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68.909 resultaten

C# Programming

Amsterdam ma 27 jul. 2026 en 9 andere data
In the course C# Programming participants learn to program in the . C# Introduction The course C# Programming starts with a discussion of the essentials of the .NET Framework and .NET Core. Covered are the Common Language Runtime, managed code, assemblies and garbage collection. Language Syntax Next attention is paid to variables, data types, operators and loops. Calling methods and dealing with arrays and strings is also part of the course. Classes and Objects Then object-oriented programming with classes and objects is discussed. Concepts such as encapsulation, inheritance and polymorphism are explained. There is also attention for error handling by means of exception handling. Multithreading Subsequently the participants learn to work with multiple threads and the implementation of concurrent tasks. The coordination between threads through synchronization mechanisms such as events and Monitor Wait and Pulse is also discussed. Special Classes The program of the course C# Programming also includes a number of special classes such as delegates, lambdas, properties, indexers and attributes. And attention is paid to Regular Expressions with the RegExp class. Generics and Collections The C# Programming course concludes with a discussion of parameterized types and methods called generics. Generics are often used in collection classes that are next on the program. Finally attention is paid to File I/O with C# libraries.   Audience C# Programming Course This course is intended for aspiring developers who want to learn the C# programming language and its usages in .NET applications. Prerequisites C# Programming Course No specific prior knowledge is required for this course. Experience in other programming languages such as JavaScript, Java or C++ is beneficial to understanding. Realization Training C# Programming The theory is presented on the basis of presentation slides. Demos are used to clarify the discussed concepts. The theory is interspersed with exercises. The course material is in English. Certification C# Programming Participants receive an official certificate C# Programming after successful completion of the course. Modules Module 1 : C# Intro C# Versions .NET Architecture .NET Core Common Language Runtime Managed Code C# Compilation and Execution Managed Execution Assemblies MSIL and Metadata Garbage Collection .NET Framework Class Library Module 2 : Language Syntax C# Data Types Variables and Scope Operators Flow Control if and switch Statement for and foreach Loops while Statement do while Statements break and continue Strings and Arrays Methods and Parameter Passing Module 3 : Classes and Objects Class Definition Encapsulation Access Modifiers Constructors Creating Objects Fields and Properties static Modifier Overloading Constants Common Type System Value and Reference Types Module 4 : Inheritance Derived Classes Overriding Methods Hiding Methods Polymorphism Abstract Classes Interfaces Implementing Interfaces Type Casting Implicit and Explicit Casting Module 5 : Exception Handling Error Conditions Exceptions in C# Exception Handling Syntax Exception Flow Exceptions Template Exceptions Object finally Clause Throwing Exceptions User Defined Exceptions Module 6 : Namespaces Defining Namespaces Using Namespaces Nested Namespaces Namespace Directory Assemblies and Modules Assembly Manifest Types of Assemblies Global Assembly Cache Strong Names Module 7 : Threads Thread Benefits and Drawbacks C# Thread Model Thread Class Thread Stack Thread Delegate Autonomous Classes Passing Parameters Thread Naming Background Threads Thread Exceptions Thread Methods Module 8 : Synchronization Concurrent Method Invocation Blocking on Monitor Lock Statement Mutual Exclusion in C# Joining Threads Interrupting Threads DeadLock Wait Handles Interthread Communication Condition Synchronization Monitor Wait and Pulse Module 9 : Special Classes What is a Delegate? Multicasting Delegates and Events Enumerations Extension Methods Partial Classes Attributes Attribute Parameters Custom Attributes Nullable Types Static Classes Module 10 : Utility Classes Object Class Boxing and Unboxing Overriding Equals Math Class DateTime Structure Regex Class Process and Environment Class Localizing Dates and Numbers Module 11 : Generics What are Generics? Need for Generics Generic Class Syntax Multiple Generic Parameters Bounded Types Runtime Type Parameter Constraints Generic Methods Module 12 : Collections Framework Classes Predefined Collections Array and List Class Queue and Stack Class Linked List Sorted List Dictionary Hashtable Module 13 : File I/O I/O Classes Accessing Text Files Using Directive Accessing Binary Files Buffered Streams Serialization Accessing File System Directory Classes
€2.650
Klassikaal
max 12
5 dagen

