Courses

Strategic innovation is a process of reinventing or redesigning strategy to drive growth, generate value for the organization and its customers, and to create competitive advantage. It is imperative for organizations to adopt this type of innovation in order to adapt to the speed of technology change. Change management, on the other hand, is required when an organization is undergoing a transformation shaped by strategic innovation. Our Certificate in “Strategic Innovation and Change Management” is designed to enhance learners’ general understanding of strategic innovation and change management, and to appreciate the main stages of the innovation process. Learners will also gain skills to practice the principles of change and knowledge management, and to appreciate the benefits of collective intelligence, and intelligent organizations.

Course outline:

 

• Typologies and main stages of an innovation process
• The major global issues and challenges as catalysts for the innovation process
• Innovation as a key factor for the survival and relevance of a company
• Organizational innovation: analysis and applicability of innovation cases in other industries
• Principles of change management
• Planning for effective change
• Culture, leadership, and motivation: central or contextual factors for change
• Metrics to assess and diagnose the change process
• Managing change: analysis of case studies of change projects
• Principles of knowledge management
• Knowledge work: processes, purposes, and contexts
• Collaboration, social capital, and organizational networks analysis
• Collective intelligence and intelligent organizations

Data used to produce official statistics are collected from various sources using the appropriate methods relating to various data collection methodologies. Whether we are using administrative sources, sample surveys or data linking, the collection process does not come without challenges and certainly is not error-free.  It is thus imperative for researchers to be able to identify the most relevant data source, the appropriate method to use in data collection and should also be able to design the process which reduces the challenges and minimize the errors. Our Certificate in “Introduction to Data Sources and Data Collection Methodologies” course provides insight into the different sources of data, various methods which can be used in data collection and the various challenges and sources of errors associated with data collection.

Course outline:

 

• Introduction to data collection methodologies
• The collection of data with surveys
• Planning the steps of the data collection process
• Sampling designs
• Target populations and sampling frames
• Sources of error in statistical operations
• Methods and modes of data collection methodologies
• Design of questionnaires to complement data collection methodologies
• The collection of data with administrative sources
• Statistics based on administrative information
• Data linkage and integration
• Evaluation of the quality of administrative data
• Protection of privacy and confidentiality

Data used to produce official statistics are collected from various sources using the appropriate methods relating to various data collection methodologies. Whether we are using administrative sources, sample surveys or data linking, the collection process does not come without challenges and certainly is not error-free.  It is thus imperative for researchers to be able to identify the most relevant data source, the appropriate method to use in data collection and should also be able to design the process which reduces the challenges and minimize the errors. Our Certificate in “Introduction to Data Sources and Data Collection Methodologies” course provides insight into the different sources of data, various methods which can be used in data collection and the various challenges and sources of errors associated with data collection.

Course outline:

 

• Introduction to data collection methodologies
• The collection of data with surveys
• Planning the steps of the data collection process
• Sampling designs
• Target populations and sampling frames
• Sources of error in statistical operations
• Methods and modes of data collection methodologies
• Design of questionnaires to complement data collection methodologies
• The collection of data with administrative sources
• Statistics based on administrative information
• Data linkage and integration
• Evaluation of the quality of administrative data
• Protection of privacy and confidentiality

Strategic innovation is a process of reinventing or redesigning strategy to drive growth, generate value for the organization and its customers, and to create competitive advantage. It is imperative for organizations to adopt this type of innovation in order to adapt to the speed of technology change. Change management, on the other hand, is required when an organization is undergoing a transformation shaped by strategic innovation. Our Certificate in “Strategic Innovation and Change Management” is designed to enhance learners’ general understanding of strategic innovation and change management, and to appreciate the main stages of the innovation process. Learners will also gain skills to practice the principles of change and knowledge management, and to appreciate the benefits of collective intelligence, and intelligent organizations.

