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Allison May - Virginia Department of Education
Szkolenia Data Visualization
Opinie uczestników
Plany Szkoleń Data Visualization
| Kod | Nazwa | Czas trwania | Charakterystyka kursu |
|---|---|---|---|
| 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 |
| 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 |
| 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. | 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 |
| 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 | 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 |
| 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 and infrastructure 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 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 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 |
| 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 |
| 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 Introduction 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 |
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