Curs 2009-2010
Data Analysis (2116)
Teaching guide
Activities schedule
Teaching guide
Course description
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Academic course |
2009-2010 |
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Name of the course |
Data analysis |
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Code |
21116 |
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Course type |
Compulsory |
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Degree |
International Business Economics |
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Credits |
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ECTS |
4,5 |
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Work load for students |
107 hours |
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Year |
First |
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Type |
Quarter |
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Period |
First quarter |
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Coordination |
Walter García-Fontes |
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Department |
Economics and Business |
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Teaching staff |
Walter García-Fontes |
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|
Luca Di Gennaro |
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|
Ana Isabel Guerra |
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|
Eva Ventura |
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Teaching assistants |
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Grups |
Business Administration and |
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Management (2) |
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Economics (2) |
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Business Administration (2) |
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International Business Economics (1) |
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Languages |
Catalan, English and Spanish |
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Schedule |
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Lectures |
Wednesday |
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Seminars |
Monday through Frida |
Introduction to the course
Data Analysis is a course centred in the collection, organization and descriptive analysis of statistical data.
This course is also often called Descriptive Statistics. This is the most basic course in Statistics, but establishes the basis for all the statistical knowledge, and therefore it is a very important course. The acquired competences, though, apart from being useful for the future, are also instrumental for everyday use in all courses. Furthermore statistics is used in a lot of other contexts, such as the media or the administration, and probably every person knows statistics without having taken a specific statistics course.
This is a practical course where statistics is approached from an intuitive point of view, without the use of mathematical tools.
The course not only introduces the concepts and techniques related to descriptive statistics, but also practices the use of the computer for data analysis.
In short, this is a course where the basic concepts of descriptive statistics are learnt and where these concepts are worked out through practical cases and applied to the analysis of various datasets with the help of the computer.
Requirements for the learning process
The course contains all the elements to be followed, and does not presuppose any previous knowledge in statistics.
No previous mathematical knowledge are required either, except for basic mathematics needed with the most elemental algebraic operations, as well as known formulae manipulation, especially with the inclusion of summations.
The course uses the computer intensively as a tool to support the analysis and interpretation of statistical data. It is supposed that students have previous experience in computing environments, despite the fact that its use for data organization and analysis will be practiced and therefore there are no previous requirements in computing.
Value added for students
This is a basic course to get the necessary competences to support decision taking with the use of facts and data about the economic environment. It is part, therefore, of the sequence of courses that work out the instrumental competences of statistical analysis of real phenomena.
Data Analysis is the first course in statistics. This is complemented later with courses that provide the mathematical fundamentals of statistics, as well as other courses specific to business management and economics.
Course Competences
These are the competences that are worked out in the course:
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Competence type |
Evaluation weight |
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General/Transversal |
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1. Oral and written communication |
2% |
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competences |
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2. Analysis and synthesis abilities |
1% |
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3. Team work abilities |
1% |
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4. Learning by using and |
1% |
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experience |
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5. Application of theoretical knowledge and |
1% |
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analysis tools to real situations |
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6. Abilities to work autonomously |
1% |
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Specific |
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7. Knowledge about numerical and |
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graphical descriptive and data |
10% |
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analysis techniques |
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8. Application of numerical and |
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graphical descriptive and data |
10% |
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analysis techniques |
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9. Use of basic computing techniques |
7% |
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10. Abilities to use the computer to |
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apply the basic numerical |
9% |
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and graphical techniques |
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11. Abilities to apply statistical |
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techniques for problem |
40% |
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solving |
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12. Abilities for searching appropriate |
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sources and data selection |
1% |
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for the course project |
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13. Abilities to communicate to |
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non-expert people professional |
1% |
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reports with the use of |
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statistical data |
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Evaluation
The highest marks possible is 100, to be obtained according to various continuously evaluated activities and a final evaluation. Both the continuous and final evaluation will test the competences acquired during the course.
Evaluation Type
Continuous evaluation
Weekly tests:
Both in class and in seminars short tests and quizzes will be handed to test autonomous work progress during the course. These tests account for 25 points of the final grade, with a minimum requirement of 10 points.
Team project:
This is part of the continuous evaluation. A team project has to be developed and 20 points can be earned. The team project consists of the analysis of a consumption product chosen by the team and the use of statistical information to introduce an alternative product into the same market.
Weekly completion of tasks:
Each week an independent work guide is assigned and is used to practice some of the concepts introduced in class. 5 points can be earned by the successful completion of these tasks.
Final Evaluation
A final exam is written at the end of the quarter where 50 points can earned. The final exam is compulsory and a minimum of 20 points have to be earned.
The exam is composed by 8 practical questions, to be answered during two hours. The first four questions are in a multiple choice format, while the last four questions are open questions.
Criteria to earn the course credits
To earn the credits in the course a minimum of 50 points have to be obtained, while also complying with a minimum of 10 points in the weekly tests and 20 points in the final exam.
