36 Statistics Preparatory Programs

“If you can’t measure it, you can’t change it.” Masterclass in Labour Statistics: From Standards to Data course enable us to monitor one of the most important aspects of our well-being: work. As we spend a large part of our lives at work and live on the earnings from it, governments must have access to timely and relevant data to inform accurate and sound policies.

This is one of several subject combinations available in our Diploma of Higher Education in Computing & IT and a second subject (Q67). Diploma of Higher Education in Computing & IT and Statistics With this joint diploma, you can specialise in an area of computing and IT and combine it with statistics, dividing your time equally between subjects. With competence in statistics and computing skills, you’ll be equipped to work in organisations that base decisions on statistical analysis or seek to interpret “big data”.

Random variables, independence, and conditional probability; discrete and continuous distributions, moments, and distributions of several random variables. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non-parametric tests, regression and correlation analyses; applications in engineering, industrial manufacturing, medicine, biology, and other fields. Prerequisite: CME 100/ENGR154 or MATH 51 or 52.

This is one of several subject combinations available in our Certificate of Higher Education in Computing & IT and Statistics and a second subject (T13) – you’ll divide your time equally between the two subjects. You will be introduced to probability and the most common methods of statistical data analysis, and you will explore the profound technological, economic, political and ethical change driven by digital technologies.

Introduces basic statistical modeling techniques. One- or two- sample inference for unknown means, proportions and variances, categorical data analysis, introduction to design of experiments and analysis of variance, analysis of simple and multiple linear regression models, non-parametric methods.

Basic concepts of estimation and tests of hypotheses, ideas from probability; one-, two-, and multiple-sample problems. Applications are in the social sciences, and students will be able to understand the basics of using a sample to predict uncertainty.

Numerical and graphical summaries of univariate and bivariate data. Basic probability, random variables, binomial distribution, normal distribution. One-sample statistical inference for normal means and binomial probabilities. Primarily for students in the social sciences with limited mathematics preparation.

Introduction to the logic and processes involved in descriptive and inferential statistics for psychology. Topics include statistical inference, significance, t-tests, ANOVAs, correlation, regression, and statistical software analysis. This is a hybrid class: class time is reserved for hands-on activities.

Due to COVID-19, classroom courses during Fall 2021 will be offered in a live online format. We continue to offer self-paced online courses.

Undergraduate Prerequisites: Academic background that includes the material covered in a standard course on college algebra.

Exposes students to the fundamentals of probability, decision analysis, and statistics, and their application to business. Topics include probability, decision analysis, distributions, sampling, estimation, hypothesis testing, and chi-square. Effective Fall 2018, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning I.

Elementary treatment of probability densities, means, variances, correlation, independence, the central limit theorem, confidence intervals, and p-values. Students learn to answer questions such as how can a pollster use a sample to predict the uncertainty of an election?

The Master of Arts and Master of Science degrees in Applied Statistics provide students with the necessary background to seek employment in statistics, data sciences or a related field. The degree programs provide training in applied statistics and consulting, as well as in the theory of statistics pertinent to applications. In addition, students are required to study an approved area outside of statistics at the graduate level. Possible areas include numerical analysis, biostatistics, education, economics, sociology, psychology, biology, business, bioinformatics, computer science, mathematical finance, and many others.

Introduces students to macro and micro approaches to project cost estimation. Case studies of both pre-project and in-process estimating cover some of the more common perils of human irrationality associated with project estimation to help develop more sensible, achievable project outcomes.

Examines the use of economic and statistical tools for making business decisions at an advanced level and prepares students for future study in business analytics. Introduces programming for data analysis (no previous programming knowledge required) and links data analysis to decision-making using both spreadsheet modeling and statistical programming.