Earning Criteria

• Successful completion of a reflection to assess background on logic, statistics, and programming, as well as their academic and professional goals on data science and quantitative analysis.
• Successful completion of a practical quiz that will assess the learner's skills to organize scripts and working directories, import data into R, create objects, apply basic R operations, recognize and create different data structures (vectors, matrices and dataframes), manipulate data, deal with missing values and calculate measures of descriptive statistics.
• Successful completion of a practical quiz that will assess how to extract and manipulate data, edit dataframes, calculate summary statistics, work with factors, build contingency tables, and create complex plots (lines, points, axis, legends).
• Successful completion of a practical quiz that will assess how to simulate data based on statistical distributions, combine dataframes, employ basic data manipulation functions (unique, duplicated, which, order, min, max), create and explore date and time datasets, sample data, and create histograms and density plots.
• Successful completion of a practical quiz that will assess how to create functions, implement loops, and test performance with different sets of parameters.
• Successful completion of a practical quiz that will assess how to create more complex functions, implement loops and conditions, optimize code speed, and organize and debug pieces of code.
• Successful completion of a reflection to assess links between background and new knowledge acquired in this course.