Course outline:

 

• Typologies and main stages of an innovation process
• The major global issues and challenges as catalysts for the innovation process
• Innovation as a key factor for the survival and relevance of a company
• Organizational innovation: analysis and applicability of innovation cases in other industries
• Principles of change management
• Planning for effective change
• Culture, leadership, and motivation: central or contextual factors for change
• Metrics to assess and diagnose the change process
• Managing change: analysis of case studies of change projects
• Principles of knowledge management
• Knowledge work: processes, purposes, and contexts
• Collaboration, social capital, and organizational networks analysis
• Collective intelligence and intelligent organizations

Critical thinking (the analysis of available facts, evidence, observations, and arguments to form a judgement) and data-driven decision making (the process of using data to inform decision-making processes and validate a course of action before committing to it) are complex subjects which include the rational, skeptical, and unbiased analysis or evaluation of factual evidence.  However, both of these topics have always been at the forefront of the required success skills in the future.  This program will provide the learners with a deeper understanding of critical thinking, help them recognize cognitive biases and barriers to critical thinking and provide them the skills to develop approaches to critical thinking.  It will also provide the guidelines on how to implement structured decision-making processes and start their journey towards building a data-driven organization and culture.

Course Outline:

• What is critical thinking
• The five pillars of critical thinking
• Cognitive biases and barriers to critical thinking
• Critical thinking approaches
• Implementing structured decision-making processes
• Key terms, frameworks, and the data-driven decision-making process
• Data ethics and privacy
• Building a data-driven organization and culture
• Data characteristics and data mining process models
• Technologies and tools employed in data-driven decision-making
• Descriptive analytics and data visualization
• Predictive and prescriptive analytics
• Data-driven decision-making application cases

Geospatial information refers to the information that identifies the geographic location and characteristics of natural or constructed features and boundaries on or about the earth. This technology can be used for scientific investigations, resource management, and development planning. Our Certificate in “Geospatial Information in Statistics” looks at geographic information systems and how they apply to official statistics. It provides learners with the knowledge to describe the nature of geographic information systems across various domains and appreciate case study work on geographic information systems in statistics.

Course outline:

 

• Geospatial statistics in context
• What is geographic information science?
• The nature of geographic information systems (GIS)
• Main knowledge domains linked to GIS
• Origin and evolution of GIS
• Foundations of spatial representation in GIS
• Spatial data structures
• Spatial relationships
• Spatial data infrastructures
• GIS and official statistics – GIS as a basis for storing statistical information, GIS and census operations, and the use of remote sensing for statistics
• Exploration of online examples of GIS applications
• Case study work on GIS in official statistics

Geospatial information refers to the information that identifies the geographic location and characteristics of natural or constructed features and boundaries on or about the earth. This technology can be used for scientific investigations, resource management, and development planning. Our Certificate in “Geospatial Information in Statistics” looks at geographic information systems and how they apply to official statistics. It provides learners with the knowledge to describe the nature of geographic information systems across various domains and appreciate case study work on geographic information systems in statistics.

Course outline:

 

• Geospatial statistics in context
• What is geographic information science?
• The nature of geographic information systems (GIS)
• Main knowledge domains linked to GIS
• Origin and evolution of GIS
• Foundations of spatial representation in GIS
• Spatial data structures
• Spatial relationships
• Spatial data infrastructures
• GIS and official statistics – GIS as a basis for storing statistical information, GIS and census operations, and the use of remote sensing for statistics
• Exploration of online examples of GIS applications
• Case study work on GIS in official statistics

Data quality is an element of increasing importance for users and for the credibility of the statistical systems. Data quality management provides a context-specific process for improving the fitness of data that’s used for analysis and decision making. The goal is to create insights into the health of that data using various processes and technologies on ensuring its quality. Data quality in itself is not the goal but rather a tool to achieve the outcome that depends on that quality. Our Certificate in “Data Quality Management” course provides learners with an understanding of the relationship between the fundamental elements that contribute to statistical activity and quality assessment, mostly based on the recommendations of the United Nations National Quality Assurance Framework. It also provides insights into the linkages between the Fundamental Principles of Official Statistics (FPOS) and the 2030 Agenda for Sustainable Development.