C++ Programming

Amsterdam ma 6 jul. 2026 en 9 andere data
In the course C++ Programming participants learn to program in the C++ language. Differences C and C++ First the differences between C and C++ are discussed concerning variable declarations, formatted output with the stream IO library, namespaces, function overloading and default function parameters. References Subsequently the new C++ reference variables are discussed. Attention is paid to both Lvalue and Rvalue references. C++ Classes An important element of the course is the C++ class concept and C++ implementation of object-oriented principles such as abstraction and encapsulation. Attention is paid to dynamic memory allocation with new and delete and the role of assignment operators and copy and move constructors. Also special features of classes such as statics, friends and iterators are discussed. Inheritance and Polymorfisme Next the object-oriented principles of inheritance and polymorphism are part of the subject matter. This includes the concepts of virtual functions, v-tables, dynamic binding and abstract classes. Operator Overloading C++ has the option to give existing operators a different meaning and this phenomenon is discussed in the module operator overloading. Templates en Standard Template Library Attention is paid to important features of the standard C++ library like the String class and the base concepts of C++ templates and the Standard Template Library (STL). Exception Handling Finally exception handling and how this is implemented in C++ is addressed. A follow up course for the course C++ Programming is Advanced C++ Programming. Audience C++ Programming Course The course C++ Programming is intended for developers who want to learn programming in C++ and others who want to understand C++ code. Prerequisites Course C++ Programming Knowledge of and experience with C programming is required to attend this course. Realization Training C++ Programming The theory is treated on the basis of presentation slides and is interspersed with exercises. Illustrative demos are used to clarify the discussed concepts. The course material is in English. Certification C++ Programming Participants receive an official certificate C++ Programming after successful completion of the course. Modules Module 1 : Intro C++ Intro C++ C++ TimeLine Comments in C++ Namespace std Output and Error Stream Standard Input Stream cin and Strings Formatted Output Variable Declaration Scope Resolution Operator Inline Functions Default Function Arguments Overloading Functions Range based for loop Module 2 : Variables and Types Standard Types Type Inference Auto Keyword Deduction with decltype Initialization Null Pointer Constant Strongly Types Enums Variable Scope Namespaces Using keyword and Directive Block Usage User Defined Literals Storage Classes const Qualifier Module 3 : References References Reference Initialization References and Pointers Rvalues and Rvalues in C Rvalues and Rvalues in C++ Reference to Constant Passing References Comparison Parameter Passing References as Return Values Returning lvalue Returning Reference to Global Rvalue References Comparing Reference Types Rvalue Reference Usage Module 4 : Classes Classes and Objects Classes in C++ Class Declaration Class Sections Constructor and Destructor Uniform Initialization Header and Sources Files Class Implementation Advantages Access Functions References to private Data this Pointer static Members Constant Objects Member Objects Friends Module 5 : Dynamic Memory Allocation new and delete Operators Dynamic Arrays Classes with Pointer Data Assignment Operator Self-Assignment Problem Chained Assignments Assignment and Initialization Copy Constructors Passing Objects Returning Objects Passing References to Objects Move Constructor Move Assignment Operator Perfect Forwarding Delegating Constructors Module 6 : Inheritance Inheritance Derived Classes in C++ Class Hierarchy Redefining Member Functions Derived Class Constructors Base - Derived Class Conversion Pointer Conversions Virtual Functions Polymorphism Dynamic Binding Virtual Function Table Pure Virtual Functions Abstract Classes Multiple Inheritance Virtual Derivation Module 7 : Operator Overloading Operator Overloading Overloading for Numeric Types Complex Type Example Overloading Rules Overloading Restrictions Not Overloadable Operators When not to Overload Numeric Class Overloading Operators as Friend Unary Overloading Operator Module 8 : Exception Handling Exception Handling in C++ Memory Exhaustion Handling Throwing Exceptions try Block catch Handlers Multiple catch Handlers Template Array Class Exceptions Array Class catch Order throw List Module 9 : Templates What are Templates? Template Functions Template Specialization Template Parameter List Class Templates Template Parameter Scope Template Function Statics Template Class Statics Inclusion Compilation Model Templates and Friends Module 10 : STL Standard Template Library STL Core Components STL Library Components STL Containers Vector Container Deque Container List Container STL Iterators STL Algorithms STL Allocators
€2.999
Klassikaal
max 12
5 dagen

