Szkolenia Data Visualization w Wrocław

Wrocław, ul.Ludwika Rydygiera 2a/22

NobleProg Classroom Wrocław
ul. Ludwika Rydygiera 2a/22
Wroclaw 50-249
Wrocław, ul.Ludwika Rydygiera 2a/22
NobleProg Wrocław ul. Ludwika Rydygiera 2a/22 50-249 Wrocław Lokal znajduje się w samym centrum miasta (vis-à-vis hotelu HP Park Plaza), zaledwie 10 minut...Read more

Opinie uczestników

Data Visualization

I thought that the information was interesting.

Allison May - Virginia Department of Education

Data Visualization

I really appreciated that Jeff utilized data and examples that were applicable to education data. He made it interesting and interactive.

Carol Wells Bazzichi - Virginia Department of Education

Data Visualization

Learning about all the chart types and what they are used for. Learning the value of decluttering. Learning about the methods to show time data.

Susan Williams - Virginia Department of Education

Data Visualization

Trainer was enthusiastic.

Diane Lucas - Virginia Department of Education

Data Visualization

Content / Instructor

Craig Roberson - Virginia Department of Education

Data Visualization

I am a hands-on learner and this was something that he did a lot of.

Lisa Comfort - Virginia Department of Education

Data Visualization

The examples.

peter coleman - Virginia Department of Education

Data Visualization

The examples.

peter coleman - Virginia Department of Education

Data Visualization

Good real world examples, reviews of existing reports

Ronald Parrish - Virginia Department of Education

A practical introduction to Data Analysis and Big Data

Willingness to share more

Balaram Chandra Paul - MOL Information Technology Asia Limited

Data Visualization Course Events - Wrocław

Kod Nazwa Miejscowość Czas trwania Data Kursu PHP Cena szkolenia [Zdalne / Stacjonarne]
datavis1 Data Visualization Wrocław, ul.Ludwika Rydygiera 2a/22 28 hours pon., 2017-11-06 09:00 35820PLN / 11655PLN
ORABI Wstęp do Oracle Business Intelligence i BI Publisher Wrocław, ul.Ludwika Rydygiera 2a/22 35 hours pon., 2017-11-13 09:00 10000PLN / 4030PLN
ORABI Wstęp do Oracle Business Intelligence i BI Publisher Wrocław, ul.Ludwika Rydygiera 2a/22 35 hours pon., 2018-01-08 09:00 10000PLN / 4030PLN
ORABI Wstęp do Oracle Business Intelligence i BI Publisher Wrocław, ul.Ludwika Rydygiera 2a/22 35 hours pon., 2018-03-12 09:00 10000PLN / 4030PLN

Plany Kursów

Kod Nazwa Czas trwania Spis treści
scilab Scilab 14 hours

Scilab is a well-developed, free, and open-source high-level language for scientific data manipulation. Used for statistics, graphics and animation, simulation, signal processing, physics, optimization, and more, its central data structure is the matrix, simplifying many types of problems compared to alternatives such as FORTRAN and C derivatives. It is compatible with languages such as C, Java, and Python, making it suitable as for use as a supplement to existing systems.

In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing.

By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge.


  • Data scientists and engineers, especially with interest in image processing and facial recognition

Format of the course

  • Part lecture, part discussion, exercises and intensive hands-on practice, with a final project

   Comparison with other languages

Getting started

Matrix operations

Multidimensional data

Plotting and exporting graphics

Creating an ATOMS toolbox

Interface with C, Java, and others

Final project: Image analysis

Closing remarks
   Overview of useful libraries and extensions

matlabdsandreporting MATLAB Fundamentals, Data Science & Report Generation 126 hours

In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform.  Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation.

Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB' capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.

Assessments will be conducted throughout the course to guage progress.

Format of the course

  • Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.


  • Practice sessions will based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange

MATLAB for data science and reporting


Part 01: MATLAB fundamentals

    MATLAB for data analysis, visualization, modeling, and programming.

