Szkolenia Data Visualization

Testi...Client Testimonials

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

Plany Szkoleń Data Visualization

Kod Nazwa Czas trwania Charakterystyka kursu
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 Introduction     Tydyverse vs traditional R plotting Setting up your working environment Preparing the dataset Importing and filtering data Wrangling the data Visualizing the data (graphs, scatter plots) Grouping and summarizing the data Visualizing the data (line plots, bar plots, histograms, boxplots) Working with non-standard data Closing remarks
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. 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 Why R for Data Analysis? Data manipulation, calculation and graphical display Python 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 analysis / 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 Scalability Public cloud AWS, Google, Aliyun, etc. Private cloud OpenStack, Cloud Foundry, etc. Auto-scalability Choosing the right solution for the problem The future of Big Data Closing remarks
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
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 To request a customized course outline for this training, please contact us.
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
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 To request a customized course outline for this training, please contact us.  
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   Introduction Data Visualization Authoring in Power BI Desktop Creating reports Interacting with reports Uploading reports it to the Power BI Service Revising report layouts Publishing to PowerBI.com Sharing and collaborating with team members Data Modeling Aquiring data Modeling data Security Working with DAX Refreshing the source data Securing data Advanced querying and data modeling Data modeling principals Complex DAX patterns Power BI tips and tricks Closing remarks
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, finance, 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.   Introduction     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)     Conversions Object-oriented programming with Python     Inheritance     Polymorphism     Static classes     Static functions     Decorators     Other 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         Zipline         PyAlgoTrade         Pybacktest         quantlib         Python APIs Extending Python (and vice versa) with other languages     C#     Java     C++     Perl     Others Python multi-threaded programming     Modules     Synchronizing     Prioritizing UI programming with Python     Framework options for building GUIs in Python         Tkinter         Pyqt 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
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
matlabdsandreporting MATLAB Fundamentals, Data Science & Report Generation 126 godz. 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. Note Practice sessions will based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange Introduction MATLAB for data science and reporting   Part 01: MATLAB fundamentals Overview     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 Overview     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 Overview     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
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
ORABI Wstęp do Oracle Business Intelligence i BI Publisher 35 godz. 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
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 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
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.   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, and Python APIs Closing remarks  
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
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 To request a customized course outline for this training, please contact us.
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 To request a customized course outline for this training, please contact us.
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 Introduction to data visualization Selecting and creating effective reports Data visualization tools and resources Generating and revising your visualizations Closing remarks
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 To request a customized course outline for this training, please contact us.

Najbliższe szkolenia

SzkolenieData KursuCena szkolenia [Zdalne / Stacjonarne]
Octave not only for programmers - Toruń, ul. Żeglarska 10/14pon., 2017-12-11 09:007000PLN / 2650PLN
Data Visualization - Bydgoszcz, ul. Dworcowa 94wt., 2017-12-12 09:0035820PLN / 11855PLN

