Szkolenia Data Visualization

Plany Szkoleń Data Visualization

Kod Nazwa Czas trwania Charakterystyka kursu
datavis1 Data Visualization 28 godz. 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 godz. 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 godz. Audience Database administrators (DBAs) Data analysts Developers System Administrators DevOps engineers Business Analysts CTOs CIOs Format of the course 30% lectures 60% hands-on exercises 10% tests   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 existing infrastructure. 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 MATCH, RETURN, WHERE, REMOVE, MERGE, etc. 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, Python APIs Closing remarks  
kdd Knowledge Discover in Databases (KDD) 21 godz. 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. Audience     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. Introduction     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 godz. 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. Audience     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 godz. 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. Audience     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 Introduction 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 Troubleshooting Administrative tasks
nlpwithr Natural Language Processing (NLP) with R 21 godz. 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. By the end of the class 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. Audience     Linguists and programmers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding Introduction     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 Optimization Integrating R with other languages (Java, Python, etc.) Closing remarks
BigData_ A practical introduction to Data Analysis and Big Data 28 godz. 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 that enable Big Data storage, Distributed Processing, and Scalability. Audience 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 Distributed Processing MapReduce Languages used for Data Analysis R language (crash course) Python (crash course) 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 Filter Natural Language Processing Processing text Understaing meaning of the text Automatic text generation Sentiment/Topic Analysis Computer Vision Big Data infrastructure Data Storage SQL (relational database) MySQL Postgres Oracle NoSQL Cassandra MongoDB Neo4js Understanding the nuances: hierarchical, object-oriented, document-oriented, graph-oriented, etc. Distributed File Systems HDFS Search Engines ElasticSearch Distributed Processing Spark Machine Learning libraries: MLlib Spark SQL Scalability Public cloud AWS, Google, Aliyun, etc. Private cloud OpenStack, Cloud Foundry, etc. Auto-scalability Choosing right solution for the problem  
octnp Octave nie tylko dla programistów 21 godz. 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. Wstęp 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 Wykresy 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

Najbliższe szkolenia

SzkolenieData KursuCena szkolenia [Zdalne / Stacjonarne]
Data Visualization - Szczecin, ul. Małopolska 23pon., 2017-06-19 09:0035820PLN / 11855PLN
Introduction to Data Visualization with R - Poznań, Garbary 100/63pon., 2017-06-19 09:0035850PLN / 11664PLN

Other regions

Szkolenie Data Visualization, Data Visualization boot camp, Szkolenia Zdalne Data Visualization, szkolenie wieczorowe Data Visualization, szkolenie weekendowe Data Visualization , nauka przez internet Data Visualization, edukacja zdalna Data Visualization, wykładowca Data Visualization , Trener Data Visualization,Kurs Data Visualization, instruktor Data Visualization, kurs zdalny Data Visualization,Kursy Data Visualization, lekcje UML, nauczanie wirtualne Data Visualization, e-learning Data Visualization

