3044 Software Development courses

Duration 3 Days 18 CPD hours This course is intended for Developers & Developer Consultants Overview To discuss the role of the ABAP Dictionary in the SAP system and its scope of functions. This course discusses the role of the ABAP Dictionary in the SAP system and its scope of functions. Introduction to the ABAP Dictionary Describing the ABAP Dictionary Data Types in the ABAP Dictionary Creating Domains and Data Elements Creating Flat Structures Creating Table Types and Deep Structures Creating Type Groups Database Tables Creating Transparent Tables Defining Cluster Tables and Pooled Tables Performance During Table Access Creating Database Table Indexes Setting Up Table Buffering Input Checks Creating Fixed Values Defining Foreign Keys to Perform Input Checks Creating Text Tables Dictionary Object Dependencies Differentiating Between Active and Inactive Dictionary Objects Identifying Dependencies with ABAP Dictionary Objects Table Changes Performing a Table Conversion Enhancing Tables Using Append Structures Views and Maintenance Views Creating Database Views Creating Maintenance Views Creating View Clusters Search Helps Creating Search Helps Applying Advanced Search Help Techniques

Information on the risks and practical advice to address them TSC's eBooks, whitepapers, and reports cover some of the most important risks in information and cyber security — risks that constantly challenge information and cyber security professionals who work tirelessly to reduce them across their organisations and home users alike.

Information on the risks and practical advice to address them TSC's eBooks, whitepapers, and reports cover some of the most important risks in information and cyber security — risks that constantly challenge information and cyber security professionals who work tirelessly to reduce them across their organisations and home users alike.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for technical professionals who require the skills to administer IBM MQ. Overview After completing this course, you should be able to: Describe the IBM MQ deployment options Create and manage queue managers, queues, and channels Use the IBM MQ sample programs and utilities to test the IBM MQ network Configure distributed queuing Configure MQ client connections to a queue manager Define and administer a queue manager cluster Administer Java Message Service (JMS) in MQ Implement basic queue manager restart and recovery procedures Use IBM MQ troubleshooting tools to identify the cause of a problem in the IBM MQ network Manage IBM MQ security Monitor the activities and performance of an IBM MQ system This course is also available as self-paced virtual (e-learning) course IBM MQ V9.1 System Administration (ZM156G). This option does not require any travel.This course teaches you how to customize, operate, administer, and monitor IBM MQ on-premises on distributed operating systems. The course covers configuration, day-to-day administration, problem recovery, security management, and performance monitoring. In addition to the instructor-led lectures, the hands-on exercises provide practical experience with distributed queuing, working with MQ clients, and implementing clusters, publish/subscribe messaging. You also learn how to implement authorization, authentication, and encryption, and you learn how to monitor performance. Introducing IBM MQ Exercise Getting started with IBM MQ Working with IBM MQ administration tools Exercise Working with IBM MQ administration tools Configuring distributed queuing Exercise Implementing distributed queuing Managing clients and client connections Exercise Connecting an IBM MQ client Advanced IBM MQ client features Working with queue manager clusters Exercise Implementing a basic cluster Publish/subscribe messaging Exercise Configuring publish/subscribe message queuing Implementing basic security in IBM MQ Exercise Controlling access to IBM MQ Securing IBM MQ channels with TLS Exercise Securing channels with TLS Authenticating channels and connections Exercise Implementing connection authentication Supporting JMS with IBM MQ Diagnosing problems Running an IBM MQ trace Backing up and restoring IBM MQ messages and object definitions Using a media image to restore a queue Backing up and restoring IBM MQ object definitions High availability Monitoring and configuring IBM MQ for performance Monitoring IBM MQ for performance Monitoring resources with the IBM MQ Console Additional course details: Nexus Humans WM156G IBM MQ V9.1 System Administration (using Windows for labs) 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 WM156G IBM MQ V9.1 System Administration (using Windows for labs) 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 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

