Szkolenia Predictive Analytics

Szkolenia Predictive Analytics

Predictive Analytics courses

Opinie uczestników

Predictive Modelling with R

He was very informative and helpful.

Pratheep Ravy - UPC Schweiz GmbH

From Data to Decision with Big Data and Predictive Analytics

zakres materialu

Maciej Jonczyk - Orange Polska

From Data to Decision with Big Data and Predictive Analytics

usystematyzowanie wiedzy z dziedziny ML

- Orange Polska

Applied Machine Learning

ref material to use later was very good

PAUL BEALES - Seagate Technology

Plany Szkoleń Predictive Analytics

Kod Nazwa Czas trwania Charakterystyka kursu
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
datamodeling Pattern Recognition 35 godz. This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience     Data analysts     PhD students, researchers and practitioners   Introduction Probability theory, model selection, decision and information theory Probability distributions Linear models for regression and classification Neural networks Kernel methods Sparse kernel machines Graphical models Mixture models and EM Approximate inference Sampling methods Continuous latent variables Sequential data Combining models  
Piwik Getting started with Piwik 21 godz. Web analysist Data analysists Market researchers Marketing and sales professionals System administrators Format of course     Part lecture, part discussion, heavy hands-on practice Introduction to Piwik Why use Piwik? Piwik vs Google Analystics Setting up Piwik Selecting which websites to monitor Working with the dashboard Understanding visitor activity Actions Referrals Generating reports  
intror Introduction to R with Time Series Analysis 21 godz. Introduction and preliminaries Making R more friendly, R and available GUIs Rstudio Related software and documentation R and statistics Using R interactively An introductory session Getting help with functions and features R commands, case sensitivity, etc. Recall and correction of previous commands Executing commands from or diverting output to a file Data permanency and removing objects Simple manipulations; numbers and vectors Vectors and assignment Vector arithmetic Generating regular sequences Logical vectors Missing values Character vectors Index vectors; selecting and modifying subsets of a data set Other types of objects Objects, their modes and attributes Intrinsic attributes: mode and length Changing the length of an object Getting and setting attributes The class of an object Arrays and matrices Arrays Array indexing. Subsections of an array Index matrices The array() function The outer product of two arrays Generalized transpose of an array Matrix facilities Matrix multiplication Linear equations and inversion Eigenvalues and eigenvectors Singular value decomposition and determinants Least squares fitting and the QR decomposition Forming partitioned matrices, cbind() and rbind() The concatenation function, (), with arrays Frequency tables from factors Lists and data frames Lists Constructing and modifying lists Concatenating lists Data frames Making data frames attach() and detach() Working with data frames Attaching arbitrary lists Managing the search path Data manipulation Selecting, subsetting observations and variables           Filtering, grouping Recoding, transformations Aggregation, combining data sets Character manipulation, stringr package Reading data Txt files CSV files XLS, XLSX files SPSS, SAS, Stata,… and other formats data Exporting data to txt, csv and other formats Accessing data from databases using SQL language Probability distributions R as a set of statistical tables Examining the distribution of a set of data One- and two-sample tests Grouping, loops and conditional execution Grouped expressions Control statements Conditional execution: if statements Repetitive execution: for loops, repeat and while Writing your own functions Simple examples Defining new binary operators Named arguments and defaults The '...' argument Assignments within functions More advanced examples Efficiency factors in block designs Dropping all names in a printed array Recursive numerical integration Scope Customizing the environment Classes, generic functions and object orientation Graphical procedures High-level plotting commands The plot() function Displaying multivariate data Display graphics Arguments to high-level plotting functions Basic visualisation graphs Multivariate relations with lattice and ggplot package Using graphics parameters Graphics parameters list Time series Forecasting Seasonal adjustment Moving average Exponential smoothing Extrapolation Linear prediction Trend estimation Stationarity and ARIMA modelling Econometric methods (casual methods) Regression analysis Multiple linear regression Multiple non-linear regression Regression validation Forecasting from regression
