This course addresses the organizational elements of the Data and Business Analytics (including cognitive computing and robotics process automation) functions by focusing on the management, structural/reporting, and human resource/skills considerations of data and business analytics. Topics such as determining where the group(s) should report, how they are assessed/measured, the necessary skills and how to source them, key data/analytics/cognitive computing processes, data governance, how to lead data-driven innovation in products and services, IT and non-IT roles, and customer and competitor alignment, all driven by the demand to improve the quality and speed of business decisions, minimize the risks/challenges for implementing them, and how to leverage data as a strategic asset. By concentrating on IT’s data, analytics, and cognitive computing responsibilities, in essence this course puts the candidate in the role of the CAO/CDO (Chief Analytics Officer/Chief Data Officer) as they define the vision, strategies, missions, and build the management processes and organization/skills necessary to deploy these data driven initiatives. The course focuses on the important organizational structure in terms of separate or combined organizations, and placement within the overall enterprise and IT organizational structures. This course is geared for managers and consultants engaged in building and growing this organization, including CIOs and non-IT executives to help prepare the enterprise to leverage their investment in Big Data/BA. It combines the optional Building & Managing the Analytics Organization and Building & Managing the Data Organization courses (E & F) below.
This course addresses the business digital transformation underway that are being driven/enabled by the changes in design and management of data for business intelligence/business analytics (BI/BA) and cognition systems as enterprises evolve to leveraging Big Data (and Internet of Things). It focuses on the emerging data sources (e.g., social, mobile, robotics process automation), data models, IT data management processes, and data integration considerations as they pertain to BI/BA and cognitive computing (from marketing to human resources).
The goal is to raise thought-provoking technical issues prompted by the rapid evolution of business and data technologies, as well as to provide practical information for immediate use. The course is organized around the following transformational themes:
- Data Sources
- Data Technologies
- Data Applications
- Infrastructure Considerations
The emphasis is on the industry considerations resulting from the integration of emerging data/knowledge, analytics, and cognitive technologies.
This course will focus on providing candidates with a well-grounded understanding and appreciation of the contemporary methods, tools and techniques used to make analytics an integral part of managerial decision making. It will concentrate on the approaches for realizing the hidden knowledge in corporate databases and will help participants make near-real time intelligent business and operation decisions. The course will introduce various types of analytics including: reporting/visualization, predictive/data mining, decision-making/prescriptive analytics, pattern recognition, and forecasting. Methodological and practical aspects of knowledge discovery algorithms will also be covered including: data preprocessing, k-nearest neighborhood algorithm, machine learning (e.g. decision trees, artificial neural networks), predictive modeling, cognitive computing, clustering and market segmentation, association rule mining techniques, and time series forecasting. The focus of this course is on understanding the potential of these analytical techniques in various organizational settings.
Select At Least One (1) From The Following:
I. Knowledge & Discovery Approaches *
This course follows the Analytics Applications and Techniques Course, and will focus on the hands-on application of data mining, text mining, cognitive computing, artificial intelligence, and big data products/tools/software in solving real world business and operational problems. A variety of popular knowledge discovery software products (both professional/industrial and free/open source) will be used to demonstrate a wide range of interesting application scenarios. This course will provide participants with an in-depth understanding of the tradeoffs that exist in identifying, designing and implementing knowledge discovery projects. It concentrates on building hands-on skills to apply appropriate techniques to discover hidden knowledge in corporate and external databases (both structured and unstructured) to help managers make near-real time intelligent strategic and operational business decisions. The main goal of this course is to provide candidates with not only a well-grounded understanding and appreciation of the methods and methodologies but also help candidates develop hands-on experiences in applying them to real world problems and data sets.
II. Deploying Blockchain Technologies *
As Blockchain emerges as an essential technology across every industry (well beyond just Bitcoin), with all of the buzzwords flying around it can be difficult to separate Blockchain hype from business reality. This foundational technical course will enable IT candidates to understand the essential concepts of the distributed ledger, relevant Blockchain terminology, real world Blockchain use cases, and technology management considerations for carrying out Blockchain projects.
This course will also explore the concept of anonymous consensus and how it is essential to ensuring that the blocks in a Blockchain contain the single version of the truth, as well as learning the mechanics of Blockchain validation and how consensus can eliminate errors that otherwise require reconciliation. How identities work inside of Blockchain and the dependencies that Blockchain Oracles have on Smart Contracts are covered in detail.
Specific and generic industry examples and emerging applications and blockchain technologies (e.g., Bitcoin, Ethereum, Ripple, The Hyperledger Foundation which is actually 6 Blockchain’s including Fabric from IBM, Multichain, EOS, Corda), and approaches for deploying blockchain will be the focus of this course. At the end of this course candidates will be prepared to engage in the technical management and development responsibilities necessary to effectively and efficiently implement blockchain initiatives.
III. Deploying AI Technologies *
While having a relatively long history, Artificial Intelligence (AI) is still actively evolving to where it is now emerging as an essential technology across every industry.
This foundational technical course will enable candidates to understand the essential concepts for implementing AI initiatives. Upon completion of this course candidates will be competent in Machine Learning concepts, AI Techniques, Cognitive Computing, and Deep Learning techniques using Python (the open-source software/programming library designed to conduct research and build solutions in machine learning and deep neural network structure; alternative programming languages/products will also be covered).
The focus of this course will be on assimilating the concepts of Machine Learning and Deep Learning with relevant industry specific algorithms, to build artificial neural networks and traverse layers of data abstraction, and to understand the power of data in the candidates’ new role as a Technical AI professional. The concepts of Neural Networks, Artificial Neural Networks, Natural Language Processing and working with libraries like NLTK, MatPlotlib, TFlearn, Keras &Tensorflow, along with current and emerging industry projects, will also be covered. Specific and generic industry examples and emerging applications and AI technologies, and approaches for deploying AI will be the emphasized throughout this course.
At the end of this course candidates will be prepared to engage in the technical management and development responsibilities necessary to effectively and efficiently implement AI initiatives.
IV. Deploying Robotics Process Automation Technologies
(also consider courses from the Business Process Management Certificate)
Robotic process automation (RPA) is a fundamental technology in the reformation of all back office and front office business processes. As organizations leverage RPA, expertise in the technical and management considerations for deploying and supporting these RPA software robots to automate tasks has become essential. The purpose of this course is to prepare IT professionals, including business analysts, business intelligence developers, data or solutions architects, and system integrators, with the current and emerging tools and practices, to ensure successful RPA deployment across the enterprise. Candidates will be primed to create and launch a RPA implementation plan for their organization.