CIND 123 - Data Analytics: Basic Methods
Course DescriptionThis course is an introduction to R, analyzing and exploring data with R, and using R with a database. It focuses on statistics for model building and evaluation. Topics cover experimental research, correlation analysis, regression, confidence intervals, and group comparisons, and parametric and non-parametric models.
What Will You Learn?
This course provides data analytics technical skills for conducting quantitative analyses on data and big data.
Learn how to:
- Utilize real datasets with graphs and numerical measures.
- Conduct a uni-variate and bi-variate analyses on datasets in R.
- Flag potential outliers for further investigation.
- Illustrate graphically the probability distribution of variables.
- Differentiate between discrete and continuous probability distributions.
- Calculate binomial probabilities using normal approximations.
- Investigate whether a sample size is large enough to apply the central limit theorem.
- Apply testing and estimation techniques for simple linear regression analyses in R.
Students are recommended to check the important dates for the Chang School current term before enrolling in the course and paying the fees. Notably, the Azure Virtual Desktop assigned to students will be accessible two days after the course’s starting date, and swapping between sections will not be permitted.
Students are also encouraged to download the Microsoft Remote Desktop app to access the software needed to complete this course’s requirements. Students also need to test the compatibility of the computer they plan to use before the first session, as machines operated using a third-party administrator, such as laptops provided by a workplace, may not allow access to the required software/download(s). International students might need to use their virtual private network (VPN) software if they cannot connect to University resources.
- Applied Analytics and Statistics for 21st Century Decision-Making : Required Courses (Option 1), Required Courses (Option 2)
- Business Decision Analysis : Electives (select 4)
- Computer Coding (Formerly Computer Programming Applications) : Electives (select 2)
- Data Analytics, Big Data, and Predictive Analytics : Required Courses
- Financial Predictive Data Analytics : Electives (select 2)
- Health Informatics : Electives (select 3)
- Scientific Research Policy and Ethics : Required Courses (select 3)