Brighton

3rd Floor, Queensberry House , Brighton, united kingdom, BN1 3XF

This centre occupies a prominent location on Queens Road which links Brighton station to the Churchill Square shopping centre and the seafront. Queensberry House, which offers some sea views, is in the heart of the business district in this city. Brighton is a highly sophisticated and modern resort with a thriving economy and tourist industry. Millions of visitors flock here every year, both for leisure and to attend conferences and exhibitions at the Brighton Centre, one of the largest multi-purpose conference and exhibition centres in the country. Because it is home to so many businesses involved in media, particularly digital or 'new media', Brighton is sometimes referred to as 'Silicon Beach'. The city's priority sectors, which include financial services, creative industries and environmental technologies as well as digital media, are all growing steadily.

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Data Analytics with Tableau, Python, R, and SQL

35 Hours

Data Analysis with Python, R, Power Query, and Power BI

21 Hours

Statistical Analysis with Stata and Integration with R

35 Hours

Data Analytics With R

21 Hours

Data and Analytics - from the ground up

42 Hours

NLP: Natural Language Processing with R

21 Hours

R Programming for Excel

21 Hours

R Programming for Finance

28 Hours

Advanced R

14 Hours

Algorithmic Trading with Python and R

14 Hours

Programming with Big Data in R

21 Hours

Introductory R (Basic to Intermediate)

14 Hours

R Fundamentals

21 Hours

Cluster Analysis with R and SAS

14 Hours

Data Mining with R

14 Hours

Data Mining & Machine Learning with R

14 Hours

Foundation R

7 Hours

Introduction to R with Time Series Analysis

21 Hours

KNIME with Python and R for Machine Learning

14 Hours

Marketing Analytics using R

21 Hours

Predictive Modelling with R

14 Hours

Advanced Machine Learning with R

21 Hours

Introductory R for Biologists

28 Hours

R

21 Hours

Neural Network in R

14 Hours

Advanced R Programming

7 Hours

R Programming for Data Analysis

14 Hours

Shiny, R and HTML: Merging Data Science and Web Development

7 Hours

Statistical Analysis with Stata and R

35 Hours

Data Science with Tableau and R Programming

14 Hours

Introduction to Data Visualization with Tidyverse and R

7 Hours

Building Web Applications in R with Shiny

7 Hours

Forecasting with R

14 Hours

R for Data Analysis and Research

7 Hours

Introduction to R

21 Hours

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