Continuous Delivery

Amsterdam do 25 jun. 2026 en 9 andere data
In the course Continuous Delivery participants learn how a continuous delivery process for automatic testing and deployment of software applications can be set up with Docker and Jenkins. Intro Continuous Delivery The course Continuous Delivery primarily explains the principles of Continuous Delivery. In a Continuous Delivery process software is released frequently in short cycles, tested and deployed via an automated deployment pipeline. Docker Containers Next the use of Docker Containers for quickly launching a furnished environment is discussed. The operation, architecture and configuration of Docker Containers is covered in detail. Jenkins Essentials Attention is also paid to the Jenkins tool for automating software building. This involves setting up a Jenkins Continuous Integration Pipeline and explaining the content and structure of the Jenkins file. Acceptance Testing The automation of Acceptance Tests with Docker and Cucumber is also part of the course program. And there is attention for the different environments in a Continuous Delivery process and the tests that are done in them. Configuration Management Then Application and Infrastructure Configuration and the use of the configuration language Ansible with Playbooks, Handlers and Variables are treated. Advanced Topics Finally a number of advanced Continuous Delivery aspects are discussed, such as dealing with changes in the Database, parallelizing pipelines and the use of shared libraries. Audience Course Continuous Delivery The course Continuous Delivery is intended for developers, testers and administrators who are involved in software development and who want to implement continuous delivery. Prerequisites Course Continuous Delivery General knowledge and familiarity with software development, programming, testing and deployment is required to participate in the course Continuous Delivery. Realization Training Continuous Delivery The theory is discussed on the basis of presentations. The concepts are illustrated with demos. The theory is interspersed with exercises. The course times are from 9.30 to 16.30. Certificate Course Continuous Delivery Participants receive an official certificate Continuous Delivery after successful completion of the course. Modules Module 1 : Intro Continuous Delivery What is Continuous Delivery? Traditional Delivery Process Shortcomings Traditional Delivery Benefits of Continuous Delivery Fast Delivery and Feedback Cycle Low Risk Releases Automated Deployment Pipeline Continuous Integration Automated Acceptance Testing Configuration Management DevOps Culture Module 2 : Docker Essentials Virtualization and Containerization Disadvantages of Virtualization Benefits of Containers Isolation and Portability Installing Docker Docker Architecture Docker Components Docker Client Docker Server Docker Daemon Docker REST API Module 3 : Docker Applications Building Docker Images Docker Commit Dockerfile Environment Variables Running Docker Containers Docker Container States Docker Networking Container Networks Exposing Container Ports Automatic Port Assignment Using Docker Volumes Module 4 : Jenkins Essentials What is Jenkins? Extensibility by Plugins Jenkins Installation Installing on Docker Jenkins Pipeline Master and Slaves Vertical and Horizontal Scaling Test and Production Instances Configuring Agents Jenkins Swarm Agents Custom Jenkins Images Module 5 : CI Pipeline What is a Pipeline? Multi Stage Application Sections, Directives and Steps Commit Pipeline Pushing to GitHub Compile Stage Unit Test Stage Jenkinsfile Code Coverage and CheckStyle Scheduled Builds Development Workflows Module 6 : Acceptance Testing Acceptance Testing Intro? Docker Registry Artifact Repository Docker Hub Private Docker Registry Domain Certificates Building Images Pushing and Pulling Images Acceptance Test in Pipeline Acceptance Testing Stage Running Acceptance Tests Module 7 : Configuration Management Application Configuration Infrastructure Configuration Automation and Version Control Configuration Languages Chef, Puppet and Ansible Agent Based Using Ansible Creating Inventory Playbooks Handlers and Variables Deployment with Ansible Working with Redis Ansible and Docker Module 8 : CI Pipeline Types of Environment Production Environment Staging Environment Test Environment Development Environment Non Functional Testing Performance Testing Load and Stress Testing Scalability Testing Security Testing Non Functional Challenges Application Versioning Complete Jenkins File Module 9 : Docker Swarm Server Clustering Docker Swarm Intro Setting up a Swarm Adding Worker Nodes Deploying a Service Publishing Ports Rolling Updates Draining Nodes Multiple Manager Nodes Scheduling Strategy Docker Stack Specifying docker-compose.yml Kubernetes Module 10 : Advanced Continuous Delivery Managing Database Changes Understanding Schema Updates Database Migrations Using Flyway Configuring Flyway SQL Migration Script Backwards Compatibility Changes Non-Backwards Compatibility Changes Adding and Dropping Columns Changing Code Merging Data Avoiding Shared Database Parallelizing Pipelines Shared Libraries
€1.499
Klassikaal
max 12
2 dagen

