Part 1: Data Vizualisation
In this module you will learn how to explore efficiently your dataset and how to make effective graph.
Lab 1: Introduction to data visualization and ggplot2
Lab 2: Data visualization - Part 2
Lab 3: Data visualization & data wrangling - Part 3
Lab 4: Data visualization & data wrangling - Part 4
Part 2: Modelling data & Statistical inference
Lab 5: Modelling with a single predictor
Lab 6: Modelling with a single predictor - Part 2
Lab 7: Modelling with a single predictor - Part 3
Part 3: Communicating your results (Open science & reproducibility)
Lab 9: Introduction to Rmarkdown
Part 4: Looking forward!
Congratulation! You made it through the labs and by now you should be relatively comfortable with R and with wrangling, analyzing and visualizing your datasets. Practice will make your data science skills stronger and if you wish to continue with Quantitative Methods in the future I recommend you to try new things and learn new concepts. Here I provide some resources that can be useful for your future in data science.
4.1 Data Ethics : Data ethics is important as coding statistics and Machine Learning algorithms is a matter of choice, choice in your data, in what you want to visualize … This slides guide you through some of the good choices you need to make.
4.2 Interactive data visualization: You can take you visualization level further by making interactive plots in R!
I wish you good luck with your term paper and I wish you to have a successful career!