Certificate in Management of Enterprise Data Analytics (Big Data)
There is quite a buzz about Big Data and Predictive Analytics these days! Is it really possible to dramatically improve an organization’s performance, across multiple domains, using the huge volumes of data which organizations generate today and which are available from so many external sources? Can we really enhance profitability, streamline processes, improve marketing, accelerate R&D, meet our customers’ needs more effectively – simply by adopting data-driven decision making? The answer is “YES” and the Certificate in the Management of Enterprise Data Analytics is designed to provide learners with the quantitative, technical, business and managerial skills needed to enable organizations to realize these multi-faceted benefits.
Designed to address the growing need for qualified analytic managers and business-minded data scientists, each course in this ground-breaking programme considers the mathematical, technological and managerial / organizational aspects of Big Data in parallel and synergistically. Experienced and highly qualified instructors explain not only the components of analytics, but how a successful data science function is created, nurtured and ultimately managed to provide the greatest possible value derived from data derived insight. This programme will challenge the both business focused and technically minded participants to broaden their horizons, adopt new ways of thinking and embrace the promise of a smarter, better future – a paradigm shift, achievable through data analytics.
In-class or Online
Not at UTM
To complete the certificate programme, 2942, 2943 and either of 2944 or 3030 must be completed. Many students take all 4 courses to deepen their knowledge and understanding. The programme is open to students with an undergraduate degree or college diploma in business, economics, statistics, organizational dynamics, computer science, mathematics, accounting, finance, computer security, engineering or other fields which are rigorous and encompass both quantitative and qualitative aspects. A minimum of 3 years work experience is very highly recommended, and candidates should have a good fluency not only with computer usage, but with technological concepts in general. A previous course in statistics or probability will definitely be an asset, as well.
- This certificate requires the successful completion of 3 of the 4 courses. SCS 2942 and SCS 2943 are required; participants can then choose either the “Management” stream (SCS 2944) or the “Tools and Techniques” stream (SCS 3030) to complete the certificate
- Students have two years from the start date of their first course to complete the certificate.
Jerrard Gaertner, CPA, CA-CISA/IT, CIPP/IT, CGEIT, CISSP, CIA, CFI, I.S.P., ITCP
Larry Simon, MBA, CMC
Certificate Learning Outcomes
- Maximize the potential effectiveness of Big Data exercises by developing detailed insight into underlying business processes and data structures related to the inquiry subject matter;
- Describe various categories of data management/analysis software tools and their respective applicability, strengths and weaknesses;
- Research up-to-date procedures, standards and techniques applicable to data analysis and apply these to an on-going enterprise data analysis project;
- Facilitate the implementation of Big Data derived insights and business process changes through effective communications, training, advocacy and engaging senior management;
- Prepare a preliminary data security and privacy planning document with respect to a proposed enterprise data warehouse, business intelligence (BI) or Big Data initiative;
- Effectively manage an enterprise data-related initiative, including specialized staffing, custom facilities and third party consultants as required;
- Perform at an intermediate level as a data analyst and junior level as a data scientist;
- Solve simple statistical and mathematical problems related to very large data sets;
- Develop intermediate level programmes in selected open source data analytic tools;
- Contribute meaningful input to staff discussions regarding statistical methods, data base technology, knowledge representation, cost effectiveness of tool sets, selection bias and machine learning;
- Work effectively with all three types of data – structured, semi-structures, unstructured;
- Establish data quality standards and train others in this regard.
Who Should Attend
Candidates with the need, interest and/or career drive to enter the world of Big Data.
The programme is suitable for those currently working as business analysts, mathematics teachers, computer programmers, accountants, traders, auditors, researchers, meteorologists, statisticians and others with similar work experience and a good facility with numbers, analysis and computer technology. Mid and senior level managers with excellent organizational skills and a good understanding of enterprise dynamics and change management, who have strong end-user computer skills, are also encouraged to apply.
