •Additional Materials: 00:00:00 •Get and set current directory: 00:04:00 •Get data from the web: 00:04:00 •Loading flat files: 00:03:00 •Loading Excel files: 00:05:00 •Additional Materials: 00:00:00 •Base plotting system: 00:03:00 •Base plots: Histograms: 00:03:00 •Base plots: Scatterplots: 00:05:00 •Base plots: Regression Line: 00:03:00 •Base plots: Boxplot: 00:03:00 •Introduction to dplyr package: 00:04:00 •Using the pipe operator (%>%): 00:02:00 •Columns component: select(): 00:05:00 •Columns component: rename() and rename_with(): 00:02:00 •Columns component: mutate(): 00:02:00 •Columns component: relocate(): 00:02:00 •Rows component: filter(): 00:01:00 •Rows component: slice(): 00:04:00 •Rows component: arrange(): 00:01:00 •Rows component: rowwise(): 00:02:00 •Grouping of rows: summarise(): 00:03:00 •Grouping of rows: across(): 00:02:00 •COVID-19 Analysis Task: 00:08:00 •Additional Materials: 00:00:00 •Assignment - R Programming for Data Science: 00:00:00

Overview This comprehensive course on Bash Scripting, Linux and Shell Programming will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Bash Scripting, Linux and Shell Programming comes with accredited certification from CPD, 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 Bash Scripting, Linux and Shell Programming. It is available to all students, of all academic backgrounds. Requirements Our Bash Scripting, Linux and Shell Programming 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 11 sections • 61 lectures • 03:03:00 total length •Introduction: 00:02:00 •Bash vs Shell vs Command Line vs Terminal: 00:06:00 •Listing Folder Contents (ls): 00:05:00 •Print Current Folder (pwd): 00:01:00 •Change Folder (cd): 00:03:00 •Using A Stack To Push Folders (pushd/popd): 00:03:00 •Check File Type (file): 00:01:00 •Find File By Name (locate) & Update Locate Database (updatedb): 00:02:00 •Find A Command (which): 00:02:00 •Show Command History (history): 00:02:00 •Show Manual Descriptions (whatis): 00:01:00 •Search Manual (apropos): 00:02:00 •Reference Manuals (man): 00:02:00 •Creating A Folder (mkdir): 00:02:00 •Creating A File (touch): 00:02:00 •Copy Files/Folders (cp): 00:02:00 •Move & Rename Files/Folders (mv): 00:02:00 •Delete Files/Folders (rm): 00:02:00 •Delete Empty Folder (rmdir): 00:02:00 •Change File Permissions (chmod): 00:06:00 •File Concatenation (cat): 00:03:00 •File Perusal Filter (more/less): 00:02:00 •Terminal Based Text Editor (nano): 00:03:00 •Run Commands As A Superuser (sudo): 00:03:00 •Change User (su): 00:03:00 •Show Effecter User and Group IDs (id): 00:02:00 •Kill A Running Command (ctrl + c): 00:02:00 •Kill All Processes By A Name (killall): 00:02:00 •Logging Out Of Bash (exit): 00:01:00 •Tell Bash That There Is No More Input (ctrl + d): 00:02:00 •Clear The Screen (ctr + l): 00:02:00 •Zoom In (ctrl + +): 00:02:00 •Zoom Out (ctrl + -): 00:02:00 •Moving The Cursor: 00:02:00 •Deleting Text: 00:04:00 •Fixing Typos: 00:03:00 •Cutting and Pasting: 00:03:00 •Character Capitalisation: 00:03:00 •Bash File Structure: 00:03:00 •Echo Command: 00:04:00 •Comments: 00:04:00 •Variables: 00:06:00 •Strings: 00:06:00 •While Loop: 00:04:00 •For Loop: 00:04:00 •Until Loop: 00:03:00 •Break & Continue: 00:03:00 •Get User Input: 00:02:00 •If Statement: 00:09:00 •Case Statements: 00:06:00 •Get Arguments From The Command Line: 00:04:00 •Functions: 00:05:00 •Global vs Local Variables: 00:03:00 •Arrays: 00:06:00 •Shell & Environment Variables: 00:06:00 •Scheduled Automation: 00:03:00 •Aliases: 00:03:00 •Wildcards: 00:03:00 •Multiple Commands: 00:02:00 •Resource: 00:00:00 •Assignment - Bash Scripting, Linux and Shell Programming@@: 00:00:00

If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

This extensive course for beginners provides the basics of chatbots with machine learning, deep learning, AWS, and its applications, building it from scratch with hands-on practice for chatbot development. This course will help you learn basic to advanced mechanisms of developing chatbots using machine learning, deep learning, and AWS with Python.

