Postgraduate programme
Statistical Systems specialisation in
Central Banks’ Statistics
with access to MASTER DEGREE programme
Postgraduate programme
Statistical SystemS
specialisation in
Central Banks’ Statistics
Overview The postgraduate programme in Statistical Systems, with a specialisation in Central Banks' Statistics, was developed in a close collaboration between ISEGI-NOVA and Banco de Portugal in order to provide the experts and managers who work with central banks' statistics (either as producers, analysts or users of statistical information) with the fundamental knowledge and skills to the development of their activity. Anchored in the latest techniques and statistical methodologies, the programme places a special emphasis on the collection and compilation of monetary, financial, foreign exchange and balance of payments statistics, as well as any other statistics produced by central banks. This postgraduate programme is supported by the European Central Bank.
Goals
Target
The main goal of this programme is the training of
The programme is targeted at staff of central banks,
managers and experts to:
including producers, analysts or users of statistical
● Develop techniques and methodologies of data collection;
information. The programme is also addressed to all those interested
● Master the tools and processes used for the storage,
in central banks' statistics, particularly employees in
organization and access to information in an entity
statistical departments of banks and other financial
responsible for the production of central banks'
institutions, as well as in national statistical offices and
statistics;
other statistical authoritites.
● Apply the statistical and computational methodologies and tools of exploration and analysis of information, to produce official statistics that can add value to decision making; ● Organize and communicate results, in written form and orally, adapting them to the level and specific interests of the audience.
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Coordinators of the programme ● João Cadete de Matos ● Maximiano Pinheiro ● Pedro Simões Coelho
Study programme The programme offers 16 elective course units, which correspond to 96 ECTS. To earn the postgraduate programme diploma, students complete 60 ECTS, which correspond to 10 course units. Students can enroll in a maximum of 12 course units, total of 72 ECTS.
Course Units (UC)
fall semester
►
Sampling and Estimation * | 6 ECTS
►
Central Banks' Statistics | 6 ECTS
►
Multivariate Data Analysis * | 6 ECTS
►
Databases Management | 6 ECTS
Postgraduate Programme in Statistical Systems
►
Business Intelligence | Datawarehousing | 6 ECTS
►
Management of Statistical Systems | 6 ECTS
►
Computational Statistics I | 6 ECTS
►
Inquiry Methodologies | 6 ECTS
spring semester Postgraduate Programme in Statistical Systems
Course Units (UC) ►
Business Intelligence | Analytics | 6 ECTS
►
People Leadership and Management | 6 ECTS
►
National Accounts | 6 ECTS
►
Financial Report | 6 ECTS
►
Applied Econometrics and Forecasting * | 6 ECTS
►
Research Seminar* | 6 ECTS
►
Computational Statistics II | 6 ECTS
►
Statistical Treatment of Data * | 6 ECTS
*Course Unit mandatory for students that intend to earn a Master degree
The postgraduate programme gives access to the Master Program in Statistics and Information Management, with a specialisation in Information Analysis and Management. To earn the Master diploma, students must present a scholarly thesis or a project work in the third semester, which corresponds to 35 additional credits.
Course Units Content 1. Multivariate Data Analysis ►
Introduction to multivariate statistical analysis
►
Principal component analysis
► ►
►
Data modification: manipulation of variables, observations and SAS data sets
Analysis on common and specific factors
►
Combining data sets
Hierarchical classification
►
Production of reports
►
Some statistical procedures
►
Advanced topics in data modification
2. Sampling and Estimation ►
Introduction to sampling
►
Simple random sampling
►
Stratified random sampling
►
Monetary and financial statistics
Post-stratification
►
External Statistics
►
Complex sampling: sampling in conglomerates and two-stage sampling
►
Other (including central balance sheet, securities, financial accounts)
►
Ratios estimation and subpopulations study
►
Use of auxiliary information
►
►
6. Databases Management ►
Introduction to Information Systems and Databases
►
Databases development: main stages
Business Intelligence
►
Databases planning; system specification; analysis of requirements
Data Warehousing
►
Conceptual modeling: EA model and UML notation
Laboratories:
►
Logic modeling: relational model and normalization
L.1 SQL Server Management Studio
►
SQL language, relational algebra
►
L.2 SQL Business Intelligence Development Studio
►
Physical modeling; development and implementation of databases
►
L.3 SQL Server Integration Services
►
L.4 SQL Server Integration Services
►
Organization and operation of the statistical system
►
L.5 SQL Server Management Studio
►
Planning of the statistical function
►
Management of statistical information systems