Working with the MATLAB user interface

Overview of MATLAB syntax

Entering commands
    Using the command line interface

Creating variables
    Numeric vs character data

Analyzing vectors and matrices
    Creating and manipulating
    Performing calculations

Visualizing vector and matrix data

Working with data files
    Importing data from Excel spreadsheets

Working with data types
    Working with table data

Automating commands with scripts
    Creating and running scripts
    Organizing and publishing your scripts

Writing programs with branching and loops
    User interaction and flow control

Writing functions
    Creating and calling functions
    Debugging with MATLAB Editor

Applying object-oriented programming principles to your programs


Part 02: MATLAB for data science

    MATLAB for data mining, machine learning and predictive analytics

Accessing data
    Obtaining data from files, spreadsheets, and databases
    Obtaining data from test equipment and hardware
    Obtaining data from software and the Web

Exploring data
    Identifying trends, testing hypotheses, and estimating uncertainty

Creating customized algorithms

Creating visualizations

Creating models

Publishing customized reports

Sharing analysis tools
    As MATLAB code
    As standalone desktop or Web applications

Using the Statistics and Machine Learning Toolbox

Using the Neural Network Toolbox


Part 03: Report generation

    Presenting results from MATLAB programs, applications, and sample data
    Generating Microsoft Word, PowerPoint®, PDF, and HTML reports.
    Templated reports
    Tailor-made reports
        Using organization’s templates and standards

Creating reports interactively vs programmatically
    Using the Report Explorer
    Using the DOM (Document Object Model) API

Creating reports interactively using Report Explorer
    Report Explorer Examples
        Magic Squares Report Explorer Example

    Creating reports
        Using Report Explorer to create report setup file, define report structure and content

    Formatting reports
        Specifying default report style and format for Report Explorer reports

    Generating reports
        Configuring Report Explorer for processing and running report

    Managing report conversion templates
        Copying and managing Microsoft Word , PDF, and HTML conversion templates for Report Explorer reports

    Customizing Report Conversion templates
        Customizing the style and format of Microsoft Word and HTML conversion templates for Report Explorer reports

    Customizing components and style sheets
        Customizing report components, define layout style sheets

Creating reports programmatically in MATLAB
    Template-Based Report Object (DOM) API Examples
        Functional report
        Object-oriented report
        Programmatic report formatting

    Creating report content
        Using the Document Object Model (DOM) API

    Report format basics
        Specifying format for report content

    Creating form-based reports
        Using the DOM API to fill in the blanks in a report form

    Creating object-oriented reports
        Deriving classes to simplify report creation and maintenance

    Creating and formatting report objects
        Lists, tables, and images

    Creating DOM Reports from HTML
        Appending HTML string or file to a Microsoft® Word, PDF, or HTML report generated by Document Object Model (DOM) API

    Creating report templates
        Creating templates to use with programmatic reports

    Formatting page layouts
        Formatting pages in Microsoft Word and PDF reports

Summary and closing remarks

pythonmultipurpose Advanced Python 28 hours

In this instructor-led training, participants will learn advanced Python programming techniques, including how to apply this versatile language to solve problems in areas such as distributed applications, finance, data analysis and visualization, UI programming and maintenance scripting.


  • Developers

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice


  • If you wish to add, remove or customize any section or topic within this course, please contact us to arrange.


    Python versatility: from data analysis to web crawling

Python data structures and operations
    Integers and floats
    Strings and bytes
    Tuples and lists
    Dictionaries and ordered dictionaries
    Sets and frozen sets
    Data frame (pandas)

Object-oriented programming with Python
    Static classes
    Static functions

Data Analysis with pandas
    Data cleaning
    Using vectorized data in pandas
    Data wrangling
    Sorting and filtering data
    Aggregate operations
    Analyzing time series

Data visualization
    Plotting diagrams with matplotlib
    Using matplotlib from within pandas
    Creating quality diagrams
    Visualizing data in Jupyter notebooks
    Other visualization libraries in Python

Vectorizing Data in Numpy
    Creating Numpy arrays
    Common operations on matrices
    Using ufuncs
    Views and broadcasting on Numpy arrays
    Optimizing performance by avoiding loops
    Optimizing performance with cProfile

Processing Big Data with Python
    Building and supporting distributed applications with Python
    Data storage: Working with SQL and NoSQL databases
    Distributed processing with Hadoop and Spark
    Scaling your applications

Python for finance
    Packages, libraries and APIs for financial processing
        Python APIs

Extending Python (and vice versa) with other languages

Python multi-threaded programming

UI programming with Python
    Framework options for building GUIs in Python

Python for maintenance scripting
    Raising and catching exceptions correctly
    Organizing code into modules and packages
    Understanding symbol tables and accessing them in code
    Picking a testing framework and applying TDD in Python

Python for the web
    Packages for web processing
    Web crawling
    Parsing HTML and XML
    Filling web forms automatically

Closing remarks

datavis1 Data Visualization 28 hours

This course is intended for engineers and decision makers working in data mining and knoweldge discovery.

You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers and help them to understand hidden information.

