Data Wrangling – Organising and Enabling Data

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Location

InTuition House

210 Borough High St

London

SE1 1JA

United Kingdom

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Sales Have Ended

Registrations are closed
This course is now full. Please contact info@aqmen.ac.uk to be added to the waiting list. If you would like to be notified of new training opportunities please sign up to our mailing list: http://www.aqmen.ac.uk/mailing_list
Event description

Description

Data Science: Training and Capacity Building

Data Wrangling – Organising and Enabling Data

The Industrial Strategy recognises that a major challenge facing UK businesses and industry is how best to utilise big data to improve economic performance and increase productivity. A substantial barrier to exploiting the potential offered by emerging forms of big data is the lack of a suitably trained workforce with appropriate analytical skills. Many statistical techniques used in the social sciences are also suitable for the analysis of big data in non-academic settings, however social science graduates often lack experience in applying their skills and knowledge in non-academic research domains.

This three-day workshop will provide a fast-track introduction for individuals wishing to learn how to work with data suitable for statistical analysis of business problems. Preparing and enabling data (data wrangling) is an essential aspect of undertaking data intensive statistical research. Data wrangling is highly time consuming and can be complex especially when dealing with messy data, which is often encountered in the business world.

The workshop will provide participants with a practical introduction to the fundamental concepts that characterise data wrangling and which are critical for successful data analyses.

The workshop will comprise both lectures and practical hands-on exercises in the programming languages R and Python. The final day of the workshop will be a group exercise in the form of a ‘hackathon’, where participants will be tasked with addressing an industry challenge using the techniques learned during the first two days.

As a result of attending this workshop, participants will be equipped with a range of skills that are highly desirable in industry, including the use of the R and Python programming languages, working with real-world (i.e. messy) data, and harvesting data from the web and other online sources.

Topics covered during the course include:

  • Organising the data wrangling workflow
  • Importing data sets into R
  • Preparing and enabling data for statistical analyses
  • Collecting data from online sources using Python
  • Dealing with messy data (e.g. web data, business data)
  • Using loops and functions to make the workflow more efficient
  • Dealing with missing data
  • Practical hands-on sessions introducing the methods and techniques outlined above.

Prerequisite knowledge

The workshop is suitable for postgraduate social science students and early-career researchers with a good general knowledge of statistical methods and data analysis. Some experience of analysing large-scale and complex social surveys (e.g. the birth cohort studies or household panel surveys) or administrative datasets would be an advantage.

The workshop is also suitable for academic staff engaged in teaching data analysis and statistical methods in social science settings who are seeking to incorporate data science and big data analysis skills into their programmes.

Travel and accommodation bursaries

Attendees may be eligible to claim travel and/or accommodation costs to attend. In order to be eligible for a bursary you must reside outside London. Reimbursement can only take place if you follow the reimbursement process detailed below and bursaries will be capped at the following rates:

Expenses

Reimbursement process

Attendees are responsible for arranging travel and/or accommodation (if applicable) themselves and will only be reimbursed upon presentation of original receipts (no photocopies or credit card receipts will be accepted) and completion of the relevant expense claim form which will be provided post-event.

AQMEN will not reimburse the following costs (unless agreed prior to the event):

  • Mileage
  • First class travel
  • Meals or room service
  • Inter-city travel (e.g. buses to and from event venue)
  • Taxi fares
  • Credit card fees
  • Sundries (e.g.wireless internet access or newspapers at accommodation)

All expense claims and receipts must be received by the AQMEN office no later than 2 weeks following the last day of the event in order to be eligible for reimbursement.

The workshop is free to attend. Training will run from 10am - 4.30pm each day with refreshments and lunch provided.

Registration closes at 1pm on 7th March 2019.

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Date and Time

Location

InTuition House

210 Borough High St

London

SE1 1JA

United Kingdom

View Map

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