Internal

IF0SDJ: Foundation Statistics and Data Science

IF0SDJ: Foundation Statistics and Data Science

Module code: IF0SDJ

Module provider: International Study and Language Institute

Credits: 40

Level: Foundation Level

When you'll be taught: Semester 2 / Summer

Module convenor: Dr James Appleby, email: j.f.appleby@reading.ac.uk

Pre-requisite module(s): Before taking this module, you must have GCSE Mathematics 4+ or equivalent. (Open)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Available to visiting students: No

Last updated: 5 August 2024

Overview

Module aims and purpose

Data science and statistics play a large role in many areas our lives, and an understanding of these is key in terms of both life during, and after, university.  During your degree, you are highly likely to encounter reported statistics when evaluating research, and most disciplines will also expect you to produce your own. Examples beyond university life include impacting business decision making, guiding educational policy, research data on how to better target adverts delivered online, and even medical research.  This module provides an introduction to both topics, and helps develop key theoretical, practical, and numerical skills to help succeed in your future undergraduate studies.

For the statistical component, you will be learning basic statistical concepts (mean, variance, and conditional probability), and exploring two well-known distributions (Binomial and Normal).  Later on, you will be building on this and learning to apply hypothesis test methodology to a wide range of scenarios.

In data science you will apply the concepts learned in the statistics part of the course to undertake hypothesis tests in a statistical programming language.

The skills developed in this module have a wide application across university degrees such as business, psychology, biology, and mathematics.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Calculate basic descriptive statistics (including mean and standard deviation) from a dataset
2. Interpret basic descriptive statistics in real life context
3. Utilise common office software to present these statistics in a variety of ways
4. Undertake mean, median and proportion hypothesis tests with the assistance of the course calculator and using a piece of statistical software
5. Interpret the findings of hypothesis tests and confidence intervals in context

Module content

The schedule of this module, including start and finish dates, follows that of the January Start Foundation, which does not follow standard University Semesters. There is however significant overlap and Semesters referred to in this document are the University Semesters where most of this module teaching will take place. Information about specific key module dates will be provided by the International Foundation Programme prior to the start of the course.

Semester 2

Students will develop basic calculatory skills and learn how to generate statistics from data sets. Topics included are:

• Foundations of Statistics
• Numerical Measures
• Data Representation and Interpretation
• Probability Theory
• Discrete Random Variables and Binomial Distribution
• Bivariate Data
• The Normal Distribution
• Data Collection

Also covered is the use of common office software used to present data. Topics included are:

• Word Processing Software
• Slide Creation Software

Summer

Students will expand upon semester 2 by learning how to undertake and interpret hypothesis tests. Topics included are:

• Estimation and Approximation and Combinations of Independent Random Variables
• Introduction to Hypothesis Testing
• Methods of Hypothesis Testing
• Contingency Tables
• Non-Parametric Tests and Experimental Design
• Confidence Intervals and the Central Limit theorem
• Concepts in Hypothesis Testing
• Hypothesis Tests Between Two Parameters

In this semester, there will also be a focus on using common statistical software to undertake these tests once they have been learned.

Structure

Teaching and learning methods

This module uses the flipped learning approach: - students are given two sets of stats questions and a collection of lecture shorts (1–5-minute videos) that allow students to make an attempt at these questions.  These are then reviewed in two tutorial classes.

Students are also provided with a 2-hour Information and Communications Technology (ICT) practical (full instruction set, collaborative exercise) to better explore the ICT and data science components of the course.

Feedback and feedforward are provided through face-to-face tutorials for students on campus and posted on Blackboard for summative assessments. Students will also be expected to take responsibility for their own learning by setting goals and making regular use of the University library, especially their online resources for students working remotely, Blackboard (the University Virtual Learning Environment) and other online resources.

The schedule of this module, including start and finish dates, follows that of the January Start Foundation, which does not follow standard University Semesters. There is however significant overlap and Semesters referred to in this document are the University Semesters where most of this module teaching will take place. Information about specific key module dates will be provided by the International Foundation Programme prior to the start of the course.

Study hours

At least 132 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Scheduled teaching and learning activities  Semester 1  Semester 2  Summer
Lectures 12 16.5
Seminars 21 16.5
Tutorials 11 11
Project Supervision
Demonstrations
Practical classes and workshops 22 22
Supervised time in studio / workshop
Scheduled revision sessions
Feedback meetings with staff
Fieldwork
External visits
Work-based learning

Self-scheduled teaching and learning activities  Semester 1  Semester 2  Summer
Directed viewing of video materials/screencasts 34 34
Participation in discussion boards/other discussions
Feedback meetings with staff
Other
Other (details)

Placement and study abroad  Semester 1  Semester 2  Summer
Placement

Please note that the hours listed above are for guidance purposes only.

Independent study hours  Semester 1  Semester 2  Summer
Independent study hours 100 100

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Type of assessment Detail of assessment % contribution towards module mark Size of assessment Submission date Additional information
Set exercise Practical project exploration 20 750 words (max) report, plus PowerPoint Semester 2, Vacation Week 1 A short exploration of elements of one of three large data sets provided using techniques learned in Semester 2 \$£ including a short report with inserted excel outputs, and a marked PowerPoint.
In-person written examination Stats test 1 20 1 hour 30 minutes Semester 2, Teaching Week 8 An exam testing the skills learned in semester 2 \$£ largely technical in nature and not exploring deep context.
Set exercise Practical project 30 750 words Summer, Vacation Week 5 This builds on the exploration coursework and undertakes some basic data analysis of one of three large data sets, with the expectation that the student will carry out this analysis using the statistical software taught on the course.
In-person written examination Stats test 2 30 1 hour 30 minutes Summer, Vacation Week 6 An exam testing the skills learned in semester 2 and 3 \$£ exploring the context of semester 2 topics further as well as the new material from semester 3.

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Regular tutorial classes on problems of the type encountered in examinations. Additional exercises based on recorded lectures (students send in solutions to gain feedback, with some using automated marking).  Optional surgery (drop-in) sessions.

All summative pieces are given formative feedback to enhance their effectiveness.

Reassessment

Type of reassessment Detail of reassessment % contribution towards module mark Size of reassessment Submission date Additional information
Set exercise Practical project exploration 20 750 words (max) report, plus PowerPoint During the University resit period A short exploration of elements of one of three large data sets provided using techniques learned in Semester 2 \$£ including a short report with inserted excel outputs, and a marked PowerPoint.
In-person written examination Stats test 1 20 1 hour 30 minutes During the University resit period An exam testing the skills learned in semester 2 \$£ largely technical in nature and not exploring deep context.
Set exercise Practical project 30 750 words During the University resit period This builds on the exploration coursework and undertakes some basic data analysis of one of three large data sets, with the expectation that the student will carry out this analysis using the statistical software taught on the course.
In-person written examination Stats test 2 30 1 hour 30 minutes During the University resit period An exam testing the skills learned in semester 2 and 3 \$£ exploring the context of semester 2 topics further as well as the new material from semester 3.