CSS Fundamentals

Amsterdam do 23 jul. 2026 en 9 andere data
In the course CSS Fundamentals participants learn to use Cascading Style Sheets (CSS) for the layout and style of HTML Web pages. CSS Intro The course CSS Fundamentals starts with an explanation of the basic principles of CSS. This includes how CSS selectors select HTML elements based on ID or class attributes or their position in the hierarchy of the page and then apply styling to them. Text and Fonts Next the CSS syntax is treated in more detail on the basis of the layout of text with colors, fonts and backgrounds. The cascading aspect of CSS and the inheritance concept is also covered. CSS Box Model Attention is also paid to the CSS Box Model. While laying out an HTML page, the rendering engine of the browser represents each element as a rectangle according to the CSS Basic Box Model with a margin, padding and an outline. CSS Layout The course program also describes how CSS can be used to control the layout of the page. Elements have default block or inline display values ​​but this can be changed with CSS. Other CSS layout attributes such as float or overflow are discussed as well. Tables and Grids Then it is treated how CSS can control the representation of tables, list and grids. Attention is paid to the various gap properties that are used. CSS Advanced Finally a number of advanced applications of CSS are covered, such as applying round corners, working with shadows and color gradients. 2D and 3D transformations with CSS3 are also discussed in this respect.   Audience CSS Fundamentals Course The course CSS Fundamentals is designed for persons who wish to learn the usage of CSS for the styling of Web Pages. Prerequisites Course CSS Fundamentals To join this course is no specific skills or knowledge is required. Realization Training CSS Fundamentals The concepts are treated with the help presentation slides. A demo Web site is used to clarify the concepts. Attention is also paid to hands-on exercises. The course material is in English. The course times are from 9.30 up and to 16.30. Certification CSS Fundamentals Participants receive an official certificate CSS Fundamentals after successful completion of the course. Modules Module 1 : CSS Intro What is CSS? CSS Standard CSS Syntax CSS Selectors Basic Selectors CSS Rules Styling in Place Internal Style Tag External Style Sheets What is Cascading? Checking Browser Support Caniuse Site Module 2 : Text and Fonts Working with Fonts Formatting Text Font Families Font Style and Size Font Color Font Web Safe Font Fallbacks Text Alignment Text Decoration Text Transformation Borders CSS Backgrounds Module 3 : CSS Box Model Basic Box Model Margin Edge Padding Edges Content edge Border Edge Margin Collapsing Box Background Containing Block CSS Outline Outline Shorthand Outline Offset Outline Width Module 4 : CSS Layout Layout Concepts display Property Block Level Elements Inline Elements Units of Measurement width and max-width Element Positioning Position and Overflow float Property Overflow inline Blocks Module 5 : Tables and Grids Table Borders Table Styles Collapsing Borders Full-Width Tables Styling Lists Navigation Bar Grid Layout Grid Elements Grid Columns CSS Counters CSS Links Module 6 : CSS Advanced CSS Rounded Corners CSS Combinators Pseudo Classes Pseudo Element CSS Gradients CSS Shadows Text Effects CSS Resets CSS3 Transformations 2D Transforms 3D Transforms
€1.299
Klassikaal
max 12
2 dagen

PySpark for Big Data

Amsterdam ma 15 jun. 2026 en 9 andere data
In the course PySpark for Big Data participants learn to use Apache Spark from Python. Spark Architecture The course PySpark for Big Data discusses the architecture of Spark, the Spark Cluster Manager and the difference between Batch and Stream Processing. Hadoop After a discussion of the Hadoop Distributed File System, parallel operations and working with RDDs, Resilient Distributed Datasets are discussed in the course PySpark for Big Data. The configuration of PySpark applications via SparkConf and SparkContext is also explained. MapReduce en SQL Extensive consideration is given to the possible operations on RDDs, including map and reduce. The use of SQL in Spark is also discussed. The GraphX library is discussed and DataFrames is discussed. Iterative algorithms are also treated. Mlib library Finally the course PySpark for Big Data pays attention to machine learning with the Mlib library. Audience PySpark for Big Data The course PySpark for Big Data is intended for developers and upcoming Data Analysts who want to learn how to use Apache Spark from Python. Prerequisites training PySpark for Big Data To participate in this course, some experience with programming is beneficial for understanding. Prior knowledge of Python or big data handling with Apache Spark is not required. Realization course PySpark for Big Data The theory is treated on the basis of presentations. Illustrative demos are used to clarify the concepts discussed. There is ample opportunity to practice and alternate theory and practice. The course times are from 9.30 am to 4.30 pm. Certification course PySpark for Big Data Participants receive an official certificate PySpark for Big Data after successful completion of the course. Modules Module 1 : Python Primer Python Syntax Python Data Types List, Tuples, Dictionaries Python Control Flow Functions and Parameters Modules and Packages Comprehensions Iterators and Generators Python Classes Anaconda Environment Jupyter Notebooks Module 2 : Spark Intro What is Apache Spark? Spark and Python PySpark Py4j Library Data Driven Documents RDD's Real Time Processing Apache Hadoop MapReduce Cluster Manager Batch versus Stream Processing PySpark Shell Module 3 : HDFS Hadoop Environment Environment Setup Hadoop Stack Hadoop Yarn Hadoop Distributed File System HDFS Architecture Parallel Operations Working with Partitions RDD Partitions HDFS Data Locality DAG (Direct Acyclic Graph) Module 4 : SparkConf SparkConf Object Setting Configuration Properties Uploading Files SparkContext.addFile Logging Configuration Storage Levels Serialize RDD Replicate RDD partitions DISK_ONLY MEMORY_AND_DISK MEMORY_ONLY Module 5 : SparkContext Main Entry Point Executor Worker Nodes LocalFS SparkContext Parameters Master RDD serializer batchSize Gateway JavaSparkContext instance Profiler Module 6 : RDD’s Resilient Distributed Datasets Key-Value pair RDDs Parallel Processing Immutability and Fault Tolerance Transformation Operations Filter, groupBy and Map Action Operations Caching and persistence PySpark RDD Class count, collect, foreach,filter map, reduce, join, cache Module 7 : Spark Processing SQL support in Spark Spark 2.0 Dataframes Defining tables Importing datasets Querying data frames using SQL Storage formats JSON / Parquet GraphX GraphX library overview GraphX APIs Module 8 : Broadcast and Accumulator Performance Tuning Serialization Network Traffic Disk Persistence MarshalSerializer Data Type Support Python’s Pickle Serializer DStreams Sliding Window Operations Multi Batch and State Operations Module 9 : Algorithms Iterative Algorithms Graph Analysis Machine Learning API mllib.classification Random Forest Naive Bayes Decision Tree mllib.clustering mllib.linalg mllib.regression
€2.450
Klassikaal
max 12
3 dagen