Duration 4 Days 24 CPD hours This course is intended for This course is appropriate for anyone who wants to create applications or modules to automate and simplify common tasks with Perl. Overview Working within in an engaging, hands-on learning environment, guided by our expert web development, PHP practitioner, students will learn to: Create a working script that gets input from the command line, the keyboard, or a file Use arrays to store and process data from files Create formatted reports Use regular expressions Use the appropriate types of variables and data structures Refactor duplicate code into subroutines and modules What is available in the standard library Use shortcuts and defaults, and what they replace Introduction to Perl Programming Essentials is an Introductory-level practical, hands-on Perl scripting training course that guides the students from the basics of writing and running Perl scripts to using more advanced features such as file operations, report writing, the use of regular expressions, working with binary data files, and using the extensive functionality of the standard Perl library. Students will immediately be able to use Perl to complete tasks in the real world. Session: An Overview of Perl What is Perl? Perl is compiled and interpreted Perl Advantages and Disadvantages Downloading and Installing Perl Which version of Perl Getting Help Session: Creating and running Perl Programs Structure of a Perl program Running a Perl script Checking syntax and warnings Execution of scripts under Unix and Windows Session: Basic Data and I/O Numeric and Text literals Math operators and expressions Scalar variables Default values Writing to standard output Command line arguments Reading from the standard input Session: Logic and Loops About flow control The if statement and Boolean values Using unless and elsif Statement modifiers warn() and die() The conditional construct Using while loop and its variants Using the for loop Exiting from loops Session: Lists and Arrays The list data type Accessing array elements Creating arrays List interpolation Arrays and memory Counting elements Iterating through an array List evaluation Slices and ranges Session: Reading and writing text files File I/O Overview Opening a file Reading text files Writing to a text file Arrays and file I/O Using the <> operator Session: List functions Growing and shrinking arrays The split() function Splitting on whitespace Assigning to literal lists The join() function The sort() function Alternate sort keys Reversing an array Session: Formatting output Using sprintf() and printf() Report formatting overview Defining report formats The write() function Advanced filehandle magic Session: Hashes Hash overview Creating hashes Hash attributes Traversing a hash Testing for existence of elements Deleting hash elements Session: References What is a reference? The two ways to create references References to existing data References to anonymous data Dereferencing scalar, array, and ash references Dereferencing elements of arrays and hashes Multidimensional arrays and other data structures Session: Text and Regular Expressions String length The substr() function The index() and rindex() functions String replication Pattern matching and substitution Regular expressions Session: Raw file and data access Opening and closing raw (binary) files Reading raw data Using seek() and tell() Writing raw data Raw data manipulation with pack() and unpack() Session: Subroutines and variable scope Understanding packages Package and Lexical variables Localizing builtin variables Declaring and calling subroutines Calling subroutines Passing parameters and returning values Session: Working with the operating system Determining current OS Environment variables Running external programs User identification Trapping signals File test operators Working with files Time of day Session: Shortcuts and defaults Understanding $_ shift() with no array specified Text file processing Using grep() and Using map() Command-line options for file processing Session: Data wrangling Quoting in Perl Evaluating arrays Understanding qw( ) Getting more out of the <> operator Read ranges of lines Using m//g in scalar context The /o modifier Working with embedded newlines Making REs more readable Perl data conversion Session: Using the Perl Library The Perl library Old-style library files Perl modules Modules bundled with Perl A selection of modules Getting modules from ActiveState Getting modules from CPAN Using Getopt::Long Session: Some Useful Tools Sending and receiving files with Net::FTP Using File::Find to search for files and directories Grabbing a Web page Some good places to find scripts Perl man pages for more information Zipping and unzipping files

Duration 3 Days 18 CPD hours This course is intended for The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services. Overview You will be able to Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description Explain the Benefits of Artificial Intelligence (AI) Describe how we Learn from Data - Functionality, Software and Hardware Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together Describe a ''Learning from Experience'' Agile Approach to Projects Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI) Ethical and Sustainable Human and Artificial Intelligence (AI) Recall the General Definition of Human and Artificial Intelligence (AI) Describe what are Ethics and Trustworthy Artificial Intelligence (AI) Describe the Three Fundamental Areas of Sustainability and the United Nationïs Seventeen Sustainability Goals Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI) Artificial Intelligence (AI) and Robotics Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description Describe what a Robot is Describe what an intelligent Robot is Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations Explain the Benefits of Artifical Intelligence (AI) Describe the Challenges of Artificial Intelligence (AI) Projects Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects List Opportunities for Artificial Intelligence (AI) Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs) Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice Describe how we Learn from Data - Functionality, Software and Hardware Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsïFunctionality The Management, Roles and Responsibilities of Humans and Machines Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together List Future Directions of Humans and Machines Working Together Describe a ''Learning from Experience'' Agile Approach to Projects

