Clement Twumasi Educative Channel

Prior knowledge of programming and statistics is not required. We shall lay a good foundation in the basic quantitative analysis & upgrade into more advanced statistical analysis (statistical tests, different classes of regression models: fixed & mixed-effect regression models, machine learning algorithms, advanced data visualization, data cleaning in R, etc.). We will also learn adequately about looping (for loops, while loops, etc.), use of apply functions (lapply, sapply, mcapply, etc.), storing results from loops as a vector, matrix or three-dimensional array; writing your own functions for some tasks to prove some theorems (central limit theorem, bootstrapping, jackknife and hypercube sampling schemes, etc.), doing novel simulations (of probability distributions, and other statistical processes) and parallelising codes for high speed depending on the complexity of the problem, use of existing libraries/packages for data analysis, amongst others. Lots of practice tasks assigned.