All candidates should have good to excellent computer skills, as well as some familiarity with probability and statistics prior to beginning the course. Exceptional candidates lacking some or all of these pre-requisites may nevertheless apply to the programme by sending a copy of their resume and educational background to email@example.com.
Estimated workload for the programme is 3 lecture hours per week, plus 8 – 12 hours per week of readings, exercises and project homework.
- This certificate requires the successful completion of all 3 required courses.
- Participants have two years from the start date of their first course to complete the certificate, however extensions will be granted to students requiring additional time due to Certificate changes.
This course provides learners with an introduction and overview of enterprise analytics, Big Data and the many topics which underlie successful development, deployment, management and value creation. Employing lectures, readings, videos, group projects, exercises and class discussion, the course addresses statistics, computer / data architecture, software tools and techniques, computer security, data life cycle, data quality management, IT governance, organizational dynamics, cognitive bias, privacy and staff training. Case studies and examples are used to emphasize the relationship between the parts, and participants are encouraged to work outside of their comfort zones, exploring both technical and managerial aspects of Big Data. Throughout, the goal of value generation is emphasised, although students are also sensitized to the often unappreciated CSR /ethical aspects of predictive research.
This course builds on 2942, particularly in the areas of statistical techniques, software tools and architecture, computer security and privacy, data management and data quality, and organizational change. Important additions include review and validation of statistical predictions, data extraction (ETL), identifying the best candidates for predictive analysis, legislative and regulatory requirements and constraints, nurturing the data science professional, senior management as “client”, methodology, policies & procedures, standards & best practices. The course also entails lectures, readings, videos, group projects, exercises and class discussion, but additionally provides an opportunity for participants to hear and interact with outside expert guest lecturers, vendors and specialized practitioners (i.e. web analytics, fraud detection, CRM, logistics specialists, Hadoop, SAS). More complete and complex case studies and examples are used in the course and participants are again encouraged to work outside of their comfort zones as part of their professional development.
Learners may elect either 2944 Data Management from Enterprise Data Analytics to Data-Based Decision Making [Advanced - Management] or 3030 Big Data Tools and Techniques Mining Financial, Operational and Social Network Data [Advance – Technical] to complete the certificate programme. Many students elect to take both courses, to deepen their knowledge and understanding of the area.
The primary objective of 2944 is to prepare learners to function as competent managers of Big Data / predictive analytic groups or departments. This is accomplished through an intensive course curriculum focused on (a) ensuring that statistical and technical skills (software, architecture, tools and techniques) are appropriately robust; (b) ensuring that knowledge of methods & procedures, IT governance, regulatory requirements, security & privacy, management & organizational change is adequately developed; and (c) applying the knowledge and skills learned to simulated and real world cases and problems to show that participants can use what they have learned effectively, practically, professionally and to the benefit of their respective organizations.
The course is conducted much like an intensive seminar, with substantial weekly readings from multiple reference sources and texts, class discussion in small groups and individually, lectures, guest lecturers, role plays and case work. If possible, optional field trips will be offered, as well. It provides learners with exposure to a range of methodologies and options for implementing Big Data and analytics in an enterprise, with emphasis on finding the best fit between alternate methods, styles, organizational structure, tools and staffing, and building an effective business case. The course also considers in some detail issues regarding data silos, commercial data sources, the value of data, build versus build decisions, Hadoop security, optimization, “solution engineering”, Big Data communications strategies and analytics project management.
This course is for students interested in delving deeper into the tools and techniques of Enterprise Data Analytics and looking to develop hands-on skills with data mining methods. Building on the foundational knowledge of SCS 2942 and 2943, students will learn more of the details of machine learning, how Hadoop and other NoSQL database management systems work, and will be introduced to the Python programming language. Python, along with its many preprogrammed statistical libraries, is an increasingly popular data mining tool. Skills learned will also be transferable to other tools such as R, SAS, etc. A significant proportion of class time is used for hands-on lab work, accessing data available on the Web and mining it for insight.