Szkolenia Data Visualization w wielkopolskie

Data Visualization Course Events - wielkopolskie

Kod Nazwa Miejscowość Czas trwania Data Kursu PHP Cena szkolenia [Zdalne / Stacjonarne]
datavis1 Data Visualization Poznań, Garbary 100/63 28 hours pon., 2017-05-29 09:00 35820PLN / 11655PLN
datavisR1 Introduction to Data Visualization with R Poznań, Garbary 100/63 28 hours pon., 2017-06-19 09:00 35850PLN / 11664PLN
datavis1 Data Visualization Poznań, Garbary 100/63 28 hours pon., 2017-07-31 09:00 35820PLN / 11655PLN
datavisR1 Introduction to Data Visualization with R Poznań, Garbary 100/63 28 hours pon., 2017-08-21 09:00 35850PLN / 11664PLN
datavis1 Data Visualization Poznań, Garbary 100/63 28 hours pon., 2017-09-25 09:00 35820PLN / 11655PLN
datavisR1 Introduction to Data Visualization with R Poznań, Garbary 100/63 28 hours pon., 2017-10-16 09:00 35850PLN / 11664PLN

Plany Kursów

Kod Nazwa Czas trwania Spis treści
datavis1 Data Visualization 28 hours

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

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

Day 1:

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

Day 2:

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

Day 3:

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

Day 4:

  • different hats of data visualization
  • how can data visualization lie
  • disguised and hidden trends
  • a case study of student data
  • visual queries and region selection
neo4j Beyond the relational database: neo4j 21 hours

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 hours

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

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

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

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

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

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

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 hours

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

This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data.

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

OpenNN OpenNN: Implementing neural networks 14 hours

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

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

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

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

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

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

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

 

datavisR1 Introduction to Data Visualization with R 28 hours

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

 

Day 1:

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

Day 2:

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

Day 3:

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

Day 4:

  • different hats of data visualization
  • disguised and hidden trends
  • case studies
  • saving plots and loading Excel files

Other regions

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