Mathematica - Introduction, Visualization, and Data Presentation Training Course

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 a wide variety of packages for data mining.

This instructor-led, live training in the US (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.

This instructor-led, live training in the US (online or onsite) is aimed at intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.

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

• Understand the fundamentals of forecasting techniques in R.
• Apply exponential smoothing and ARIMA models for time series analysis.
• Utilize the ‘forecast’ package to generate accurate forecasting models.
• Automate forecasting workflows for business and research applications.

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.

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.

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).

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 is an introduction to applying neural networks in real world problems using R-project software.

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

This instructor-led, live training in the US (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to perform statistical analysis using SPSS to interpret data accurately, run complex statistical tests, and generate meaningful insights.

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

• Navigate the SPSS interface and manage datasets efficiently.
• Perform descriptive and inferential statistical analyses.
• Conduct t-tests, ANOVA, MANOVA, regression, and correlation analyses.
• Apply non-parametric tests, principal component analysis, and factor analysis for advanced data interpretation.

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.

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

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.