predmodr Predictive Modelling with R 14 godz. Problems facing forecasters Customer demand planning Investor uncertainty Economic planning Seasonal changes in demand/utilization Roles of risk and uncertainty Time series Forecasting Seasonal adjustment Moving average Exponential smoothing Extrapolation Linear prediction Trend estimation Stationarity and ARIMA modelling Econometric methods (casual methods) Regression analysis Multiple linear regression Multiple non-linear regression Regression validation Forecasting from regression Judgemental methods Surveys Delphi method Scenario building Technology forecasting Forecast by analogy Simulation and other methods Simulation Prediction market Probabilistic forecasting and Ensemble forecasting
bigdatar Programming with Big Data in R 21 godz. Introduction to Programming Big Data with R (bpdR) Setting up your environment to use pbdR Scope and tools available in pbdR Packages commonly used with Big Data alongside pbdR Message Passing Interface (MPI) Using pbdR MPI 5 Parallel processing Point-to-point communication Send Matrices Summing Matrices Collective communication Summing Matrices with Reduce Scatter / Gather Other MPI communications Distributed Matrices Creating a distributed diagonal matrix SVD of a distributed matrix Building a distributed matrix in parallel Statistics Applications Monte Carlo Integration Reading Datasets Reading on all processes Broadcasting from one process Reading partitioned data Distributed Regression Distributed Bootstrap
d2dbdpa From Data to Decision with Big Data and Predictive Analytics 21 godz. Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. Quick Overview Data Sources Minding Data Recommender systems Target Marketing Datatypes Structured vs unstructured Static vs streamed Attitudinal, behavioural and demographic data Data-driven vs user-driven analytics data validity Volume, velocity and variety of data Models Building models Statistical Models Machine learning Data Classification Clustering kGroups, k-means, nearest neighbours Ant colonies, birds flocking Predictive Models Decision trees Support vector machine Naive Bayes classification Neural networks Markov Model Regression Ensemble methods ROI Benefit/Cost ratio Cost of software Cost of development Potential benefits Building Models Data Preparation (MapReduce) Data cleansing Choosing methods Developing model Testing Model Model evaluation Model deployment and integration Overview of Open Source and commercial software Selection of R-project package Python libraries Hadoop and Mahout Selected Apache projects related to Big Data and Analytics Selected commercial solution Integration with existing software and data sources
apachemdev Apache Mahout for Developers 14 godz. Audience Developers involved in projects that use machine learning with Apache Mahout. Format Hands on introduction to machine learning. The course is delivered in a lab format based on real world practical use cases. Implementing Recommendation Systems with Mahout Introduction to recommender systems Representing recommender data Making recommendation Optimizing recommendation Clustering Basics of clustering Data representation Clustering algorithms Clustering quality improvements Optimizing clustering implementation Application of clustering in real world Classification Basics of classification Classifier training Classifier quality improvements
appliedml Applied Machine Learning 14 godz. This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Bayesian Graphical Models Factor Analysis (FA) Principal Component Analysis (PCA) Independent Component Analysis (ICA) Support Vector Machines (SVM) for regression and classification Boosting Ensemble models Neural networks Hidden Markov Models (HMM) Space State Models Clustering

