Uppsala

NobleProg Uppsala, Stationsgatan 4, Uppsala, sweden, 753 40

The course location is close to the city center with good access to public transport. You can discover shopping treasures at Godsmagasinet, a group of enchanting shops tucked away next to the train station. There is a popular Saturday flea market in Vaksala Square where you can buy souvenirs. You can enjoy a guided candlelit tour of Uppsala Cathedral or admire Viking artefacts, mummies and the eerie 17th-century anatomical theater at the Gustavianum Museum. For a refreshing outdoor afternoon, try a walk along the paths and eat next to the river Fyris, which flows right through the city.

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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

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Forecasting with R

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R for Data Analysis and Research

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Introduction to R

21 Hours

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