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Kursy w promocyjnej cenie

Szkolenie Miejscowość Data Kursu Cena szkolenia [Zdalne / Stacjonarne]
Data Analytics With R Warszawa, ul. Złota 3/11 wt., 2017-11-28 09:00 14850PLN / 5550PLN
Programowanie w ASP.NET MVC 5 Wrocław, ul.Ludwika Rydygiera 2a/22 wt., 2017-11-28 09:00 5841PLN / 2223PLN
Visual Basic for Applications (VBA) w Excel - wstęp do programowania Kraków, ul. Rzemieślnicza 1 pon., 2017-12-04 09:00 3564PLN / 2491PLN
Wprowadzenie do C# 6.0 w Visual Studio 2015/2017 Gdańsk, ul. Powstańców Warszawskich 45 pon., 2017-12-04 09:00 9890PLN / 3473PLN
Docker - zarządzanie kontenerami Warszawa, ul. Złota 3/11 wt., 2017-12-05 09:00 8910PLN / 3570PLN
Test Automation with Selenium and Jenkins Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-12-06 09:00 7722PLN / 3174PLN
Adobe Premiere Pro Gdańsk, ul. Powstańców Warszawskich 45 czw., 2017-12-07 09:00 3960PLN / 2480PLN
Scrum - Step by Step Kraków, ul. Rzemieślnicza 1 czw., 2017-12-07 09:00 4950PLN / 2450PLN
Certyfikacja OCUP2 UML 2.5 - Przygotowanie do egzaminu OCUP2 Foundation Warszawa, ul. Złota 3/11 śr., 2017-12-13 09:00 6930PLN / 2910PLN
Nagios Core Gdańsk, ul. Powstańców Warszawskich 45 śr., 2017-12-13 09:00 13919PLN / 4968PLN
Język SQL w bazie danych Oracle Kraków, ul. Rzemieślnicza 1 czw., 2017-12-14 09:00 2772PLN / 1493PLN
Continuous integration with Jenkin Wrocław, ul.Ludwika Rydygiera 2a/22 czw., 2017-12-14 09:00 14117PLN / 4678PLN
Adobe Illustrator Lublin, ul. Spadochroniarzy 9 czw., 2017-12-14 09:00 2871PLN / 1648PLN
Statystyka dla Naukowców Toruń, ul. Żeglarska 10/14 pon., 2017-12-18 09:00 9207PLN / 4675PLN
Oracle SQL dla początkujących Gdańsk, ul. Powstańców Warszawskich 45 pon., 2017-12-18 09:00 4752PLN / 2283PLN
Język SQL w bazie danych MySQL Poznań, Garbary 100/63 pon., 2017-12-18 09:00 2851PLN / 1113PLN
Zarządzanie konfliktem Szczecin, ul. Sienna 9 pon., 2017-12-18 09:00 5148PLN / 1530PLN
Tableau Advanced Gdynia, ul. Ejsmonda 2 pon., 2017-12-18 09:00 7425PLN / 2975PLN
Komunikacja interpersonalna Szczecin, ul. Sienna 9 pon., 2017-12-18 09:00 5148PLN / 1530PLN
SQL Advanced level for Analysts Gdynia, ul. Ejsmonda 2 wt., 2017-12-19 09:00 3861PLN / 1920PLN
Introduction to Selenium Poznań, Garbary 100/63 śr., 2017-12-20 09:00 1871PLN / 824PLN
Adobe Photoshop Warszawa, ul. Złota 3/11 śr., 2017-12-20 09:00 1881PLN / 1152PLN
Predictive Modelling with R Warszawa, ul. Złota 3/11 śr., 2017-12-27 09:00 8524PLN / 2983PLN
Oracle SQL dla początkujących Rzeszów, Plac Wolności 13 śr., 2017-12-27 09:00 4752PLN / 2133PLN
Wprowadzenie do C# 6.0 w Visual Studio 2015/2017 Wrocław, ul.Ludwika Rydygiera 2a/22 wt., 2018-01-02 09:00 9890PLN / 3273PLN
Leadership 101 Wrocław, ul.Ludwika Rydygiera 2a/22 wt., 2018-01-09 09:00 3890PLN / 1379PLN
Agile Software Testing Szczecin, ul. Sienna 9 czw., 2018-01-11 09:00 4257PLN / 2629PLN
Agile TDD Katowice ul. Opolska 22 czw., 2018-01-11 09:00 2970PLN / 1835PLN
Analiza biznesowa i systemowa z użyciem notacji UML - warsztat praktyczny dla PO w metodyce Scrum Katowice ul. Opolska 22 śr., 2018-01-17 09:00 7722PLN / 3624PLN
Adobe Creative Cloud - Montaż video Katowice ul. Opolska 22 pon., 2018-01-29 09:00 3861PLN / 2455PLN
Techniki DTP (InDesign, Photoshop, Illustrator, Acrobat) Opole, Władysława Reymonta 29 pon., 2018-02-05 09:00 5940PLN / 4230PLN
Javascript Basics Poznań, Garbary 100/63 wt., 2018-02-13 09:00 4455PLN / 1885PLN
Certified Agile Tester Katowice ul. Opolska 22 pon., 2018-04-02 09:00 8910PLN / 4720PLN
Perfect tester Szczecin, ul. Sienna 9 śr., 2018-04-04 09:00 5920PLN / 2294PLN
Kontrola jakości i ciągła integracja Katowice ul. Opolska 22 czw., 2018-04-12 09:00 2673PLN / 2037PLN

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