3. Business Intelligence – Data Warehousing ►
5. Central Banks' Statistics
►
4. Computational Statistics I
7. Management of Statistical Systems
►
Introduction to the SAS System
►
Management of statistical dissemination systems
►
Data access: read data from structured and unstructured files
►
Statistics quality management
8. Inquiry Methodologies
13. People Leadership and Management
►
Introduction to data collection
►
Motivation
►
Target population and survey basis
►
Communication
►
Nonsampling errors
►
Emotional Intelligence (EI)
►
Nonprobabilistic sampling
►
Leadership
►
Questionnaire design and other methods of data collection
►
Coaching
►
Organizational Culture
9. Business Intelligence - Analytics
14. Financial Report
►
Business Analytics and Data Visualization
►
Data, Text and Web Mining
►
Accounting standards
►
Business Performance Management
►
Reporting of non-financial corporations
►
Information Dashboard Design
►
Recognition and measurement of assets, liabilities and equity
►
Off balance sheet items
Introduction and general framework
►
The reporting of financial corporations
The non-financial accounts
►
Standards and regulatory requirements and oversight mechanisms
►
The financial accounts
►
Analysis, valuation and Risk Management of Financial Instruments
►
Public finances
10. National Accounts ► ►
►
Introduction to index numbers
Introduction - Least squares and basic concepts
►
Statistical/axiomatic approach to index numbers
Panel data methods
►
Productivity indicators
Models of discrete dependent variables
►
Methodologies for seasonality correction
Advanced topics in time series
►
Conversion of time series frequencies
Causal models for treatment evaluation
►
Treatment of anomalous observations (outliers)
Conducting empirical projects
►
Treatment of missing values
11. Applied Econometrics and Forecasting ► ► ► ► ► ►
12. Computational Statistics II ► ► ► ► ►
15. Statistical Treatment Of Data
16. Research Seminar
Topics on data management in SAS
►
Introduction to research work
The ODS - Output Delivery System
►
Topic formulation and research objectives
Linear regression
►
The proposal
Introduction to SAS IML - Interactive Matrix Language
►
The critical review of the literature
SAS Macro Language
►
Methodological approaches in scientific research
►
Presentation of the final work: structure, content, and editing rules
isegi-nova
The NOVA School of Statistics and Information Management (ISEGI-NOVA) is an unit of Universidade Nova de Lisboa. It is a research based school and the first institution in the Iberian Peninsula to integrate iSchools, an international organization that gathers the best universities in research and teaching in the area of information management. ISEGI-NOVA offers bachelor, postgraduate, master and doctoral programs in information management and information systems and technologies. ISEGI-NOVA Master Programs are ranked among the top 8 Best Master Degree Programs in the World according to Eduniversal Masters Ranking 2013-2014. For more information please see: www.isegi.unl.pt/index.asp?lang=EN.
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Postgraduate programME
with access to MASTER DEGREE programme
Statistical Systems specialisation in
Central Banks’ Statistics
ISEGI-NOVA
www.isegi.unl.pt
Academic Calendar and Timetable
Tuition Fees
Option 1 - After Working Hours Schedule
The tuition fee for this postgraduate programme is
The programme lasts for 2 semesters and each course
4.500€. The tuition fee for individual course units is 750€.
unit takes place once per week in sessions of 2 hours.
The addition tuition to obtain the master degree (2nd year
The classes will start in September 2014 and end in
for the development of a scholarly thesis or work project)
June 2015, on an after working hours schedule (after
is 1.600€. There are special discount and forms of
5p.m.), 3 to 4 times a week. Classes run in classroom or
payment for the postgraduate programme. For more
through videoconference.
information, please visit the course webpage.
Option 2 - Intensive Course The programme lasts for 8 weeks, divided in 2 periods
CONTACTS
of 4 weeks each (November and May), with classroom or
If you need more information about the Postgraduate
videoconference sessions. Each course unit runs in
Programme in Statistical Systems with specialisation in
part-time (4 hours), for approximately 2 weeks.
Central Banks' Statistics, contact any of the elements of this program’s team, who will be glad to clear any
Option 3 - Individual Courses Participants may choose to follow individual course units, either by videoconference or in classroom, within
questions you may have. Pedro Simões Coelho, PhD | psc@isegi.unl.pt Program Director
Option 1 (After Working Hours Schedule) or Option 2 (Intensive Course).
Ana Paiva | ana.paiva@isegi.unl.pt Marketing Manager
Gisela Garcia | sa@isegi.unl.pt Academic Services Manager
How to get to ISEGI-nova Carris 701, 713, 742, 756, 758
Metro São Sebastião (Blue and Red Lines) Praça de Espanha (Blue Line)
NOVA School of Statistics and Information Management
4th Edition: 2014-2015
Campus de Campolide, 1070-312 Lisboa Tel. (+ 351) 21 382 86 10 | Fax. (+ 351) 21 382 86 11