Kursy w promocyjnej cenie

Szkolenie Miejscowość Data Kursu Cena szkolenia [Zdalne / Stacjonarne]
Programowanie w języku Python Szczecin, ul. Sienna 9 pon., 2017-05-29 09:00 10000PLN / 4448PLN
Facebook w marketingu i reklamie Lublin, ul. Spadochroniarzy 9 pt., 2017-06-02 09:00 1881PLN / 1002PLN
MongoDB for Administrators Kraków, ul. Rzemieślnicza 1 wt., 2017-06-06 09:00 3861PLN / 2087PLN
Oracle 11g - Programowanie w PL/SQL I - warsztaty Wrocław, ul.Ludwika Rydygiera 2a/22 wt., 2017-06-06 09:00 5990PLN / 2939PLN
Adobe Photoshop Elements Gdynia, ul. Ejsmonda 2 śr., 2017-06-07 09:00 1881PLN / 1127PLN
Microsoft Office Excel - moduł Business Intelligence Gdynia, ul. Ejsmonda 2 śr., 2017-06-07 09:00 2673PLN / 1391PLN
Adobe Photoshop Elements Gdańsk, ul. Powstańców Warszawskich 45 śr., 2017-06-07 09:00 1881PLN / 1127PLN
Adobe InDesign Poznań, Garbary 100/63 czw., 2017-06-08 09:00 1881PLN / 1027PLN
Wzorce projektowe w C# Poznań, Garbary 100/63 czw., 2017-06-08 09:00 3861PLN / 1830PLN
SQL Fundamentals Gdańsk, ul. Powstańców Warszawskich 45 czw., 2017-06-08 09:00 3663PLN / 1610PLN
Visual Basic for Applications (VBA) w Excel - poziom zaawansowany Warszawa, ul. Złota 3/11 pon., 2017-06-12 09:00 3069PLN / 1623PLN
Visual Basic for Applications (VBA) w Excel - wstęp do programowania Gdynia, ul. Ejsmonda 2 pon., 2017-06-12 09:00 3564PLN / 1891PLN
Techniki DTP (InDesign, Photoshop, Illustrator, Acrobat) Opole, Władysława Reymonta 29 pon., 2017-06-12 09:00 5940PLN / 4230PLN
Spring i Hibernate w tworzeniu aplikacji w języku Java Poznań, Garbary 100/63 wt., 2017-06-13 09:00 7722PLN / 3358PLN
Drools Rules Administration Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-06-14 09:00 21196PLN / 7023PLN
Adobe LiveCycle Designer Poznań, Garbary 100/63 pon., 2017-06-19 09:00 2970PLN / 1885PLN
Build applications with Oracle Application Express (APEX) Katowice ul. Opolska 22 pon., 2017-06-19 09:00 9801PLN / 4720PLN
Front End Developer Rzeszów, Plac Wolności 13 pon., 2017-06-19 09:00 23000PLN / 7970PLN
Tworzenie i zarządzanie stronami WWW Poznań, Garbary 100/63 pon., 2017-06-19 09:00 5841PLN / 2298PLN
Introduction to Selenium Warszawa, ul. Złota 3/11 czw., 2017-06-22 09:00 1871PLN / 824PLN
Javascript And Ajax Rzeszów, Plac Wolności 13 pon., 2017-06-26 09:00 5841PLN / 3655PLN
Wprowadzenie do programowania Gdańsk, ul. Powstańców Warszawskich 45 pon., 2017-06-26 09:00 5742PLN / 4121PLN
Implementation and Administration of Elasticsearch Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-06-28 09:00 20800PLN / 6903PLN
Efektywna komunikacja interpersonalna z elementami asertywności Wrocław, ul.Ludwika Rydygiera 2a/22 czw., 2017-06-29 09:00 5148PLN / 1430PLN
Elasticsearch Advanced Administration, Monitoring and Maintenance Gdańsk, ul. Powstańców Warszawskich 45 wt., 2017-07-04 09:00 17741PLN / 5876PLN
Nginx konfiguracja i Administracja Bydgoszcz, ul. Dworcowa 94 śr., 2017-07-05 09:00 6930PLN / 2850PLN
SQL Fundamentals Warszawa, ul. Złota 3/11 pon., 2017-07-10 09:00 3663PLN / 1510PLN
Protokół SIP w VoIP Poznań, Garbary 100/63 pon., 2017-07-17 09:00 15929PLN / 5427PLN
Visual Basic for Applications (VBA) w Excel - wprowadzenie Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-08-02 09:00 2376PLN / 1192PLN
Programowanie w WPF 4.5 Lublin, ul. Spadochroniarzy 9 śr., 2017-08-16 09:00 6435PLN / 2443PLN
Tworzenie i zarządzanie stronami WWW Poznań, Garbary 100/63 pon., 2017-09-25 09:00 5841PLN / 2298PLN

Newsletter z promocjami

Zapisz się na nasz newsletter i otrzymuj informacje o aktualnych zniżkach na kursy otwarte.
Szanujemy Twoją prywatność, dlatego Twój e-mail będzie wykorzystywany jedynie w celu wysyłki naszego newslettera, nie będzie udostępniony ani sprzedany osobom trzecim.
W dowolnej chwili możesz zmienić swoje preferencje co do otrzymywanego newslettera bądź całkowicie się z niego wypisać.

Zaufali nam