Data Analysis with Python

Amsterdam ma 15 jun. 2026 en 9 andere data
In the course Data Analysis with Python you will learn how to use the Python language and Python libraries in Data Analysis projects. Python Overview The course Data Analysis with Python starts with a bird's eye view of the Python syntax aspects that are important in Data Analysis projects. Variables, data types, functions, flow control, comprehensions, classes, modules and packages are discussed. The operation of the Jupyter notebooks, the IPython shell and installing Python packages in Anaconda are also treated. Numpy Next the course Data Analysis with Python pays attention to the NumPy package with which large data sets can be processed very efficiently. NumPy's ndarray object and its methods are treated and attention is paid to the different array manipulation techniques with broadcasting and vectorized operations. Pandas Then use of the Pandas library for data analysis is on the schedule of the course Data Analysis with Python. The pandas library introduces two new data structures in Python that use Numpy and are therefore fast. The data structures are DataFrame and Series and extensive details are given on how to use them for data analysis when inspecting, selecting, filtering, combining and grouping data. MatPlotLib Also discussed in the course Data Analysis with Python is the MatPlotlib library, which is closely integrated with NumPy and is a very powerful tool for creating and plotting complex data relationships. Scikit-Learn Finally attention is paid to the essentials of the Scikit-Learn library for modeling. The course Data Analysis with Python uses many practical examples and shows how one- and two- and three-dimensional data sets can be visualized. Audience Course Data Analyse with Python The course Data Analysis with Python is intended for data analysts who want to use Python and the Python libraries in Data Analysis projects. Prerequisites training Data Analyse with Python To participate in this course knowledge of and experience with any programming language or package such as SPSS, Matlab or VBA is desirable. The course starts with a discussion of the principles of the Python programming language. Realization course Data Analyse with Python The theory is discussed on the basis of presentation slides. Illustrative demos clarify the concepts. The theory is interchanged with exercises. The Anaconda distribution with Jupyter notebooks is used as a development environment. Course times are from 9:30 to 16:30. Official Certificate Data Analysis with Python After successful completion of the course participants receive an official certificate Data Analysis with Python. Modules Module 1 : Python Language Syntax Python Features Running Python Anaconda Distribution IPython Shell Interactive and Script Mode Python Data Types Numbers and Strings Sequences and Lists Sets and Dictionaries Python Flow Control Exception Handling Module 2 : Functions and Modules Pass by Value and Reference Scope of Variables EFAP principle What are comprehensions? Lambda Operator Filter, Reduce and Map List comprehensions Set and Dictionary comprehensions Creating and Using Modules import Statement from…import Statement Module 3 : Classes and Objects Creating Classes Creating and Using Objects Accessing Attributes Property Syntax Constructors and Destructors Encapsulation Inheritance super Keyword Checking Relationships issubclass and isinstance Overriding Methods Module 4 : Numpy NumPy Numerical Types Data Type objects dtype attributes Slicing and Indexing Array comparisons Manipulating array shapes Stacking and Splitting arrays any(),all(), slicing, reshape() Manipulating array shapes Methods of ndarray Views versus copies ravel(),flatten(),transpose() Module 5 : Pandas Pandas DataFrame Import Data Inspect Data Data Visualization DataFrame Data Types Indexing and selection Data operations in pandas Missing Data Hierarchical Indexing Plotting with Pandas Combining Datasets Exploratory Data Analysis Module 6 : Data Manipulation Indexing Data Frames .loc and .iloc Accessor Slicing and Indexing a Series Filtering with Boolean Series Zeros and NaNs all and any Nonzeros Using map Function Hierarchical Indexing Rearranging Data Reshaping by Pivoting Transformation and Aggregation Grouping Data Module 7 : MatplotLib Simple Plots Plot format String Subplots Histograms Logarithmic Plots Scatter plots Fill between Legend and Annotations Three Dimensional Plots Contour Plots Transformations Projections Module 8 : Time Series Indexing Pandas Time Series Reading and Slicing Times Using a DatetimeIndex Reindexing the Index Separating and Resampling Rolling mean and Frequency Resample and Roll with it Manipulating Time Series Method chaining and Filtering Missing values and Interpolation Time Zones and Conversion Plotting Time Series Module 9 : SciKitLearn Essentials SkiKit Learn library Machine learning essentials Supervised and Unsupervised Feature matrix Target array Estimator API Hyperparameters Fit method Predict method Model Selection Linear Regression Logistic Regression
€2.650
Klassikaal
max 12
4 dagen

