Szkolenia Data Visualization
Wizualizacja danych prezentuje dane w formacie graficznym. Szkolenia NobleProg na żywo na żywo Data Visualization demonstrują poprzez dyskusję i ćwiczą umiejętności, strategie, narzędzia i podejścia do wizualizacji i raportowania danych dla różnych odbiorców. Studia przypadków są również analizowane i omawiane, aby pokazać, w jaki sposób rozwiązania wizualizacji danych są stosowane w świecie rzeczywistym, aby wyprowadzić znaczenie z danych i odpowiedzieć na kluczowe pytania. Szkolenie z zakresu wizualizacji danych jest dostępne w różnych formatach, w tym szkolenia na miejscu na żywo i szkolenia instruktorskie prowadzone na żywo. interaktywna, zdalna konfiguracja pulpitu. Szkolenia z lokalnej wizualizacji danych mogą być przeprowadzane na żywo w siedzibie klienta lub w lokalnych centrach szkoleniowych NobleProg.
Opinie uczestników
Trener przyniósł dobry wgląd i sposoby podejścia do opracowania bazy danych wykresów. Używał przykładów ze slajdów przedstawionych, ale także czerpał z własnego doświadczenia, które było dobre.
Autodata Ltd
Nazwa kursu: Beyond the relational database: neo4j
Translated by
The trainer did bring some good insight and ways to approach developing a graph database. He used examples from the slides presented but also drew on his own experience which was good.
Autodata Ltd
Trener przyniósł dobry wgląd i sposoby podejścia do opracowania bazy danych wykresów. Używał przykładów ze slajdów przedstawionych, ale także czerpał z własnego doświadczenia, które było dobre.
Autodata Ltd
Nazwa kursu: Beyond the relational database: neo4j
Translated by
The trainer did bring some good insight and ways to approach developing a graph database. He used examples from the slides presented but also drew on his own experience which was good.
Autodata Ltd
Trener przyniósł dobry wgląd i sposoby podejścia do opracowania bazy danych wykresów. Używał przykładów ze slajdów przedstawionych, ale także czerpał z własnego doświadczenia, które było dobre.
Autodata Ltd
Nazwa kursu: Beyond the relational database: neo4j
Translated by
The trainer did bring some good insight and ways to approach developing a graph database. He used examples from the slides presented but also drew on his own experience which was good.
Autodata Ltd
Elastyczność w dopasowaniu do szczegółów związanych z Autodata, aby uzyskać więcej scenariusza z prawdziwego świata.
Autodata Ltd
Nazwa kursu: Beyond the relational database: neo4j
Translated by
Flexibility to blend in with Autodata related details to get more of a real world scenario as we went on.
Autodata Ltd
Elastyczność w dopasowaniu do szczegółów związanych z Autodata, aby uzyskać więcej scenariusza z prawdziwego świata.
Autodata Ltd
Nazwa kursu: Beyond the relational database: neo4j
Translated by
Flexibility to blend in with Autodata related details to get more of a real world scenario as we went on.
Autodata Ltd
Ogólnie zawartość była dobra.
Sameer Rohadia Continental AG / Abteilung: CF IT Finance
Nazwa kursu: A practical introduction to Data Analysis and Big Data
Translated by
Overall the Content was good.
Sameer Rohadia - Continental AG / Abteilung: CF IT Finance
prezentacja technologii
Continental AG / Abteilung: CF IT Finance
Nazwa kursu: A practical introduction to Data Analysis and Big Data
Translated by
presentation of technologies
Continental AG / Abteilung: CF IT Finance
Obejmował szeroki zakres informacji.
Continental AG / Abteilung: CF IT Finance
Nazwa kursu: A practical introduction to Data Analysis and Big Data
Translated by
It covered a broad range of information.
Continental AG / Abteilung: CF IT Finance
Gotowość do dzielenia się więcej
Balaram Chandra Paul MOL Information Technology Asia Limited
Nazwa kursu: A practical introduction to Data Analysis and Big Data
Translated by
Willingness to share more
Balaram Chandra Paul - MOL Information Technology Asia Limited
Dobre przykłady z życia wzięte, przeglądy istniejących raportów
Ronald Parrish Virginia Departament Edukacji
Nazwa kursu: Data Visualization
Translated by
Good real world examples, reviews of existing reports
Ronald Parrish - Virginia Department of Education
Przykłady.
Peter Coleman Virginia Department of Education
Nazwa kursu: Data Visualization
Translated by
The examples.
peter coleman - Virginia Department of Education
Przykłady.
Peter Coleman Virginia Department of Education
Nazwa kursu: Data Visualization
Translated by
The examples.
peter coleman - Virginia Department of Education
Jestem uczącym się handlem i to było coś, co zrobił bardzo dużo.
Lisa Comfort Virginia Department of Education
Nazwa kursu: Data Visualization
Translated by
I am a hands-on learner and this was something that he did a lot of.
Lisa Comfort - Virginia Department of Education
Treść / Instruktor
Craig Roberson Virginia Departament Edukacji
Nazwa kursu: Data Visualization
Translated by
Content / Instructor
Craig Roberson - Virginia Department of Education
Trener był entuzjastyczny.
