| Module Title: | Quantitative Techniques 1 – Data Analysis |
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| Language of Instruction: | English |
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| Teaching & Learning Strategies: |
Student-centred lectures fostering individual and collaborative engagement with problem-solving exercises and classroom activities, in class demonstrations, blended learning (integrated mathcasts, software screencasts, applets, spreadsheets, eBooks and other learning resources), independent learning. Examples of real data and statistics used to develop students' critical thinking, ability to deal with uncertainty and international perspectives (e.g. by exploring issues related to economics, social justice, climate change …) Initial development of enquiry skills with integrated emphasis on IT skills. |
| Module Aim: |
The aim of this module is to develop students’ mathematical and statistical reasoning and skills, including how to collect, analyse, interpret and present data. Students will be introduced to the areas of descriptive statistics, surveying, sampling, linear correlation and regression, and forecasting. The module's emphasis on both the conceptual and practical will assist students to confidently and fluently use mathematical and statistical thinking and techniques to enquire using data, solve problems and make better business decisions. |
| Learning Outcomes |
| On successful completion of this module the learner should be able to: |
| LO1 |
Describe basic concepts in data analysis, descriptive statistics, surveys, sampling, linear correlation and regression, and time series |
| LO2 |
In business scenarios, calculate and interpret statistics |
| LO3 |
Apply statistical skills and thinking to explore data numerically and graphically |
| Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
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| No recommendations listed |
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. |
| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. |
| No requirements listed |
Module Content & Assessment
| Indicative Content |
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Introduction to Quantitative Techniques (10%)
Use an electronic calculator; Undertake basic arithmetic operations; Rearrange equations; Work with decimals and percentages; Calculate and interpret absolute and relative change
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Introduction to Statistics, Surveys and Samples (30%)
Describe statistics and data analysis; Appreciate the importance of statistical reasoning in business and everyday life; Interpret critically numbers and statistics: draw warranted conclusions and spot flaws in arguments based on numbers and statistics; Appreciate the statistical investigative cycle; Distinguish between categorical (nominal, ordinal) and numerical (discrete, continuous) data, and between primary and secondary data; Tabulate data and interpret tables; Draw conclusions from tables, including Simpson's Paradox; Interpret different types of charts and graphs; Explain the terms population, sample and inference; Distinguish between and describe random and non-random sampling methods; Design a questionnaire; Outline the procedure to follow in conducting a sample survey; Describe experiments; Appreciate the business applications of big data and analytics; Appreciate ethical issues; Appreciate the role of information technology in collecting data
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Averages and Dispersion (25%)
Recognise and explain variability; Calculate and interpret the mean, median and quartiles; Calculate and interpret the range and interquartile range; Calculate and interpret the variance and standard deviation; Interpret the shape of histograms and boxplots; Interpret output from spreadsheet and statistical software
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Linear Correlation and Regression, and Time Series (35%)
Draw and interpret scatter diagrams, calculate and interpret the coefficient of linear correlation, the coefficient of determination and the line of linear regression, make and interpret predications using the line of linear regression, calculate and interpret correlation coefficient for ranked data; Identify the factors which affect a time series, calculate a moving average trend and seasonal variation, and forecast future values; Interpret output from spreadsheet and statistical software
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| Assessment Breakdown | % |
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| Continuous Assessment | 50.00% |
| End of Module Formal Examination | 50.00% |
| Continuous Assessment |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Written Report |
Data analysis assignment (integrated project) |
2,3 |
20.00 |
Week 10 |
| Other |
Online quizzes |
1,2,3 |
30.00 |
n/a |
| End of Module Formal Examination |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Formal Exam |
n/a |
1,2,3 |
50.00 |
End-of-Semester |
SETU Carlow Campus reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Every Week |
3.00 |
| Independent Learning |
Every Week |
6.00 |
| Total Hours |
9.00 |
| Workload: Part Time |
| Workload Type |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Every Week |
1.50 |
| Independent Learning Time |
Every Week |
7.50 |
| Total Hours |
9.00 |
Module Delivered In
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