This is a summary table of the evaluation criteria:
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Activity evaluation |
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Final evaluation |
Final exam |
50 points of the final grade |
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(at the end |
(a minimum of 20 points |
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of the quarter) |
are needed) |
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Continuous |
Weekly |
25 points of the final grade |
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Evaluation |
evaluation |
(a minimum of 15 points |
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tests |
are needed) |
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Team |
20 points of the final grade |
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project |
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Weekly task |
5 points of the final gradel |
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assignments |
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Total points to be earned |
100 punts |
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(A minimum of 50 points |
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are needed and the minimum |
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requirements for the final |
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exam and for the weekly |
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tests have to be met) |
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Methodology
Course organization
These are the different activities during the course:
1. 10 sessions in large lecture group for the introduction of concepts and its basic applications.
2. 8 sessions in seminar group to practice interactively the different statistical concepts introduced in the course.
3. 10 sessions of autonomous individual work.
4. Autonomous team work and team members interaction.
5. Final exam preparation.
6. Final exam writing
The work load of these different activities is the following:
1. Lectures (2 hours a week during 10 weeks).
2. Seminars (1 hour a week, starting the third week, for 8 weeks)
3. Independent work (suggested time: 4 hours a week)
4. Team work (suggested time: 3 hours a week)
5. Final exam preparation (suggested time: 15 hours before the final exam)
6. Final exam (2 hours)
The following table shows the time that students will dedicate to this course (in average):
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Lecture |
Seminar |
Individual |
Team |
Exam |
Exam |
Weekly |
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Group |
Group |
Work |
Work |
Study |
load |
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Week 1 |
2 |
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3 |
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5 |
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Week 2 |
2 |
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3 |
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5 |
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Week 3 |
2 |
1 |
4 |
3 |
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10 |
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Week 4 |
2 |
1 |
4 |
3 |
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10 |
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Week 5 |
2 |
1 |
4 |
3 |
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10 |
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Week 6 |
2 |
1 |
4 |
3 |
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10 |
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Week 7 |
2 |
1 |
4 |
3 |
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10 |
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Week 8 |
2 |
1 |
4 |
3 |
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10 |
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Week 9 |
2 |
1 |
4 |
3 |
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10 |
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Week 10 |
2 |
1 |
4 |
3 |
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10 |
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Exam |
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15 |
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15 |
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Preparation |
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Final Exam |
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2 |
2 |
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Total Hours |
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(107) |
20 |
8 |
38 |
24 |
15 |
2 |
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Activities schedule
Course calendar
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Week |
Activity |
Resources |
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Continued from the previous page |
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Week |
Activity |
Resources |
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Week 1 |
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Lecture |
Course |
Moore initial section |
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introduction; |
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What is |
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statistics? |
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Week 2 |
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Lecture |
Descriptive analysis |
Moore pag. 6-20 |
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of data |
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Week 3 |
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Lecture |
Test 1: Week 1 i 2 |
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Numerical description |
Moore pag. 30-51 |
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of one quantitative variable |
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Seminar |
Test: OpenOffice Calc; |
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Practice case 1: |
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n |
one numerical variable |
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Week 4 |
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Lecture |
Data tranformation; |
Dossier pag. 1-8 |
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transformation; |
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Numerical summaries |
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of grouped data |
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Seminar |
Test: ODStatistics basics; |
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Practice case 2: boxplot |
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Week 5 |
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Lecture |
Test 2: One numerical |
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variable analysis |
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Computations with |
Moore pag. 51-75 |
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the normal distribution |
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Seminar |
First team project |
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presentation |
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Week 6 |
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Lecture |
Analysis with two |
Moore pag. 97-173 |
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numerical variables |
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Seminar |
Test: normal distribution |
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computations; |
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Practical case: use of the |
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normal distribution |
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in management |
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Week 7 |
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Lecture |
Test 3: Normal distribution |
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Two categorical variables |
Moore page. 173-203 |
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Seminar |
Test: two numerical variables; |
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Practical case: use of |
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regression for management |
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Week 8 |
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Lecture |
Time series |
Dossier pag. 42-69 |
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Seminar |
Second team project |
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Presentation |
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Week 9 |
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Lecture |
Test 4: Two categorical |
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variables and combinations |
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Inequality |
Dossier pag. 9-14 |
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Seminar |
Test: two categorical |
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variables; |
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Practical case: false |
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categorical relations |
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Week 10 |
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Lecture |
Index number |
Dossier pag. 22-41 |
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Seminar |
Third team project |
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presentation |
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Main events
Each week autonomous guided assignments have to be completed.
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Week |
Event |
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1 |
Seminar formation |
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2 |
Team formation and project election |
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3 |
Test 1 in lecture, Test 1 in seminar |
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4 |
Test 2 in seminar |
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5 |
Test 2 in lecture, First team presentation |
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6 |
Test 3 in seminar |
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7 |
Test 3 in lecture, Test 4 in seminar |
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8 |
Second team presentation |
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9 |
Test 4 in class, Test 5 in seminar |
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10 |
Third team presentation |
Teaching resources
1. Textbook: The Basic practice of statistics, David S. Moore 2nd ed. , W.H. Freeman, 2000.
2. Data analysis software: ODStatistics, available to download and in all computer rooms at UPF (running under OpenOffice).
3. Theory dossier prepared by the instructors.