2010-11 academic year

Data Analysis (21116)

Degree/study: Degree in International Business Economics
Year: 1st
Term: 1st
Number of ECTS credits: 6 credits
Hours of studi dedication: 107 hours
Teaching language or languages: English
Teaching Staff: Walter Garcia-Fontes

1. Presentation of the subject

Data Analysis is a course centered 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.

2. Competences to be attained

These are the competences that are worked out in the course:

Competence type

Evaluation weight

General/Transversal

 

1. Oral and written communication

2%

competences

 

2. Analysis and synthesis abilities

1%

3. Team work abilities

1%

4. Learning by using and

1%

experience

 

5. Application of theoretical knowledge and

1%

analysis tools to real situations

 

6. Abilities to work autonomously

1%

Specific

 

7. Knowledge about numerical and

 

graphical descriptive and data

10%

analysis techniques

 

8. Application of numerical and

 

graphical descriptive and data

25%

analysis techniques

 

9. Use of basic computing techniques

7%

10. Abilities to use the computer to

 

apply the basic numerical

9%

and graphical techniques

 

11. Abilities to apply statistical

 

techniques for problem

40%

solving

 

12. Abilities for searching appropriate

 

sources and data selection

1%

for the course project

 

13. Abilities to communicate to

 

non-expert people professional

1%

reports with the use of

 

statistical data

 

3. Assessment

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.

3.1. EvaluationType

Continuous evaluation

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.

Weekly tests: Weekly tests will be administered both dur­ing lectures (20 points can be earned) and during seminars (20 points can be earned), accounting for 40 points of the final grade. Tests in seminars evaluate mainly competences in the use of computer tools for data analysis which can be only evaluated in seminars, therefore they cannot be substituted by any other activity. Tests in lecture evaluate the continuous learning process during the course, and are similar to the questions to be found in the final exam, therefore they are com­plementary to the final exam.

It is necessary to obtain a minim of 15 points (out of 40)jointly by the lecture and seminar tests. The lowest grade in tests in lectures, as well as the lowest grade in the tests in class, either because of low performance or absence, will be discarded.

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.

Final Evaluation    A final exam is written at the end of the quarter where 40 points can be earned. The final exam is compulsory and a minimum of 15 points have to be earned.

If all lecture tests have been taken (with one absence allowed), the grades in lecture tests will be taken into account only if they improve the average with the final exam, otherwise the final exam will count 60 points (being in this case the minimum required grade 25 points).

The final exam will be composed by 8 practical questions to be answered during two hours.

3.2. 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 the minim grades stated before.

This is a summary table of the evaluation criteria:

Activity evaluation

Final evaluation

Final exam

 

Minimum:

40 points of the final grade (or 60 points of the grades of the lecture tests do not improve the average with the final exam) 

A minimum of 15 points in case the final counts over 40 or 25 in case the final counts over 60

Continuous Evaluation

Continuous evaluation tests 

Weekly task assignments 

Lecture tests

 

Seminar tests 

Minimum

 

 

5 points of the final grade

20 points of the final grade (only taken into account if they raise the final grade)

20 points of the final grade

15 points out of 40 for lecture and seminar tests

Team

project

15 points of the final grade

Total points to be earned

100 points (A minimum of 50 points are needed and the minimum requirements for the final exam and for the weekly tests have to be met)

4. Bibliography and teaching resources

1. Textbook: The Basic practice of statistics, David S. Moore 2nded. , W.H. Freeman, 2000.

2.Data analysis software: ODStatistics, available to download and in all computer rooms at UPF (running under Open Office).

3.Theory dossier prepared by the instructor

5. 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)

2. Seminars (1 hour a week, starting the third week)

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):

 

Lecture Group

Seminar Group

Individual Work

Team Work

Exam Study

Exam load

Weekly

Week 1

2

 

3

 

 

 

5

Week 2

2

 

3

 

 

 

5

Week 3

2

1

4

3

 

 

10

Week 4

2

1

4

3

 

 

10

Week 5

2

1

4

3

 

 

10

Week 6

2

1

4

3

 

 

10

Week 7

2

1

4

3

 

 

10

Week 8

2

1

4

3

 

 

10

Week 9

2

1

4

3

 

 

10

Week 10

2

1

4

3

 

 

10

Exam Preparation

 

 

 

 

15

 

15

Final Exam

 

 

 

 

 

2

2

Total Hours (107)

20

8

38

24

15

2

 

7. Planning of activities

Course calendar

Week

Activity

Resources

Week 1

Lecture

Course introduction;

What is statistics?

Moore initial section

Week 2

Lecture

Descriptive analysis of data

Moore pag. 6-20

Week 3

Lecture

Seminar

Test 1: Week 1 & 2

Numerical description of one quantitative variable

Test: Open Office Calc;

Practice case 1: one numerical variable

Moore pag. 30-51

Week 4

Lecture

Seminar

Data transformation;

Numerical summaries of grouped data

Test: ODStatistics basics;

Practice case 2: boxplot

Dossier pag 1-8

Week 5

Lecture

Seminar

Test 2: One numerical variable analysis

Computations with normal distribution

First team project presentation

Moore pag. 51-75

Week 6

Lecture

Seminar

Analysis with two numerical variables

Test: normal distribution computations;

Practical case 3

Moore pag. 97-173

Week 7

Lecture

Seminar

Test 3: Normal distribution

Two categorical variables

Test: two numerical variables;

Practical case 4

Moore pag. 173-203

Week 8

Lecture

Seminar

Time series

Second team project presentation

Dossier pag 42-69

Week 9

Lecture

Seminar

Test 4: two categorical variables and combinations

Inequality

Test: two categorical variables;

Practical case 5

Dossier pag. 9-14

Week 10

Lecture

Seminar

Index number

Last team project presentation

Dossier pag. 22-41

 

Main events

Each week autonomous guided assignments have to be com­pleted.

Week

Event

1

Seminar formation

2

Team formation and project election

3

Test 1 in lecture, Test 1 in seminar

4

Test 2 in seminar

5

Test 2 in lecture, First team presentation

6

Test 3 in seminar

7

Test 3 in lecture, Test 4 in seminar

8

Second team presentation

9

Test 4 in class, Test 5 in seminar

10

Third team presentation