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Due to the COVID-19 situation, we are offering our online courses at a 20% discount
Note: The courses will be delivered remotely using online collaborative teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the course.
Introduction to R
R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is open-source software, and RStudio, which is a versatile, user-friendly interface for using R. It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers.
This course will cover some basic features of R and lay the groundwork for you to improve your R skills independently. The course is self-paced and focused on developing practical skills.
You will need a computer with course access (to install R before attending the course).
Course 1: Tuesday 11 & Wednesday 12 August, 2020 (two half-day sessions)
Course 2: Wednesday 19 & Thursday 20 August, 2020 (two half-day sessions)
Duration: 9.30 am to 1.00 pm – Each day
Details and bookings
Introductory Statistics for Researchers
In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?"
How does one decide which statistical procedure is the most appropriate? What do all the sections of the output mean?
This course is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher. The theory behind the statistical procedures outlined in the course will, in general, not be discussed.
Statistical analyses require specialised software to perform calculations. In this course, we use the free statistical program R, although researchers may have another statistical package available to them.
A range of statistical analyses will be discussed in the course, as described in the course outline below. We will talk through examples of all analysis types and will demonstrate how to carry them out in R. Equal emphasis will also be put on interpreting the output of these analyses. There will be plenty of practical work in the course. You will need basic R proficiency to carry out the practical work; we will teach only the additional R commands needed for these analyses.
You will need to use a computer during the course. You will also need administrator rights to install software needed for the course onto your computer. Previous basic experience using the program R is essential. Please read the Important Notes on the registration page!
Date: Monday 24 to Thursday 27 August, 2020 (four half-day sessions)
Duration: 9.00 am to 1.00 pm – Each day
Introduction to Regression Modelling in R
The core outcome from this course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models. Learning statistical methods in a systematic way, instead of as a "cookbook" of different methods, enables you to take a systematic approach to key steps in analysis (like assumption checking) and to extend your skills to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models).
This short course, taking place over five half-days, is aimed at applied researchers with prior experience using R and familiar with introductory statistics tools - you should know about the t-test, linear regression, analysis of variance and know something about orthogonal and nested designs. If you have not used R before, we strongly recommend you learn basic features of R and how to use the RStudio interface to R before this course. If you have no experience with R, Stats Central offers an Introduction to R short course, which will be run on 11-12 August and 19-20 August (see above). If you need to revise introductory statistics material, you should attend the Introductory Statistics for Researchers course on 24-27 August (see above), as the material in it is taken as assumed knowledge for this regression course.
You will need to use a computer during the course. Some familiarity with introductory statistics and R will be assumed.
Date: Monday 31 August to Friday 4 September, 2020 (five morning sessions)
Duration: 9.00 am to 12.30 pm – Each day
Introduction to Python for Data Science
Python is a widely used programming language to manipulate, analyze, and visualize data. It is one of the most popular languages for Data Science, especially when dealing with complex, uncurated or text datasets.
This course assumes that you have never used Python before, but you have some basic programming knowledge. You will learn how to obtain and install Python, which is open-source software, and Jupyter Notebook, which is an interactive computational environment, in which you can combine code execution, rich text, mathematics, plots and rich media.
This two half-day introduction to Python will cover some useful features of Python for data science. It will discuss various online resources available to further develop your data science skills using Python.
Presenter: A/Professor Raymond Wong (Stats Central and UNSW School of Computer Science and Engineering)
Course Requirements: You will need a computer.
Date: Tuesday 1 & Wednesday 2 September, 2020 (two half-day sessions)
Duration: 10.00 am to 1.00 pm – Each day
Text Analytics in Python (Advanced)
More than 70% of the data on the internet is unstructured. Among them, text is the most common form that appears in almost all data sources. For example, text data such as emails, online reviews, tweets, news and reports hold valuable information and insight for most research and applications. Text analytics, usually involving techniques from text mining or natural language processing (NLP), can automatically uncover patterns and extract meaning/context from these unstructured texts.
This course assumes that you have basic Python programming knowledge, or have previously attended "Introduction to Python for Data Science" from Stats Central. This course will provide you the foundation to process and analyze text.
In this course, we will cover some useful Python features and libraries for text processing and analysis. We will touch on some advanced topics such as sentiment analysis, text classification, and/or topic extraction.
Date: Tuesday 8 & Wednesday 9 September, 2020 (two half-day sessions)
Duration: 10.00 am to 1.00 pm – Each day
Enquiries: Stats Central UNSW
Statistical Society of Australia
PO Box 213
Belconnen ACT 2616 Australia
02 6251 3647www.statsoc.org.auABN 82 853 491 081
Please direct enquiries to:
Marie-Louise Rankin, Executive Officer
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