Diane Lucas Virginia Departament Edukacji
Nazwa kursu: Data Visualization
Translated by
Trainer was enthusiastic.
Diane Lucas - Virginia Department of Education
Poznanie wszystkich typów wykresów i ich zastosowania. Nauka wartości declutteringu. Poznanie metod pokazywania danych o czasie.
Susan Williams Virginia Department of Education
Nazwa kursu: Data Visualization
Translated by
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
Naprawdę doceniam to, że Jeff wykorzystał dane i przykłady, które miały zastosowanie do danych edukacyjnych. Sprawił, że było to interesujące i interaktywne.
Carol Wells Bazzichi Virginia Departament Edukacji
Nazwa kursu: Data Visualization
Translated by
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
Myślałem, że informacje są interesujące.
Allison May Virginia Department of Education
Nazwa kursu: Data Visualization
Translated by
I thought that the information was interesting.
Allison May - Virginia Department of Education
Plany Szkoleń Data Visualization
| Kod | Nazwa | Czas trwania | Charakterystyka kursu |
|---|---|---|---|
| d3js | D3.js for Data Visualization | 7 godz. | D3.js (or D3 for Data-Driven Documents) is a JavaScript library that uses SVG, HTML5, and CSS for producing dynamic, interactive data visualizations in web browsers. In this instructor-led, live training, participants will learn how to create web-based data-driven visualizations that run on multiple devices responsively. By the end of this training, participants will be able to: Use D3 to create interactive graphics, information dashboards, infographics and maps Control HTML with jQuery-like selections Transform the DOM by selecting elements and joining to data Export SVG for use in print publications Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| highcharts | Highcharts for Data Visualization | 7 godz. | Highcharts is an open-source JavaScript library for creating interactive graphical charts on the Web. It is commonly used to represent data in a more user-readable and interactive fashion. In this instructor-led, live training, participants will learn how to create high-quality data visualizations for web applications using Highcharts. By the end of this training, participants will be able to: Set up interactive charts on the Web using only HTML and JavaScript Represent large datasets in visually interesting and interactive ways Export charts to JPEG, PNG, SVG, or PDF Integrate Highcharts with jQuery Mobile for cross-platform compatibility Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonmultipurpose | Advanced Python | 28 godz. | 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, data analysis and visualization, UI programming and maintenance scripting. Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Notes If you wish to add, remove or customize any section or topic within this course, please contact us to arrange. |
| datavisualizationreports | Data Visualization: Creating Captivating Reports | 21 godz. | In this instructor-led, live training, participants will learn the skills, strategies, tools and approaches for visualizing and reporting data for different audiences. Case studies are also analyzed and discussed to exemplify how data visualization solutions are being applied in the real world to derive meaning out of data and answer crucial questions. By the end of this training, participants will be able to: Write reports with captivating titles, subtitles, and annotations using the most suitable highlighting, alignment, and color schemes for readability and user friendliness. Design charts that fit the audience's information needs and interests Choose the best chart types for a given dataset (beyond pie charts and bar charts) Identify and analyze the most valuable and relevant data quickly and efficiently Select the best file formats to include in reports (graphs, infographics, references, GIFs, etc.) Create effective layouts for displaying time series data, part-to-whole relationships, geographic patterns, and nested data Use effective color-coding to display qualitative and text-based data such as sentiment analysis, timelines, calendars, and diagrams Apply the most suitable tools for the job (Excel, R, Tableau, mapping programs, etc.) Prepare datasets for visualization Audience Data analysts Business managers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| nlpwithr | NLP: Natural Language Processing 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. 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. Audience Linguists and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| embeddingprojector | Embedding Projector: Visualizing your Training Data | 14 godz. | Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project. By the end of this training, participants will be able to: Explore how data is being interpreted by machine learning models Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals. Explore the properties of a specific embedding to understand the behavior of a model Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| BigData_ | A Practical Introduction to Data Analysis and Big Data | 35 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 and infrastructure that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress. |
| deckgl | deck.gl: Visualizing Large-scale Geospatial Data | 14 godz. | deck.gl is an open-source, WebGL-powered library for exploring and visualizing data assets at scale. Created by Uber, it is especially useful for gaining insights from geospatial data sources, such as data on maps. This instructor-led, live training introduces the concepts and functionality behind deck.gl and walks participants through the set up of a demonstration project. By the end of this training, participants will be able to: Take data from very large collections and turn it into compelling visual representations Visualize data collected from transportation and journey-related use cases, such as pick-up and drop-off experiences, network traffic, etc. Apply layering techniques to geospatial data to depict changes in data over time Integrate deck.gl with React (for Reactive programming) and Mapbox GL (for visualizations on Mapbox based maps). Understand and explore other use cases for deck.gl, including visualizing points collected from a 3D indoor scan, visualizing machine learning models in order to optimize their algorithms, etc. Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| zeppelin | Zeppelin for interactive data analytics | 14 godz. | Apache Zeppelin is a web-based notebook for capturing, exploring, visualizing and sharing Hadoop and Spark based data. This instructor-led, live training introduces the concepts behind interactive data analytics and walks participants through the deployment and usage of Zeppelin in a single-user or multi-user environment. By the end of this training, participants will be able to: Install and configure Zeppelin Develop, organize, execute and share data in a browser-based interface Visualize results without referring to the command line or cluster details Execute and collaborate on long workflows Work with any of a number of plug-in language/data-processing-backends, such as Scala ( with Apache Spark ), Python ( with Apache Spark ), Spark SQL, JDBC, Markdown and Shell. Integrate Zeppelin with Spark, Flink and Map Reduce Secure multi-user instances of Zeppelin with Apache Shiro Audience Data engineers Data analysts Data scientists Software developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| datameer | Datameer for Data Analysts | 14 godz. | Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion. In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources. By the end of this training, participants will be able to: Create, curate, and interactively explore an enterprise data lake Access business intelligence data warehouses, transactional databases and other analytic stores Use a spreadsheet user-interface to design end-to-end data processing pipelines Access pre-built functions to explore complex data relationships Use drag-and-drop wizards to visualize data and create dashboards Use tables, charts, graphs, and maps to analyze query results Audience Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| fsharpfordatascience | F# for Data Science | 21 godz. | Data science is the application of statistical analysis, machine learning, data visualization and programming for the purpose of understanding and interpreting real-world data. F# is a well suited programming language for data science as it combines efficient execution, REPL-scripting, powerful libraries and scalable data integration. In this instructor-led, live training, participants will learn how to use F# to solve a series of real-world data science problems. By the end of this training, participants will be able to: Use F#'s integrated data science packages Use F# to interoperate with other languages and platforms, including Excel, R, Matlab, and Python Use the Deedle package to solve time series problems Carry out advanced analysis with minimal lines of production-quality code Understand how functional programming is a natural fit for scientific and big data computations Access and visualize data with F# Apply F# for machine learning Explore solutions for problems in domains such as business intelligence and social gaming Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| scilab | Scilab | 14 godz. | 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. Audience 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 |
| tidyverse | Introduction to Data Visualization with Tidyverse and R | 7 godz. | The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble. In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse. By the end of this training, participants will be able to: Perform data analysis and create appealing visualizations Draw useful conclusions from various datasets of sample data Filter, sort and summarize data to answer exploratory questions Turn processed data into informative line plots, bar plots, histograms Import and filter data from diverse data sources, including Excel, CSV, and SPSS files Audience Beginners to the R language Beginners to data analysis and data visualization Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| ORABI | Wstęp do Oracle Business Intelligence i BI Publisher | 35 godz. | |
| echarts | ECharts | 14 godz. | eCharts is a free JavaScript library used for interactive charting and data visualization. In this instructor-led, live training, participants will learn the fundamental functionalities of ECharts as they step through the process of creating and configuring charts using ECharts. By the end of this training, participants will be able to: Understand the fundamentals of ECharts Explore and utilize the various features and configuration options in ECharts Build their own simple, interactive, and responsive charts with ECharts Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| powerbiforbiandanalytics | Power BI for Business Analysts | 21 godz. | Microsoft Power BI is a free Software as a Service (SaaS) suite for analyzing data and sharing insights. Power BI dashboards provide a 360-degree view of the most important metrics in one place, updated in real time, and available on all of their devices. In this instructor-led, live training, participants will learn how to use Microsoft Power Bi to analyze and visualize data using a series of sample data sets. By the end of this training, participants will be able to: Create visually compelling dashboards that provide valuable insights into data Obtain and integrate data from multiple data sources Build and share visualizations with team members Adjust data with Power BI Desktop Audience Business managers Business analystss Data analysts Business Intelligence (BI) and Data Warehouse (DW) teams Report developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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 |
| 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. |
| 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. |
| neo4j | Beyond the relational database: neo4j | 21 godz. | 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. Audience 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. |
| 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 |
| 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. |
Najbliższe szkolenia
| Szkolenie | Data Kursu | Cena szkolenia [Zdalne / Stacjonarne] |
|---|---|---|
| Data Visualization - Częstochowa, ul. Wały Dwernickiego 117/121 | pon., 2018-05-07 09:00 | 6650PLN / 7900PLN |
| A Practical Introduction to Data Analysis and Big Data - Wrocław, ul.Ludwika Rydygiera 2a/22 | pon., 2018-05-07 09:00 | 8320PLN / 9820PLN |
| Knowledge Discover in Databases (KDD) - Opole, Władysława Reymonta 29 | pon., 2018-05-07 09:00 | 4990PLN / 5990PLN |
| OpenNN: Implementing neural networks - Olsztyn, ul. Gietkowska 6a | pon., 2018-05-07 09:00 | 3330PLN / 4080PLN |
| deck.gl: Visualizing Large-scale Geospatial Data - Rzeszów, Plac Wolności 13 | wt., 2018-05-08 09:00 | 1990PLN / 2740PLN |