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Program Courses Listing
Business Intelligence, Data Warehousing and Analytic - Basic Intermediate
Robert Tan
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.
Data Analysis - Basic Intermediate
Robert Tan
Data Analysis - Intermediate Advanced
Robert Tan
Machine Learning Including Artificial Intelligence - Basic Intermediate
Robert Tan
Machine Learning Including Artificial Intelligence - Intermediate Advanced
Robert Tan
Certified Executive Program in Data Science
Robert Tan
Given the massive amounts of data that are produced these days, it has become essential that industries adopt data science an integral part of their effort to grow their business, increase customer satisfaction and make better business decisions. Data science deals with vast volumes of data using modern tools and techniques and complex machine-learning algorithms to identify unseen patterns, derive meaningful information, and inform data-driven business decisions. Data Science depend on several technical concepts such as Machine Learning, Modeling, Databases Statistics and Programming. This certification program will discuss the foundations of machine learning and data science and will cover data science methodology and explorations and several important topics such as supervised and unsupervised learning models as well as data pre-processing techniques and data visualization.
Course detail:
- High-level overview of data science and machine learning
- Data science methodology and data exploration for leaders and managers
- Working with data pre-processing and data visualization - data pre-processing and error estimates, metrics for numeric and categorical data, technical standards, the problem with missing values, estimates of error of regression and classification systems, and techniques for feature extraction and projections
- Unsupervised learning models - market basket analysis, recency-frequency-monetary (RFM) analysis, clustering algorithms (K means, self-organizing maps (SOMs), additional topics on clustering)
- Supervised learning models - decision theory and Bayesian learning systems, learning and classification based on instances, induction of decision trees (general principles, discrete-diffraction-transform (DDT) algorithm, others), ensemble classifiers, neural networks (single perceptron, multi-layer perceptron (MLP), introduction to deep learning neural networks), and support vector machines
Certified Executive Program in Statistical User-Centricity
Robert Tan
Globally there is clear trend that National Statistical Offices (NSOs) are moving from a supply driven approach towards a demand driven, user centric approach. This is seen as a necessity because if NSOs cannot provide what governmental requirements, they will move to source those requirements from private sector companies which can provide what they need.
Hence, NSO's are running the risk of becoming redundant if they cannot provide what their main users (decision makers in governments) need. For NSO's this leads to the necessity of deeply understanding the needs of their main users by adapting a user centric approach which is proving to be a huge challenge.
This course identifies and provides solutions for the major challenges of moving towards a user centric approach for official statistics. It focuses on maximizing interactions with decision makers to really understand their needs, ranging from sending out questionnaires on users' wishes and requirements, to infrequent meetings to understand their vision and use cases comprehensively.
Course outline:
• Methodology and Customer Centricity Principles
• Customer Centricity Tools
• Building and Maintaining Customer Insights Engines
Design and Execution of Efficient Data Collection Operations
Robert Tan
This course offers an in-depth look at various data sources and collection methods used in producing official statistics, such as administrative data, sample surveys, and data linking. It addresses the challenges and potential errors in data collection processes. Learners will develop skills to identify the most relevant data sources, choose the best methods, and design processes that minimize errors and tackle challenges effectively.
Learning Objective:
- Introduction to data collection methodologiesThe collection of data with surveys
- Some basic terms used in surveys
- Planning the steps of the data collection process
- Sampling designs
- Sources of error in statistical operations
- Methods and modes of data collection methodologies
- Survey interviewing
- Formulation and classiications of questions
- The collection of data with administrative sources
- Statistics based on administrative information
- Evaluation of the quality of administrative data
- Protection of privacy and conidentiality
- Evaluating survey questions
- Making questionnaires suitable for data processing and analysis
- Managing ieldwork
- Post-collection processing of survey data
- The transfer of administrative information into statistical information