Data Analysis with R

Amsterdam ma 29 jun. 2026 en 9 andere data
In the course Data Analysis with R you will learn programming in the R language and how you can use R for data analysis and visualization. R Intro The course Data Analysis with R starts with the installation of R and the R Studio development environment. The basic syntax of R and the installation of R packages are also discussed. Plotting in R Next you will learn how you can quickly gain insight into the data with the ggplot2 package by means of plots. The different plot types, themes and layouts are discussed as well. Transformations Then it is time for the dplyr package with which common data transformation problems such as filtering, sorting, summation and grouping can be solved. Data Cleaning Presenting data with the rmarkdown package is also covered. As well as tidying raw data with the tidyr package, where columns become variables and rows become observations. Date and Times Time series occur in many data sets. The processing of these time series is addressed with the lubridate package that has many useful functions for processing dates and time. Data Import Part of the course program is also the import of data from CSV files and file formats from other statistical packages such as SPSS or SAS. Reading from and writing to databases is also treated. Statistical Analysis Finally the course Data Analysis with R deals with statistical analysis models such as linear and non-linear models, variable transformations and regressions. All this is supported with many practical examples and can also be applied to cases that are brought along by the students. Audience Course Data Analysis with R The course Data Analysis with R is intended for Big Data analysts and scientists who want to use R to analyze their data and to make static analyzes. Prerequisites Data Analysis with R Experience with programming is beneficial to good understanding but is not required. Realization Training Data Analysis with R The theory is discussed on the basis of presentations and examples. The concepts are explained with demos. Then there is time ample to practice with it yourself. R-Studio is used as a development environment. Course times are from 9:30 am to 16:30 pm Certification Course Data Analysis with R After successful completion of the course the participants receive an official certificate R Programming. Modules Module 1 : Intro R Overview of R History of R Installing R The R Community R Development R Studio R Console R Style Using R Packages Cheatsheets R Syntax R Objects Module 2 : Graphics and Plots ggplot2 Graphics Devices and Colors High-Level Graphics Functions Low-Level Graphics Functions Graphical Parameters Controlling the Layout Changing Plot Types Quick Plots and Basic Control Aesthetics Changing Plot Types Labels Themes and Layout Module 3 : Transformations dplyr R Functions Functions for Numeric Data Scoping Rules mutate arrange group by summarize select filter joining dataframe Module 4 : Presentation rmarkdown Reproducible research Reporting Sharing results Repetitive Tasks Family of apply Functions apply Function lapply Function sapply Function tapply Function Module 5 : Data Cleaning tidyr spread gather seperate unite Logical Data Missing Data Character Data Duplicate Values NA’s Module 6 : Date Times Time and Date Variables lubridate Setting a datetime Getting values from a datetime strftime Command strptime Command as.Date function Datetimes Calculations difftime Command Time Series Analysis Module 7 : Data Import R Datasets Data.Frames Importing CSV Files Import from Text Files Import from Excel Import from Spss or SAS Connecting to a database Connecting to a cluster Databases and ODBC dbplyr Module 8 : Linear Models What is a model? Statistical Models in R How to evaluate a model? How to use a model? Simple Linear Models logistic regression linear regression R squared p values confidence intervals Module 8 : Non-Linear Models Decision Trees random forest boosting overfitting Optional material : Interactive dashboards with Shiny Web Scraping Writing packages Spark Functional programming
€2.650
Klassikaal
max 12
4 dagen

