While it is important to understand how to leverage your organizations data/information assets (from marketing to research to talent analytics), IT and business partners must effectively work together to recognize what questions need to be asked. This certificate combines the technical, managerial, and industry skills necessary to deploy this important new technology. Based on the candidates background and anticipated engagement in BI, this program can help prepare the novice or expand the knowledge of an experienced BI professional; as well as the non-IT executive interested in understanding how to leverage this important technology.

The Global Institute for IT Management (GIIM) has developed two 4-course certificate programs to address these important considerations. One, Deploying Analytics (described here) is similar to many university IT analytics programs that are being offered; albeit with a stronger focus on industry and practical considerations. The second, Managing Data as an Asset Certificate, focuses on the leadership, management, and industry skills necessary to leverage these important new technologies; how to derive value from data.

Data Positions and Careers

The courses in this certificate focus on managing the technical considerations for implementing and integrating the information technologies that are required to have a successful/valuable big data and business analytics/knowledge management strategy across the enterprise, including robotics process automation, Cognitive Computing, AI (Artificial Intelligence), Blockchain, IoT (internet of things), Bring-Your-Own-Infrastructure, SMAC (Social, Mobile, Business Analytics, and Cloud), and security.

Candidates Should:

• have completed the course Introduction to Data or have the equivalent experience prior to taking courses in this certificate.
  

• also consider courses from the following Certificates:
• Managing Data as an Asset
• The Essentials of Data Management
• Digital Industry
• Cyber Security Management
• AI
• Blockchain
• Digital Marketing

Select at least 4 courses from the following:

• Courses 1 - 3 are required
  
• All courses are available face-to-face   and synchronously online
  
• Courses marked with an * are also available asynchronously via the GIIM Cloud/Web

1. Building the Data/Analytics Organization *

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.

2. Managing the Data Transformation *

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.

3. Analytics, Applications & Techniques *

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 trade-offs 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 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 *

McKinsey

Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand.  While AI having a relatively long history,  it is now emerging as an essential technology across every industry.  Artificial intelligence (AI) is an academic term that has been seized upon by the media, marketing departments and commentators as shorthand, and to add narrative spice. The now-dominant AI term includes physical and software robots and tools including ‘robotic process automation’, ‘cognitive automation’ and ‘artificial intelligence’. 

The field requires broad training involving principles of computer science, cognitive psychology, and engineering. If you want to grow your data scientist career and capitalize on the demand for the role, these courses are for you. 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.

Appreciating how enterprises automate services using a variety of automation technologies is at the core of this courses. The array of available automation products described include scripting tools, software robots, robotic process automation, artificial intelligence, desktop automation, cognitive computing, business process management automation, and machine learning, to name a few. Understanding how these tools worked, the type of data used as input, how they processed data, and the type of results produced are fundamental.

Recognizing the difference between Robotic Process Automation (RPA) and Cognitive Automation (CA; which people commonly call artificial intelligence/AI) and the impact they can have is essential. The realm of RPA consists of tools that automate tasks that have clearly defined rules to process structured data to produce deterministic outcomes. A ‘software robot’ is configured to process tasks the way humans do, by giving it a logon ID, password, and playbook for executing processes. RPA tools are ideally suited for automating those mindless ‘swivel chair’ chores performed by humans, like taking structured data from spreadsheets and applying some rules to update an ERP system. RPA tools ‘take the robot out of the human’, meaning that the tedious parts of a person’s job could be automated, leaving the human to do more interesting work that requires judgement and social skills. Automation Anywhere, Blue Prism, and UiPath are the top RPA providers by market share.

The realm of cognitive automation (CA) consists of more powerful software suites that automate or augment tasks that do not have clearly defined rules. We do not like to call such software ‘Artificial Intelligence’ because we believe the AI label aggrandizes what these tools do. With CA technologies, inference-based algorithms process data to produce probabilistic outcomes. A variety of tools are in the realm of CA, such as tools that analyze data based on supervised machine learning, unsupervised machine learning, and deep learning algorithms, backed by powerful computing and memory. The input data is often unstructured, such as natural language, either written or spoken. Google’s Machine Learning Kit, IPsoft’s Amelia, IBM’s Watson suite and Expert Systems’ Cogito are examples of CA tools.

