Apologies for cross-posting

CMIST's Short Course training programme offers training at introductory, intermediate and advanced level in research methods and quantitative data analysis aimed at academics and applied researchers in the public and private sectors.

Courses take place in the Humanities Bridgeford Street Building, University of Manchester.

CMIST has three courses coming up in December which have available places:-
·         Social Media Data Analysis (introductory) - 02 December 2016<http://www.cmist.manchester.ac.uk/study/short/introductory/social-media-data-analysis/>
·         Multilevel Modelling (intermediate) - 08 December 2016<http://www.cmist.manchester.ac.uk/study/short/intermediate/multilevel-modelling/>
·         Computational Science using Big Data in R  (Advanced) - 12 December 2016<http://www.cmist.manchester.ac.uk/study/short/advanced/computational-social-science/#d.en.429367>

Booking for all courses is open at http://www.cmist.manchester.ac.uk/study/short/booking/
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Social Media Data Analysis
2 December 2016, 9.30am-4.30pm
Instructor: Mike Thelwall
Venue: Cathie Marsh PC Cluster, Humanities Bridgeford Street Building, University of Manchester
Level: Introductory
Fee: £195 (£140 for those from educational, government and charitable institutions).
Outline
This course describes how to use free software Mozdeh and Webometric Analyst to gather tweets and to download comments on YouTube videos. The course will also describe simple methods to gain insights into the meaning of the downloaded texts and to identify patterns within the data.
Objectives
You will learn to use the free Mozdeh and Webometric Analyst software in order to:

·         Gather tweets from a specific user or matching a keyword query

·         Gather comments on one or more YouTube videos

·         Construct network diagrams from users or comments

·         You will also learn some basic analysis methods for the Twitter and YouTube comments gathered:

·         Simple quantitative methods, such as word frequency analysis, gender difference detection, sentiment analysis and time series graphs.

·         Content analysis to provide insights into the YouTube or Twitter topic studied
Prerequisites
Participants should have a basic familiarity with YouTube and Twitter, and be prepared to learn to use new software. Familiarity with Microsoft Windows.

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Multilevel Modelling
8 December 2016, 10am-4.30pm
Instructor: Jennifer Prattley
Venue: Cathie Marsh PC Cluster, Humanities Bridgeford Street Building, University of Manchester
Level: Intermediate
Fee: £195 (£140 for those from educational, government and charitable institutions).
Outline
This one-day course begins with a description of some examples where multilevel models are useful in statistical analysis and some examples of multilevel populations. We then cover the basic theory of multilevel models including random intercept and random slope specifications, the use of contextual variables in multilevel analysis and modelling repeated measures.  This course suits social scientists who want to learn about a quantitative technique that allows both individual and group level variations to be simultaneously taken into account when modelling social phenomena.
Objectives

·         Introduce the general idea of multilevel modelling

·         Consider some issues of multilevel modelling from a substantive and theoretical perspective.

·         Show how Multilevel modelling can applied to social data using specialist software MLwiN
Prerequisites
No prior knowledge of multilevel modelling is assumed.  You will need to have some familiarity with regression models.

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Computational Science using Big Data in R
12th December 2016, Time: 9am - 5pm
Instructor: Dr Jonathan Minton
Venue: Cathie Marsh PC Cluster, Humanities Bridgeford Street Building, University of Manchester
Level: Advanced
Fee: £195 (£140 for those from educational and charitable institutions).
Outline
This course will introduce a workflow for working efficiently with large amounts of data in R, using data from the Human Mortality Database (HMD) and Human Fertility Database (HFD). Using both of these large databases in an extended case study, the course will show how the R packages plyr and purrr can be used to automate and speed up all stages of the quantitative social science workflow, from tidying and loading data from multiple sources, to producing dozens of separate analyses and data visualisations through a single chunk of code.
While working through the extended case study, related packages, processes and patterns for working with large-scale and complex data efficiently will be introduced, including packages like stringr, tidyr and dplyr for data management, and 'piped coding' approaches for making R code more 'literate': easier to write, understand and reason about.
If you use the HMD and HFD, the code presented will likely be useful right away for your work. Even if you do not, the general patterns, concepts and methods introduced through the case study will help you think about how to manage large amounts of data and automate your own data workflows.
Objectives
By the end of the course, you will:

·         Understand the difference between 'piped' and 'standard' R code, and why 'piped' expressions are closer to written and spoken language, and so easier to reason about, develop, and debug.

·         Understand the concept of the 'data to information' chain, and why you should think carefully about all stages in the sequence linking the acquisition of raw data to the development of new knowledge.

·         Have been introduced to the 'tidy data' paradigm for storing and working with standard, rectangular data.

·         Have reasoned through the challenges of loading data from multiple sources, and arranging and combining data into a tidy data target source.

·         Have been introduced to and applied the 'split-apply-combine' paradigm from plyr, and the functional programming paradigm from purrr, to achieve process automation in two related ways.

·         Be introduced to the pattern of solving programming tasks first in specific cases, and then of generalising these solutions to form functions which can be applied many times.

·         Understand how to automate the production of multiple figures and other outputs using both plyr and purrr.

·         Be able to use a pre-existing efficient data workflow when working with data from the HFD and HMD, and be ready to produce analogous workflows for other tasks and sources of data.
Prerequisites
You must already be adept and comfortable using R in quantitative research, as well as willing to explore alternative approaches for working with R. Ideally, you should also be familiar with the RStudio integrated design environment for working with R.

Booking for all courses is open at http://www.cmist.manchester.ac.uk/study/short/booking/



Mark Kelly
Administrator
CMIST | G9 Humanities Bridgeford Street | University of Manchester | Manchester | M13 9PL
Tel 0161 275 0796
Internal 50796