With volumes of data rapidly expanding, the profile of data analytics has become important for most large companies. Data analytics involves sifting through tons of data to identify patterns and trends in order to make recommendations that assist senior leadership in making business decisions. Companies that have the technical capabilities to gather the large volumes of data need individuals, who can analyse the data effectively. The course imparts in students, the tools and the skill sets that help them make inferences out of volumes of data by asking the right questions and analyzing the data, thereby helping them take up jobs as Data Analysts in different industries. This course is ideal for individuals from technical backgrounds.
Duration: 30 Hours: Read formatted data into R
Subset, remove missing values from, and clean tabular data
Write custom functions in R to implement new functionality and making use of control structures such as loops and conditionals
Use the R code debugger to identify problems in R functions
Make a scatterplot/boxplot/histogram/image plot and modify a plot with custom annotations
Define a new data class in R and write methods for that class
Plotting systems and graphics devices in R
In this course students will learn how to fit regression models, how to interpret coefficients, how to investigate residuals and variability. Students will further learn special cases of regression models including use of dummy variables and multivariable adjustment. Extensions to generalized linear models, especially considering Poisson and logistic regression will be reviewed.