Course outline:

 

• Fundamental elements that contribute to statistical activity
• Fundamentals on the statistical planning and implementation
• Main statistical concepts concerning quality assessment
• Principles Governing International Statistical Activities
• Fundamental Principles of Official Statistics
• Definition of quality in statistics
• Product quality and quality reporting
• Example of national product quality
• Process quality
• Tools for measuring product quality components
• Quality management and quality frameworks
• International standards organization (ISO9001)
• Tools for measuring perceptions of various actors
• Strategic management and policy

Data quality is an element of increasing importance for users and for the credibility of the statistical systems. Data quality management provides a context-specific process for improving the fitness of data that’s used for analysis and decision making. The goal is to create insights into the health of that data using various processes and technologies on ensuring its quality. Data quality in itself is not the goal but rather a tool to achieve the outcome that depends on that quality. Our Certificate in “Data Quality Management” course provides learners with an understanding of the relationship between the fundamental elements that contribute to statistical activity and quality assessment, mostly based on the recommendations of the United Nations National Quality Assurance Framework. It also provides insights into the linkages between the Fundamental Principles of Official Statistics (FPOS) and the 2030 Agenda for Sustainable Development.

Course outline:

 

• Fundamental elements that contribute to statistical activity
• Fundamentals on the statistical planning and implementation
• Main statistical concepts concerning quality assessment
• Principles Governing International Statistical Activities
• Fundamental Principles of Official Statistics
• Definition of quality in statistics
• Product quality and quality reporting
• Example of national product quality
• Process quality
• Tools for measuring product quality components
• Quality management and quality frameworks
• International standards organization (ISO9001)
• Tools for measuring perceptions of various actors
• Strategic management and policy

Quality statistical outputs depend largely on the quality of the data being used and analyzed. One important process required in producing quality statistical output is data treatment. Data treatment means the access, collection, use, processing, storage, retention, sharing, distribution, transmission, transfer, disclosure, security, destruction, or disposal of any business or government data. In other words, a smorgasbord of activities that cover overarching steps from preprocessing to developments with classical and modern tools like data mining, decision trees, and neuronal networks. Our Certificate in “Data Treatment” course provides learners with a broad understanding of data treatment processing steps. Learners will acquire the skill to summarize and visualaize statistics and to become familiarized with data cleaning and feature scaling methods.

Course Outline:

Data pre-processing, pre-processing basics, and pre-processing steps
Introduction to data mining
Data mining techniques
Missing values in source data
Data transformations
Handling sparse data

National statistics offices organise themselves and function in alignment with the statistical value chain and the framework of official statistics. Statistical systems are described as either centralised or decentralised depending on the extent to which responsibility for delivering official statistics across the range of government activities lies with the central institution. A statistical system “works” combining user needs, the framework of official statistics as well as the integration within an international statistical system. Our Certificate in “Management of Statistical Systems” course provides learners with awareness of the importance on the linkage between statistical classifications and respective statistical units in order to integrate them in the dissemination structures. It also helps them understand and combine the relationship among administrative registers, “classical” sources of statistical data and the integration of data from different sources as well as am understanding of how to plan, implement, maintain and control the quality of a statistical operation.

Course outline:

 

• Statistical systems – statistical information needs, the role of official statistics
• Organisation and functioning of the national statistical system, the European and international statistical systems
• Concepts and statistical classifications – statistical harmonisation and international comparability
• Statistical sources – central business registers, combining data from different sources
• Statistical operations – methods and modes of data collection in statistical systems
• Dissemination of statistics – creation and exploitation of statistical databases, publications and statistical dissemination

Big data is a term used to refer to the study and applications of data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.  There are a number of concepts associated with big data: originally there were 3 concepts: volume, variety, velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value. Lately, the term "big data" has been used to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. 

Big data is a term used to refer to the study and applications of data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.  There are a number of concepts associated with big data: originally there were 3 concepts: volume, variety, velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value. Lately, the term "big data" has been used to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. 

The CBIP is the industry's most in-demand credential for Data Professionals, along with the CDP-Certified Data Professional in Data Management.  (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.  BI represents the tools and systems that play a key role in the strategic planning process within a corporation.  These BI systems allow a company to gather, store, access and analyze corporate data to aid in decision-making.
 

The CBIP is the industry's most in-demand credential for Data Professionals, along with the CDP-Certified Data Professional in Data Management.  (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.  BI represents the tools and systems that play a key role in the strategic planning process within a corporation.  These BI systems allow a company to gather, store, access and analyze corporate data to aid in decision-making.