Data Analytics Placements
General
- Undergraduate; 3rd year, 2nd year
- 30 learners; teams of 5
- 150 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 3/3 project matches
- Anywhere
- Academic experience
- Any
- Any
Categories
Skills
Project timeline
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March 25, 2021Experience start
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November 27, 2020Project Scope Meeting
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May 31, 2021Experience end
Timeline
-
March 25, 2021Experience start
-
November 27, 2020Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
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May 31, 2021Experience end
Overview
- Details
-
This work placement allows students to put their theoretical skills into practice within your company. It constitutes a structured period of work-based learning which has been shown to help undergraduate students put into practice a wide range of technical knowledge such as:
- Data Visualisation
- Data Mining for Business Intelligence
- Financial Mathematics
- Programming for Data Analytics
- Statistical Modeling and Forecasting
In addition, students are given the opportunity to develop their professional competence in a working environment, benefiting from mentoring and support from experienced professionals.
Students are available for up to 150 hours starting November till the end of January 2021. This is an unpaid for-credit experience.
- Learner skills
- Project planning, Business consulting, Data analysis, Research
- Deliverables
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The final deliverable will vary depending on the scope of the project.
Students need to complete 150 hours by the end of May 2021.
Project Examples
Students can work individually or in groups of up to 5 in a wide range of Data Analytic related tasks. Tasks completion will be under the supervision of an academic advisor. The advisor is a designated member of staff at our university who will support our students in the tasks to be done.
Students can work with the following:
- Database creation - SQL Server and Oracle
- Data Analytics with advanced mathematics and statistics
- Create models using data analysis and visualisation
- Apply supervised and unsupervised methods to analyse and predict data trends
- Data analysis with descriptive statistics, correlation analysis, and linear regression model
- Numeric and categorical variable analysis
- Examining relationships between interval data with correlation analysis
- Modelling relationships of multiple variables with linear regression
Additional company criteria
Companies must answer the following questions to submit a match request to this experience:
Provide a dedicated contact who is available to answer periodic queries over the duration of the project to address student's questions.
Simple evaluation and feedback to each student at the end of the placement period with a short google feedback form.
Be available for a quick video call with the London Met team to initiate your relationship and confirm your scope is an appropriate fit for the course.
Sign a learning agreement form to confirm that they are in a working partnership with London Met students.