Database Design

Amsterdam do 25 jun. 2026 en 9 andere data
In the course Database Design participants learn the techniques and considerations for creating a well-structured database. Intro Database Management Systems The course Database Design starts with a discussion of the basic architecture of Database Management Systems. Attention is paid to the Database Schema and the differences between the conceptual, logical and physical model. The role of SQL, Data Definition Language (DDL) and Data Manipulation Language (DML) is also discussed. Database Design Subsequently the phases of Database Design and the components of a database are treated. The ERD Model and the UML Model are covered here. Possible design errors and the application of constraints are also reviewed. ER Modeling In the ER Modeling section participants learn how to discover the entities and their relationships and map them to tables. They learn the principles of Entity Relationship Modeling. Also treated is how to find and model attribute domains. Table Mapping Then attention is paid to how entities and their relations can be translated into tables in a relational database. The different mapping strategies for hierarchies of entities are also covered such as table per class, table per hierarchy and the use of discriminator columns. UML Modeling Next to the use of Unified Modeling Language UML for database design is treated. The UML syntax is discussed as well as UML elements such as interfaces, associations, composition, generalization and dependencies. Normalization and Optimization The process of normalization, the different normal forms and the removal of duplicate data are explained by means of practical examples. Finally, a number of optimization techniques, such as the use of indexes, that can improve the speed of databases are discussed. Audience Course Database Design The course Database Design in intended for Web developers, web application developers, database administrator, webmasters and web project managers. Prerequisites Course Database Design To join the course Database Design no specific skills or knowledge is required. General knowledge of system design is helpful to a proper understanding. Realization Training Database Design The theory is treated using presentation slides. Demos are used to clarify the theory. There is ample opportunity to practice. The course material is in English. The course times are from 9.30 up and to 16.30. Certification Database Design Participants receive an official certificate Database Design after successful completion of the course. Modules Module 1 : Intro DBMS What is a DBMS? DBMS Abstraction Levels Data Independence Database Model Types of Databases Database Schema Conceptual Model Logical Model Physical Model SQL Language DDL and DML Language Application Interfaces Transactions DBMS Architecture Module 2 : Database Design What is Database Design? Database Design Phases Benefits of Phases Conceptual Data Model Entity Relationship Model UML Model Structuring the Model Design Errors Data Errors Constraints Database Constraints Naming Schema Elements Data Interpretation CASE Tools Module 3 : Entity Relationship Modeling E-R Model Components Identification Guidelines Entities versus Entity Classes Attributes Entities versus Attributes Classification of Attributes Attribute Domains Relationships Degree of relationships Relationship Cardinalities Notation of Cardinalities Removing M:N relations Requirement Analysis Resulting ER Diagram Module 4 : Advanced Er Modeling Weak Entity Set Generalization and Specialization Design Constraints Total and Partial Participation Disjoint Constraints Overlapping Constraints Aggregation ER Design Decisions Mapping ERD to Tables Composite Attributes Multivalued Attributes Redundancy As Tables Module 5 : Mapping ERD to Tables Entity Set Table Translation Relationship Table Translation Mapping Key Constraints Map Relationship Set to Table Combine Relationship and Entity Set Weak Entity Sets Mapping Weak Entity Sets Mapping Subclasses Table per Subclass Table per Hierarchy Discriminator Columns Joining Tables Module 6 : UML Modeling What is UML? Structural Modeling? Core Elements Core Relationships Structural Diagrams Classes and Objects Class Diagrams Interfaces Associations Composition Generalization Dependencies Module 7: Normalization What is Normalization? Unnormalized form Moving towards 1NF First Normal Form Moving to 2NF Second Normal Form Third Normal Form Other Normal Forms Benefit of Normalization Relationship Cross Tables Module 8: Database Optimization Optimization Process Use Ranges Denormalize Denormalization Issues Combine Tables Store Derived Data Add Indexes Index Operation Sorting Clustered Indexes
€1.499
Klassikaal
max 12
2 dagen

Design Patterns

Amsterdam wo 17 jun. 2026 en 9 andere data
In the course Design Patterns you will learn how design patterns can be applied to the object oriented design of systems. Design Patterns Intro After an introduction about the role that design patterns play and how they can be used to realize the non-functional requirements of systems, attention is paid to how design patterns are described and cataloged. Architectural Role Also the role of design patterns in the architecture of applications is discussed and the various categories of design patterns that are distinguished. Creational Patterns In the module Creational Patterns the Factory patterns and the Builder, Prototype and Singleton pattern are discussed. You learn out of which classes, relationships, responsibilities and cooperations a typical design pattern solution can consist. Structural Patterns Next in the module the Structural Patterns the Adapter, Composite, Bridge, Decorator, Proxy and Flyweight pattern are discussed. You will learn the consequences of applying the patterns, the benefits and possible disadvantages in terms of time and space considerations and how to decide on the use of a particular pattern. Behavioral Patterns Next in the module Behavioral Patterns the Chain of Responsibility, Interpreter, Iterator, Mediator, State and Strategy patterns are discussed. Architectural Patterns Finally the module Architectural Patterns considers certain patterns that are involved in the architectural structure of software including Operating Systems and Frameworks. This module focuses on the Layer pattern, the Micro Kernel pattern and the Model View Controller (MVC) pattern. Audience Course Design Patterns The course Design Patterns is intended for experienced developers and software architects with knowledge of object oriented programming and systems analysis who want to apply Design Patterns when designing these systems. Prerequisites Course Design Patterns Knowledge of an object-oriented programming language like C++, C#, or Java and experience with object oriented analysis and design with UML is required. Realization Training Design Patterns The concepts are treated according to presentation slides. The theory is illustrated with demos of patterns in C++, C# and Java. There are exercises in design problems where patterns are applied. The course material is in English. The course times are from 9.30 up and to 16.30. Certification Design Patterns Participants receive an official certificate Design Patterns after successful completion of the course. Modules Module 1 : Intro Design Patterns What is a design pattern? Describing design patterns Reuse through design patterns Structure of patterns Classification of patterns Catalog of Design Patterns Creational Patterns Structural Patterns Behavioral Patterns Sample design patterns Selecting Design Patterns Solving problems with design patterns Module 2 : Creational Patterns Factory Patterns Factory Method Pattern Connect parallel class hierarchies Abstract Factory Pattern Concrete Class Isolation Promoting Consistency Builder Pattern Controlling the build process Prototype Dynamic configuration Singleton Pattern Controlled access Module 3 : Structural Patterns Adapter Pattern Pluggable Adapters Composite Pattern Sharing Components Decorator Pattern Lots of little objects FaÇade Pattern Reducing client-subsystem coupling Flyweight Pattern Reducing number of instances Proxy Pattern Copy-on-write Module 4 : Behavioral Patterns Chain of responsibility Command Pattern Interpreter Pattern Iterator Pattern Mediator Pattern Memento Pattern Observer Pattern State Pattern Strategy Pattern Template Pattern Module 5 : Architectural Patterns Architectural patterns versus design patterns Patterns for real-time software Layers Pipes and Filters Blackboard Broker Model-View-Controller Presentation-Abstraction-Control Microkernel Reflection
€1.999
Klassikaal
max 12
3 dagen