Duration 2 Days 12 CPD hours This course is intended for This introduction to Spring development course requires that incoming students possess solid Java programming skills and practical hands-on Java experience. This class is geared for experienced Java developers who are new to Spring, who wish to understand how and when to use Spring in Java and JEE applications. Overview Working in a hands-on learning environment, led by our expert practitioner, students will: Explain the issues associated with complex frameworks such as JEE and how Spring addresses those issues Understand the relationships between Spring and JEE, AOP, IOC and JDBC. Write applications that take advantage of the Spring container and the declarative nature of assembling simple components into applications. Understand how to configure the Spring Boot framework Understand and work on integrating persistence into a Spring application Explain Spring's support for transactions and caching Work with Spring Boot to facilitate Spring setup and configuration Apply Aspect Oriented Programming (AOP) to Spring applications Become familiar with the conditionally loading of bean definitions and Application Contexts Understand how to leverage the power of Spring Boot Use Spring Boot to create and work with JPA repositories Introduction to Spring Boot | Spring Boot Quick Start is a hands-on Spring training course geared for experienced Java developers who need to understand what the Spring Boot is in terms of today's systems and architectures, and how to use Spring in conjunction with other technologies and frameworks. This leading-edge course provides added coverage of Spring's Aspect-Oriented Programming and the use of Spring Boot. Students will gain hands-on experience working with Spring, using Maven for project and dependancy management, and, optionally, a test-driven approach (using JUnit) to the labs in the course. The Spring framework is an application framework that provides a lightweight container that supports the creation of simple-to-complex components in a non-invasive fashion. Spring's flexibility and transparency is congruent and supportive of incremental development and testing. The framework's structure supports the layering of functionality such as persistence, transactions, view-oriented frameworks, and enterprise systems and capabilities. This course targets Spring Boot 2 , which includes full support for Java SE 11 and Java EE 8. Spring supports the use of lambda expressions and method references in many of its APIs. The Spring Framework Understand the value of Spring Explore Dependency Injection (DI) and Inversion of Control (IoC) Introduce different ways of configuring collaborators Spring as an Object Factory Initializing the Spring IoC Container Configuring Spring Managed Beans Introduce Java-based configuration The @Configuration and @Bean annotations Define bean dependencies Bootstrapping Java Config Context Injection in Configuration classes Using context Profiles Conditionally loading beans and configurations Bean Life-Cycle Methods Defining Bean dependencies Introduce Spring annotations for defining dependencies Explore the @Autowired annotation Stereotype Annotations Qualifying injection points Lifecycle annotations Using properties in Java based configuration The @Value annotation Using the Candidate Components Index Introduction to Spring Boot Introduce the basics of Spring Boot Explain auto-configuration Introduce the Spring Initializr application Bootstrapping a Spring Boot application Working with Spring Boot Provide an overview of Spring Boot Introduce starter dependencies Introduce auto-configuration @Enable... annotations Conditional configuration Spring Boot Externalized Configuration Bootstrapping Spring Boot Introduction to Aspect Oriented Programming Aspect Oriented Programming Cross Cutting Concerns Spring AOP Spring AOP in a Nutshell @AspectJ support Spring AOP advice types AspectJ pointcut designators Spring Boot Actuator Understand Spring Boot Actuators Work with predefined Actuator endpoints Enabling Actuator endpoints Securing the Actuator Developing in Spring Boot Introduce Spring Boot Devtools Enable the ConditionEvaluationReport Debugging Spring Boot applications Thymeleaf Provide a quick overview of Thymeleaf Introduce Thymeleaf templates Create and run a Spring Thymeleaf MVC application Additional course details: Nexus Humans Spring Boot Quick Start | Core Spring, Spring AOP, Spring Boot 2.0 and More (TT3322) 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 Spring Boot Quick Start | Core Spring, Spring AOP, Spring Boot 2.0 and More (TT3322) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Learn how to implement EC2 and VPC resources on AWS using the Python API: Boto3! Implement your infrastructure with code!