Day 1:

  • what is data visualization
  • why it is important
  • data visualization vs data mining
  • human cognition
  • HMI
  • common pitfalls

Day 2:

  • different type of curves
  • drill down curves
  • categorical data plotting
  • multi variable plots
  • data glyph and icon representation

Day 3:

  • plotting KPIs with data
  • R and X charts examples
  • what if dashboards
  • parallel axes mixing
  • categorical data with numeric data

Day 4:

  • different hats of data visualization
  • how can data visualization lie
  • disguised and hidden trends
  • a case study of student data
  • visual queries and region selection
datavisR1 Introduction to Data Visualization with R 28 hours

This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions


Day 1:

  • overview of R programming
  • introduction to data visualization
  • scatter plots and clusters
  • the use of noise and jitters

Day 2:

  • other type of 2D and 3D plots
  • histograms
  • heat charts
  • categorical data plotting

Day 3:

  • plotting KPIs with data
  • R and X charts examples
  • dashboards
  • parallel axes
  • mixing categorical data with numeric data

Day 4:

  • different hats of data visualization
  • disguised and hidden trends
  • case studies
  • saving plots and loading Excel files
neo4j Beyond the relational database: neo4j 21 hours

Relational, table-based databases such as Oracle and MySQL have long been the standard for organizing and storing data. However, the growing size and fluidity of data have made it difficult for these traditional systems to efficiently execute highly complex queries on the data. Imagine replacing rows-and-columns-based data storage with object-based data storage, whereby entities (e.g., a person) could be stored as data nodes, then easily queried on the basis of their vast, multi-linear relationship with other nodes. And imagine querying these connections and their associated objects and properties using a compact syntax, up to 20 times lighter than SQL. This is what graph databases, such as neo4j offer.

In this hands-on course, we will set up a live project and put into practice the skills to model, manage and access your data. We contrast and compare graph databases with SQL-based databases as well as other NoSQL databases and clarify when and where it makes sense to implement each within your infrastructure.


  • Database administrators (DBAs)
  • Data analysts
  • Developers
  • System Administrators
  • DevOps engineers
  • Business Analysts
  • CTOs
  • CIOs

Format of the course

  • Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development.


Getting started with neo4j

  • neo4j vs relational databases
  • neo4j vs other NoSQL databases
  • Using neo4j to solve real world problems
  • Installing neo4j

Data modeling with neo4j

  • Mapping white-board diagrams and mind maps to neo4j

Working with nodes

  • Creating, changing and deleting nodes
  • Defining node properties

Node relationships

  • Creating and deleting relationships
  • Bi-directional relationships

Querying your data with Cypher

  • Querying your data based on relationships
  • Setting indexes and constraints

Working with the REST API

  • REST operations on nodes
  • REST operations on relationships
  • REST operations on indexes and constraints

Accessing the core API for application development

  • Working with NET, Java, Javascript, and Python APIs

Closing remarks


kdd Knowledge Discover in Databases (KDD) 21 hours

Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing.

In this course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.

    Data analysts or anyone interested in learning how to interpret data to solve problems

Format of the course
    After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.

    KDD vs data mining

Establishing the application domain

Establishing relevant prior knowledge

Understanding the goal of the investigation

Creating a target data set

Data cleaning and preprocessing

Data reduction and projection

Choosing the data mining task

Choosing the data mining algorithms

Interpreting the mined patterns

OpenNN OpenNN: Implementing neural networks 14 hours

OpenNN is an open-source class library written in C++  which implements neural networks, for use in machine learning.

In this course we go over the principles of neural networks and use OpenNN to implement a sample application.

    Software developers and programmers wishing to create Deep Learning applications.

Format of the course
    Lecture and discussion coupled with hands-on exercises.

Introduction to OpenNN, Machine Learning and Deep Learning

Downloading OpenNN

Working with Neural Designer
    Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics

OpenNN architecture
    CPU parallelization

OpenNN classes
    Data set, neural network, loss index, training strategy, model selection, testing analysis
    Vector and matrix templates

Building a neural network application
    Choosing a suitable neural network
    Formulating the variational problem (loss index)
    Solving the reduced function optimization problem (training strategy)

Working with datasets
     The data matrix (columns as variables and rows as instances)

Learning tasks
    Function regression
    Pattern recognition

Compiling with QT Creator

Integrating, testing and debugging your application

The future of neural networks and OpenNN

druid Druid: Build a fast, real-time data analysis system 21 hours

Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo.

In this course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment.

    Application developers
    Software engineers
    Technical consultants
    DevOps professionals
    Architecture engineers

Format of the course
    Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding


Installing and starting Druid

Druid architecture and design

Real-time ingestion of event data

Sharding and indexing

Loading data

Querying data

Visualizing data

Running a distributed cluster

Druid + Apache Hive

Druid + Apache Kafka

Druid + others


Administrative tasks

nlpwithr NLP: Natural Language Processing with R 21 hours

It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

    Linguists and programmers

Format of the course
    Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

    NLP and R vs Python

Installing and configuring R Studio

Installing R packages related to Natural Language Processing (NLP).