Candidates will be primed to create and launch an RPA implementation plan for their organizations.

A. The Essentials of Data Management/Python
          The Introduction to Data course or its equivalent is a prerequisite for all courses in this certificate.

B. Industry Specific Courses

While having experience with data management, statistics, modeling and BI tools is recognized as being fundamental, industry expertise is also considered essential in being able to have a successful career in Business Intelligence/Big Data. GIIM has courses in the following industries to help prepare candidates with the requisite industry expertise: Finance, Pharmaceutical, Healthcare, Manufacturing, Hospitality, Government, Telecommunications, Petroleum, Retail, Insurance, Transportation, etc.

C. Advanced BI

Candidates that have experience (1-3 years) in BI/Big Data projects are often preparing for more arduous initiatives. This courses focusing on the more complex cutting edge approaches to BI/Big Data.

D. Statistics Fundamentals

The significant amount of corporate information available requires a systematic and analytical approach to selecting the most important information and anticipating major events. Statistical learning algorithms facilitate this process for understanding, modeling, and forecasting the behavior of major corporate variables. This course prepares candidates that do not have the important foundation of statistics.

This course introduces time series, and statistical and graphical models used for inference and prediction. The emphasis of the course is in the learning capability of the algorithms and their application to several business areas.

The course also provides an understanding of the basic methods underlying multivariate analysis through computer applications using regression/multivariate analysis.

Topics covered include principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, and other methods used for dimension reduction, pattern recognition, classification, and forecasting.

Participants should have a basic knowledge of probability theory, and linear algebra prior to taking this course.

E. Building & Managing the Analytics Organization

In essence it takes a similar perspective as course f below, but instead of focusing on the role of the CDO (Chief Data Officer), it focuses on the role of the CAO (Chief Analytics Officer).

This course addresses what the Analytics and Cognitive Computing functions should look like by focusing on the management, organizational, and human resource considerations for leveraging analytics. It addresses the emerging job roles of data governance, data stewards, data curators, data scientist, master data architects, data security & privacy, data engineers & architects, and data scientists, as well as centers of excellence/ competency. Managing data as an asset requires significant transformation at many companies. There are cultural issues that must be dealt with, and learning how to manage transformation is a critical skill. Topics such as where the group should report, how they are assessed, the necessary skills and how to source them, key data/analytics processes, integration strategies, data governance, data-driven innovation in products and services, data security/privacy and standards, IT and non-IT roles, customer and competitor drivers, and understanding how the preceding can be used to improve the quality and speed of business decisions and processes, and the risks/challenges for implementing them to leverage data as a strategic asset are fundamental. By concentrating on ITs data and analytics responsibilities, in essence this course puts the candidate in the role of the CAO (Chief Analytics Officer) as they build the management processes and organization/skills necessary to deploy these data driven strategies.

F. Building & Managing the Data Organization

In essence it takes a similar perspective as course e above, but instead of focusing on the role of the CAO, it focuses on the role of the CDO (Chief Data Officer). 

This course addresses the role of a “Chief Data Officer” (CDO) in an enterprise. The course focuses on the management, organizational, and human resource considerations for data and analytics. It addresses how big data assets fits with other information assets of the firm, and the emerging job characteristics of data governance, data scientists, master data architects, and data security and privacy. The key is how organizations can leverage information assets to provide demonstrable business value. 

Managing data as an asset frequently requires significant transformation at many enterprises including cultural and political considerations and learning how to manage the complexity of change. Topics include the alternatives for where the position should report, the necessary skills (executive and staff, IT and non-IT), governance processes, and defining an appropriate set of strategic, tactical, and operational objectives. By considering information assets from an organization-wide perspective, in essence this course puts the candidates in the role of Chief Data Officer as they build the management processes and organization/skills necessary to get the full advantage from data.

Technical Training in Data