Development with Maven

Amsterdam ma 22 jun. 2026 en 9 andere data
In the course Maven Development participants will learn the skills and knowledge needed to use Maven as an automated build and dependency management tool. Maven Intro The course starts with an overview of the problems in project and dependency management, how Maven works and the role of Maven repositories. Explained is how Maven compares to the automatic build tool Ant. Maven Projects Next the directory structure of Maven projects and the standard life cycle are discussed. Maven projects can be created using predefined archetypes that have a certain project structure built in from the start. The role of Maven goals and plugins is also covered. Project Object Model Then the Project Object Model (POM) with pom.xml is treated. The meaning of the main entries therein such as Group, Artifact and Version are discussed and also a more complex structure with multiple pom files and pom inheritance. Archetypes The Maven Archetypes are also part of the program of the course Maven Development. Attention is paid to a number of commonly used archetypes. Also discussed is how you can create archetypes yourself with the Maven Archetype plugin and which are provided with a prototype POM and prototype files. Repositories Furthermore Maven Repositories such as the Maven Central repository, Enterprise Repositories, the Local Developer Repository and Remote Repositories are treated. The order in which Maven searches the repositories is discussed and Plugin Repositories are covered as well. Build Automation Finally attention is paid to the role of Maven in performing tests, to continuous integration and to release management with Bamboo, Team City or Jenkins. Audience Development with Maven Course The course Maven Development is intended for developers who use Maven for dependency management and for the automatic building and deployment of projects. Prerequisites Course Using Maven To participate in the course Maven Development knowledge of and experience with Java and XML is required. Realization Training Using Maven The theory is discussed on the basis of the presentation slides and is interspersed with exercises. Demo projects in Maven are used to clarify the concepts. The course material is in English. The course focuses on Maven version 3. Certification Development with Maven Participants receive an official certificate Development with Maven after successful completion of the course. Modules Module 1 : Maven Intro Java Build Tools Intro Desired Features Ant + Ivy Build.xml Build File with Ivy Ivy Dependency Management Maven Build Lifecycle pom.xml Gradle Results Matrix Tools Comparison Module 2 : Core Concepts What is Maven? Why Maven? Convention over Configuration Maven Directory Structure Project Object Model Maven Project Coordinates POM Structure POM Sections Plugins Archetypes Catalog File Dependencies Module 3 : Build Lifecycle What is Build Lifecycle? Standard Lifecycles Key Lifecycle Phases Build Phases and Goals Clean Lifecycle Default or Built Lifecycle Default Lifecycle Phases Site Generation and Reporting Site Lifecycle Site Website Customizing the Lifecycle Package-specific Lifecycles Module 4 : Profiles Environment variables User-defined properties Filtering Resources Build Profiles What is a Build Profile? Project Configuration with Profiles Profile Activation Explicit Profile Activation Activation via Maven Settings Activation via Environment Variables Activation via Operating System Activation via Files Module 5 : Plugins and Goals What are Maven Plugins? Plugin Types Goals and Plugins Key Plugin Concepts Maven Antrun Plugin Maven Compiler Plugin Exec Maven Plugin Jetty Maven Plugin Eclipse Maven Integration Maven Checkstyle Plugin Findbugs Maven Plugin Maven PMD Plugin Module 6 : Archetypes What is an Archetype? Different Archetypes Archetype Generate Command Maven Archetype Archetype Maven Archetype WebApp Simple J2EE Project Maven Archetype Simple Site Creating Archetypes Maven Archetype Plugin Archetype Descriptor Prototype POM Prototype Files Module 7 : Repositories What is a Maven Repository? Enterprise Repositories Local Repository Central Repository Repositories in Super POM Remote Repository Maven Search Sequence Plugin Repositories Repository Management Deploying to Nexus with Maven Performing a Staged Release Module 8 : Dependency Management What is Dependency Management? Searching Dependencies Transitive Dependencies Dependency Terminology Dependency Scope Optional Dependencies Version Ranges Project Versions Visualizing Dependencies Dependency Conflicts Excluding Transitive Dependencies Module 9 : Build Automation Handling Rapid Changes What is a Snapshot? Snapshot Dependency Build Automation Using Snapshots Release Management The Maven Release Plugin Developer Release Workflow Integration with Source Control Continuous Integration Deployment Automation
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