Najbliższe szkolenia

SzkolenieData KursuCena szkolenia [Zdalne / Stacjonarne]
Predictive Modelling with R - Toruń, ul. Żeglarska 10/14śr., 2017-08-09 09:008610PLN / 3209PLN
From Data to Decision with Big Data and Predictive Analytics - Zielona Góra, ul. Reja 6śr., 2017-08-16 09:0029220PLN / 9605PLN
Programming with Big Data in R - Rzeszów, Plac Wolności 13śr., 2017-08-16 09:0015000PLN / 5600PLN

Other regions

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Kursy w promocyjnej cenie

Szkolenie Miejscowość Data Kursu Cena szkolenia [Zdalne / Stacjonarne]
Java Performance Tuning Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2017-07-31 09:00 9801PLN / 3000PLN
Angular JavaScript Gdynia, ul. Ejsmonda 2 pon., 2017-07-31 09:00 7425PLN / 3475PLN
WordPress Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2017-07-31 09:00 4851PLN / 1570PLN
Node.js concepts & administration, Express.js, V8 engine, monitoring, pm2 Gliwice ul. Karola Marksa 11 wt., 2017-08-01 09:00 9009PLN / 3430PLN
Automatyzacja testów za pomocą Selenium Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-08-02 09:00 7722PLN / 3174PLN
MS SQL Server 2016 Gdynia, ul. Ejsmonda 2 śr., 2017-08-02 09:00 8712PLN / 3140PLN
Visual Basic for Applications (VBA) w Excel - wprowadzenie Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-08-02 09:00 2376PLN / 1192PLN
Angular JavaScript Gdańsk, ul. Powstańców Warszawskich 45 pon., 2017-08-07 09:00 7425PLN / 3475PLN
Tworzenie stron internetowych i optymalizacja pod kątem marketingu internetowego Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2017-08-07 09:00 4851PLN / 3205PLN
Programowanie w ASP.NET MVC 5 Rzeszów, Plac Wolności 13 śr., 2017-08-09 09:00 5841PLN / 2223PLN
Język SQL w bazie danych MSSQL Lublin, ul. Spadochroniarzy 9 czw., 2017-08-10 09:00 2970PLN / 1243PLN
Programowanie w WPF 4.5 Lublin, ul. Spadochroniarzy 9 śr., 2017-08-16 09:00 6435PLN / 2443PLN
Oracle 11g - Język SQL dla programistów - warsztaty Gdańsk, ul. Powstańców Warszawskich 45 pon., 2017-08-21 09:00 6930PLN / 3640PLN
Embedded C Application Design Principles Kraków, ul. Rzemieślnicza 1 czw., 2017-08-24 09:00 12266PLN / 4517PLN
Efektywne wykorzystanie Social Media - Facebook, Twitter, Youtube, Google+, blogi Gdynia, ul. Ejsmonda 2 czw., 2017-08-31 09:00 1881PLN / 1002PLN
Certyfikacja OCUP2 UML 2.5 - Przygotowanie do egzaminu OCUP2 Foundation Katowice ul. Opolska 22 pon., 2017-09-04 09:00 6930PLN / 3360PLN
General Data Protection Regulation - zmiany prawne, wprowadzenie teoretyczne, praktyczne aspekty Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2017-09-04 09:00 7128PLN / 2560PLN
Oracle 12c – Zaawansowane programowanie w PL/SQL Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-09-06 09:00 9900PLN / 3900PLN
Techniki DTP (InDesign, Photoshop, Illustrator, Acrobat) Poznań, Garbary 100/63 pon., 2017-09-11 09:00 5940PLN / 2980PLN
Fundamentals of Devops Wrocław, ul.Ludwika Rydygiera 2a/22 wt., 2017-09-12 09:00 14563PLN / 5013PLN
Język SQL w bazie danych MSSQL Bydgoszcz, ul. Dworcowa 94 wt., 2017-09-19 09:00 2970PLN / 1243PLN
Visual Basic for Applications (VBA) w Excel - poziom zaawansowany Warszawa, ul. Złota 3/11 pon., 2017-09-25 09:00 3069PLN / 1623PLN
Tworzenie i zarządzanie stronami WWW Poznań, Garbary 100/63 pon., 2017-09-25 09:00 5841PLN / 2298PLN
Wzorce projektowe w C# Rzeszów, Plac Wolności 13 czw., 2017-09-28 09:00 3861PLN / 2331PLN
Visual Basic for Applications (VBA) w Excel - wprowadzenie Szczecin, ul. Sienna 9 czw., 2017-10-05 09:00 2376PLN / 1292PLN
Analiza biznesowa i systemowa z użyciem notacji UML - warsztat praktyczny dla PO w metodyce Scrum Łódź, ul. Tatrzańska 11 wt., 2017-10-10 09:00 7722PLN / 3474PLN
Access - podstawy Szczecin, ul. Sienna 9 wt., 2017-10-10 09:00 3465PLN / 1550PLN
PostgreSQL for Administrators Gdynia, ul. Ejsmonda 2 śr., 2017-10-11 09:00 12326PLN / 4235PLN

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