Mathematica - wprowadzenie, wizualizacja i prezentacja danych - Plan Szkolenia

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

SPSS is software for editing and analyzing data.

This instructor-led, live training (online or onsite) is aimed at HR professionals who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training (online or onsite) is aimed at HR professionals and recruitment specialists who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Econometrics is the application of economic data and statistical methods to provide quantitative analysis of economic phenomena.

This instructor-led, live training (online or onsite) is aimed at anyone who wishes to learn and master the fundamentals of econometric analysis and modeling.

By the end of this training, participants will be able to:

• Learn and understand the fundamentals of econometrics.
• Utilize Eviews and risk simulators.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Audience

• managers
• developers
• scientists
• students

Format of the course

on-line instruction and discussion OR face-to-face workshops

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

This course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (https://www.r-project.org) both on a command line and in a graphical user interface (GUI).

This course allows delegate to fully automate the process of forecasting with R

Audience

Business owners (marketing managers, product managers, customer base managers) and their teams; customer insights professionals.

Overview

The course follows the customer life cycle from acquiring new customers, managing the existing customers for profitability, retaining good customers, and finally understanding which customers are leaving us and why. We will be working with real (if anonymous) data from a variety of industries including telecommunications, insurance, media, and high tech.

Format

Instructor-led training over the course of five half-day sessions with in-class exercises as well as homework. It can be delivered as a classroom or distance (online) course.

This course is for data scientists and statisticians that already have basic R & C++ coding skills and R code and need advanced R coding skills.

The purpose is to give a practical advanced R programming course to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

Description: 

This is a course designed to teach R users how to create web apps without needing to learn cross-browser HTML, Javascript, and CSS.

Objective:

Covers the basics of how Shiny apps work.

Covers all commonly used input/output/rendering/paneling functions from the Shiny library.