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Overview Skills gained in this training include:The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysisThe fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with HadoopHow Pig, Hive, and Impala improve productivity for typical analysis tasksJoining diverse datasets to gain valuable business insightPerforming real-time, complex queries on datasets Cloudera University?s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. Hadoop Fundamentals The Motivation for Hadoop Hadoop Overview Data Storage: HDFS Distributed Data Processing: YARN, MapReduce, and Spark Data Processing and Analysis: Pig, Hive, and Impala Data Integration: Sqoop Other Hadoop Data Tools Exercise Scenarios Explanation Introduction to Pig What Is Pig? Pig?s Features Pig Use Cases Interacting with Pig Basic Data Analysis with Pig Pig Latin Syntax Loading Data Simple Data Types Field Definitions Data Output Viewing the Schema Filtering and Sorting Data Commonly-Used Functions Processing Complex Data with Pig Storage Formats Complex/Nested Data Types Grouping Built-In Functions for Complex Data Iterating Grouped Data Multi-Dataset Operations with Pig Techniques for Combining Data Sets Joining Data Sets in Pig Set Operations Splitting Data Sets Pig Troubleshoot & Optimization Troubleshooting Pig Logging Using Hadoop?s Web UI Data Sampling and Debugging Performance Overview Understanding the Execution Plan Tips for Improving the Performance of Your Pig Jobs Introduction to Hive & Impala What Is Hive? What Is Impala? Schema and Data Storage Comparing Hive to Traditional Databases Hive Use Cases Querying with Hive & Impala Databases and Tables Basic Hive and Impala Query Language Syntax Data Types Differences Between Hive and Impala Query Syntax Using Hue to Execute Queries Using the Impala Shell Data Management Data Storage Creating Databases and Tables Loading Data Altering Databases and Tables Simplifying Queries with Views Storing Query Results Data Storage & Performance Partitioning Tables Choosing a File Format Managing Metadata Controlling Access to Data Relational Data Analysis with Hive & Impala Joining Datasets Common Built-In Functions Aggregation and Windowing Working with Impala How Impala Executes Queries Extending Impala with User-Defined Functions Improving Impala Performance Analyzing Text and Complex Data with Hive Complex Values in Hive Using Regular Expressions in Hive Sentiment Analysis and N-Grams Conclusion Hive Optimization Understanding Query Performance Controlling Job Execution Plan Bucketing Indexing Data Extending Hive SerDes Data Transformation with Custom Scripts User-Defined Functions Parameterized Queries Choosing the Best Tool for the Job Comparing MapReduce, Pig, Hive, Impala, and Relational Databases Which to Choose?

Duration 2 Days 12 CPD hours This course is intended for IBM SPSS Statistics users who want to familiarize themselves with the statistical capabilities of IBM SPSS StatisticsBase. Anyone who wants to refresh their knowledge and statistical experience. Overview Introduction to statistical analysis Describing individual variables Testing hypotheses Testing hypotheses on individual variables Testing on the relationship between categorical variables Testing on the difference between two group means Testing on differences between more than two group means Testing on the relationship between scale variables Predicting a scale variable: Regression Introduction to Bayesian statistics Overview of multivariate procedures This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques and how to apply them with confidence, interpret their output, and graphically display the results. Introduction to statistical analysis Identify the steps in the research process Identify measurement levels Describing individual variables Chart individual variables Summarize individual variables Identify the normal distributionIdentify standardized scores Testing hypotheses Principles of statistical testing One-sided versus two-sided testingType I, type II errors and power Testing hypotheses on individual variables Identify population parameters and sample statistics Examine the distribution of the sample mean Test a hypothesis on the population mean Construct confidence intervals Tests on a single variable Testing on the relationship between categorical variables Chart the relationship Describe the relationship Test the hypothesis of independence Assumptions Identify differences between the groups Measure the strength of the association Testing on the difference between two group meansChart the relationship Describe the relationship Test the hypothesis of two equal group means Assumptions Testing on differences between more than two group means Chart the relationship Describe the relationship Test the hypothesis of all group means being equal Assumptions Identify differences between the group means Testing on the relationship between scale variables Chart the relationship Describe the relationship Test the hypothesis of independence Assumptions Treatment of missing values Predicting a scale variable: Regression Explain linear regression Identify unstandardized and standardized coefficients Assess the fit Examine residuals Include 0-1 independent variables Include categorical independent variables Introduction to Bayesian statistics Bayesian statistics and classical test theory The Bayesian approach Evaluate a null hypothesis Overview of Bayesian procedures in IBM SPSS Statistics Overview of multivariate procedures Overview of supervised models Overview of models to create natural groupings