An overview of R’s text manipulation capabilities

Getting started with an NLP project in R

Reading and importing data files into R

Text manipulation with R

Document clustering in R

Parts of speech tagging in R

Sentence parsing in R

Working with regular expressions in R

Named-entity recognition in R

Topic modeling in R

Text classification in R

Working with very large data sets

Visualizing your results


Integrating R with other languages (Java, Python, etc.)

Closing remarks

BigData_ A practical introduction to Data Analysis and Big Data 28 hours

Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.


  • Developers / programmers
  • IT consultants

Format of the course
    Part lecture, part discussion, heavy hands-on practice and implementation, occasional quizing to measure progress.

Introduction to Data Analysis and Big Data

  • What makes Big Data "big"?
    • Velocity, Volume, Variety, Veracity (VVVV)
  • Limits to traditional Data Processing
  • Distributed Processing
  • Statistical Analysis
  • Types of Machine Learning Analysis
  • Data Visualization

Languages used for Data Analysis

  • R language (crash course)
    • Why R for Data Analysis?
    • Data manipulation, calculation and graphical display
  • Python (crash course)
    • Why Python for Data Analysis?
    • Manipulating, processing, cleaning, and crunching data

Approaches to Data Analysis

  • Statistical Analysis
    • Time Series analysis
    • Forecasting with Correlation and Regression models
    • Inferential Statistics (estimating)
    • Descriptive Statistics in Big Data sets (e.g. calculating mean)
  • Machine Learning
    • Supervised vs unsupervised learning
    • Classification and clustering
    • Estimating cost of specific methods
    • Filtering
  • Natural Language Processing
    • Processing text
    • Understaing meaning of the text
    • Automatic text generation
    • Sentiment/Topic Analysis
  • Computer Vision
    • Acquiring, processing, analyzing, and understanding images
    • Reconstructing, interpreting and understanding 3D scenes
    • Using image data to make decisions

Big Data infrastructure

  • Data Storage
    • Relational databases (SQL)
      • MySQL
      • Postgres
      • Oracle
    • Non-relational databases (NoSQL)
      • Cassandra
      • MongoDB
      • Neo4js
    • Understanding the nuances
      • Hierarchical databases
      • Object-oriented databases
      • Document-oriented databases
      • Graph-oriented databases
      • Other
  • Distributed Processing
    • Hadoop
      • HDFS as a distributed filesystem
      • MapReduce for distributed processing
    • Spark
      • All-in-one in-memory cluster computing framework for large-scale data processing
      • Structured streaming
      • Spark SQL
      • Machine Learning libraries: MLlib
      • Graph processing with GraphX
  • Search Engines
    • ElasticSearch
    • Solr
  • Scalability
    • Public cloud
      • AWS, Google, Aliyun, etc.
    • Private cloud
      • OpenStack, Cloud Foundry, etc.
    • Auto-scalability
  • Choosing right solution for the problem
  • The future of Big Data
  • Closing remarks


octnp Octave nie tylko dla programistów 21 hours

Szkolenie dedykowane osobom, które chciałyby zapoznać się z obsługą programu alternatywnego do komercyjnego pakietu MATLAB. Kurs trzydniowy dostarcza kompleksowo informacje dotyczące poruszania się po środowisku i wykonywaniu pakietu OCTAVE w zastosowaniu do analizy danych i obliczeń inżynierskich. Adresatami szkolenia są osoby początkujące ale także ci, którzy znają program i chcieliby usystematyzować swoją wiedzę i podnieść umiejętności. Nie jest wymagana znajomość innych języków programowania ale w znacznym stopniu ułatwi to uczestnikom przyswajanie wiedzy. Na kursie pokazane zostaną możliwości wykorzystania program na wielu przykładach praktycznych.


