Providing Technology Training and Mentoring For Modern Technology Adoption
This 2-day course examines and establishes the fundamental theories, concepts, domains, techniques and terminology that are essential for every business and information technology professional who is involved in data warehousing. Students are introduced to best practice approaches and structures for data warehouse development and implementation. Common definitions and characteristics of data warehouses, data warehouse architectures, readiness issues, incremental data warehouse project planning, data warehouse strategy and mistakes to avoid all receive special emphasis.
This course concentrates on data warehouse deliverables independent of any specific methods, but within the framework of best practices. It focuses on understanding deliverables that may be produced throughout the data warehouse process and issues reasons for producing them. This course closes with exploration for practical next steps the students can take. This includes steps further to develop knowledge and skills, to position oneself for success, and to get started with data warehousing.
This workshop focuses on the full end-to-end process of the data warehouse, namely, Gather, Store and Deliver.
This 3-day course introduces students to best industry practices for designing data warehouse data structures and databases.
This 3-day course introduces experienced students to best industry practices for dealing with difficult data warehouse data structures, databases and processes.
This class is for experienced data warehouse architects and database designers. If you already have hands-on experience and want to refine your data warehousing skills, this is the class for you. The class will describe the most challenging data warehouse design problems the world of data warehousing has faced. You’ll also have the opportunity to share experiences gained on previous projects with other attendees. This class is intense and fast-paced.
This one day interactive seminar addresses the critical issues data and information management professionals will encounter in a SOA environment. It will define the five layers of SOA and the data management skills, methods and models essential to each layer.
This seminar uses proven techniques to perform an ISP project and to measure the success of its end results. ISP is broken into a set of major tasks; within each task, a set of steps is performed to achieve the desired deliverable. Each of the steps has a simple and easy to understand example. A case study is used to provide practical experience.
This course is about taking knowledge of the business and its rules and converting these into a stable data model. The data model is a representation of the objects that the business uses, the characteristics of those objects and the rules that govern their relationship.
This is a workshop and relies on realistic exercises. You will learn how to apply leading edge data modeling principles to practical and difficult modeling situations. All of the exercises have been derived from real modeling situations.
The workshop provides a full discussion of and experience with database design. Three types of trade-offs to the data will be applied – technology, safe and aggressive tradeoffs. Safe compromises are trade-offs that will optimize the data model without compromising integrity or redundancy. An example of a safe trade-off is to partition a table. Aggressive compromises will optimize the data but may comprise data integrity. An example of this is to store redundant data. Technology trade-offs use overhead features of the DBMS, such as indices. Indices can improve query performance but can compromise maintenance performance. You will learn how to apply safe, aggressive and technology trade-offs for optimizing the data.
The workshop divides design into Preliminary Design, External Design and Internal Design. Preliminary Design accomplishes the transition from analy-sis and determines the technical architec-ture of the system. It also decides what processes and data will reside on which processors. External Design is design from the viewpoint of the user. It defines dialogue flow and provides screen and report design. Internal Design is design from the viewpoint of the builder. It op-timizes the data, structures the logic and defines interfaces.It uses the same case study as BAA. You complete the course with experi-ence in design.The workshop provides a full dis-cussion of logical data base design. You will learn how to apply safe and aggres-sive trade-offs for optimizing the data. Safe trade-offs optimize the data without compromising integrity and redundancy. Aggressive trade-offs optimize the data while providing some compromise of integrity or redundancy.
This seminar provides an improved solution to the problems caused by the explosion in the demand for information and the problems resulting from inadequate systems development productivity. Also discussed will be the benefits to be gained by adopting a rapid but rigorous and integrated approach to developing systems that satisfy business needs and objectives.
This course is custom designed to review the Administration of DB2 version 9.7 for LINUX, UNIX and Windows. This course has 4 major sections; basic administration, advanced recovery, “performance and tuning” and SQL Procedures. DB2 commands and SQL will be used to focus on relevant DB2 activities. DB2 administration functions and utilities will be reviewed and applied in workshops. Performance and tuning issues will be reviewed and applied in workshops. Snapshot commands and administrative views will be used to gather performance data. In addition the design and use of stored procedures will be reviewed. GUI tools may be used to create, test and debug procedures. Design issues for functions and stored procedures will be reviewed and applied in workshops. EXPLAIN tools can be used to analyze SQL access strategy. The impact of index design will be reviewed. The Developers Workbench or IBM Data Studio may be used in workshops.
This course, designed for business analyst, marketing and business development staff, uses a case study approach to teach the skills used in data based decision-making. Participants learn how to: prepare strategies for gathering information; identify underlying issues related to a decision; generate and evaluate multiple alternatives; communicate recommendations and design plans to implement decision.
This course provides an overview of various data mining techniques with examples of how they are used in various organizations such as retail, finance, biotechnology and social media. Case studies are used to allow participants to work through several data mining issues using the techniques described.
An increase in the volume and velocity of data has spurred a need for new techniques to understand and convey its meaning. Data visualizations are graphical means of summarizing and describing data. This course describes various types of visualizations and teaches participants how to link they data they have and the message they want to deliver to the appropriate visualization.
Participants learn the skills required to conduct projects in the three main areas of a data quality – profiling, assessment and cleaning and process improvement. Data profiling skills focus on understanding the data available. Assessment and cleansing expands upon traditional definitions and incorporates multiple perspectives of data quality. The final section of the course has participants apply a proven process improvement approach to data with the intent of learning how to create ‘good’ data instead of cleaning ‘bad’ data.
Exploratory Data Analysis (EDA) is an approach to data analysis that creates an understanding of the important characteristics of a data set. As such, it is a critical first step in any decision making process. The purpose of this course is to provide a systematic method for looking at data and extracting the patterns that are contained in the data. To do that, participants will learn an approach and a set of methods that can accommodate a wide variety of data.
In 2006, Tom Davenport wrote an article for Harvard Business Review titled Competing on Analytics. Even a few short years ago, Data Analytics was a specialized skill set. Since then much has changed. New technologies can handle the volume, velocity and variety of Big Data, data mining techniques have improved, and tools have the capacity and capability to move analytics from specialist to business users. In turn, companies are leveraging these abilities to move from intuition to data-based decision-making. This course is designed to bring managers and analyst up to speed with the current thinking and techniques used to guide data analytics projects.