Podstawowe obliczenia

  • Rozpoczęcie pracy z Octave, Octave jako kalkulator, funkcje wbudowane

Środowisko Octave

  • Zmienne, liczny I formatowanie, reprezentacje liczb I ich dokładność, zapisywanie i wczytywanie danych 

Tablice i wektory

  • Wyodrębnianie liczb z wektorów, arytmetyka wektorowa


  • Prezentacja danych na wykresie, przygotowanie wielu wykresów oraz wielu okien z wykresami, zapisywanie i drukowanie wykresów

Programowanie w Octave cz. I: Skrypty

  • Tworzenie i edycja skryptu, uruchamianie i debugowanie skryptów,

Instrukcje sterujące wykonywanie programu

  • If else, switch, for, while

Programowanie w Octave cz. II: Funkcje

Macierze i wektory

  • macierze, transpozycja, funkcje tworzące macierze, budowanie złożonych macierzy, macierz jako tablica, wyodrębnianie bitów z macierzy, podstawowe funkcje macierzowe

Równanie liniowe i nieliniowe

Więcej wykresów

  • Budowanie wielu wykresów na jednym rysunku, wykresy 3D, zmiana perspektywy, wykresy powierzchniowe, rysunki i filmy,

 Matematyka macierzowa

  • Wartości własne, dekompozycja

 Liczby zespolone

  • Wykresy liczb zespolonych

Statystyka i przetwarzanie danych

Budowanie graficznego interfejsu użytkownika

ORABI Wstęp do Oracle Business Intelligence i BI Publisher 35 hours

Oracle BI

  • Wprowadzenie do Oracle BI
  • Budowanie analiz
  • Budowanie widoków I wykresów
  • Tworzenie I modyfikacja pulpitów informacyjnych

BI Publisher

  • Budowanie modeli danych z wykorzystaniem Data Model Editor.
  • Budowanie raportów BI Publisher
  • Tworzenie szablonów raportowych w MS Office
  • Publikowanie raportów na pulpitach informacyjnych
  • Planowanie wykonania I dostarczania raportów

Other regions

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MS-40361 Software Development Fundamentals MTA Exam 98-361 Gdynia, ul. Ejsmonda 2 śr., 2017-10-11 09:00 6138PLN / 2610PLN
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PostgreSQL for Administrators Gdynia, ul. Ejsmonda 2 śr., 2017-10-11 09:00 12326PLN / 4235PLN
Agile w projektach zdalnych Katowice ul. Opolska 22 pon., 2017-10-16 09:00 5049PLN / 1962PLN
Programowanie w języku C++ Bielsko-Biała, Al. Armii Krajowej 220 pon., 2017-10-16 09:00 5445PLN / 3565PLN
Programowanie w języku C++ Łódź, ul. Tatrzańska 11 pon., 2017-10-16 09:00 5445PLN / 3315PLN
PostgreSQL Administration Łódź, ul. Tatrzańska 11 wt., 2017-10-17 09:00 7821PLN / 3807PLN
Programowanie w C# Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-10-18 09:00 4851PLN / 1870PLN
Trening radzenie sobie ze stresem Gdynia, ul. Ejsmonda 2 śr., 2017-10-18 09:00 5148PLN / 1530PLN
Business Analysis Kraków, ul. Rzemieślnicza 1 śr., 2017-10-18 09:00 7722PLN / 3774PLN
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Projektowanie stron na urządzenia mobilne Kielce, ul. Warszawska 19 czw., 2017-10-19 09:00 2624PLN / 1305PLN
Adobe InDesign Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2017-10-23 09:00 1881PLN / 1027PLN
Adobe Premiere Pro Gdynia, ul. Ejsmonda 2 pon., 2017-10-23 09:00 3960PLN / 2480PLN
Administracja systemu Linux Gdynia, ul. Ejsmonda 2 wt., 2017-10-24 09:00 4950PLN / 3225PLN
Node.js Olsztyn, ul. Kajki 3/1 czw., 2017-10-26 09:00 3861PLN / 2431PLN
Microsoft Office Excel - efektywna praca z arkuszem Warszawa, ul. Złota 3/11 czw., 2017-10-26 09:00 2475PLN / 1225PLN
SQL Advanced in MySQL Warszawa, ul. Złota 3/11 czw., 2017-11-02 09:00 1881PLN / 1141PLN
Microsoft Office Excel - analiza statystyczna Warszawa, ul. Złota 3/11 czw., 2017-11-02 09:00 2673PLN / 1291PLN
Android - Podstawy Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2017-11-06 09:00 9801PLN / 4180PLN
Java Spring Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2017-11-06 09:00 14414PLN / 5970PLN
Kontrola jakości i ciągła integracja Wrocław, ul.Ludwika Rydygiera 2a/22 wt., 2017-11-07 09:00 2673PLN / 1737PLN
Nagios Core Gdańsk, ul. Powstańców Warszawskich 45 pon., 2017-11-13 09:00 13919PLN / 4968PLN
Oracle 11g - Analiza danych - warsztaty Gdynia, ul. Ejsmonda 2 pon., 2017-11-13 09:00 9900PLN / 4664PLN
ADO.NET 4.0 Development Warszawa, ul. Złota 3/11 wt., 2017-11-14 09:00 20176PLN / 6914PLN

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