BIPM-Projects

Hey there, here you can find some projects from my Business Intelligence & Process Management studies.

In the following you I will list the course outlines to show what knowledge I've got from my studies so far besides the projects. So, you are also able to see what I've done in courses where I was not allowed to share my semester project here due to the fact that we've worked together with companies and their sensitive data.

Strategic Issues of Information Technology

The module focuses on the interaction between business strategy and information technology (IT): The students shall be familiarized with an understanding of strategic management approaches from a practice-oriented perspective. Based on real life business scenarios, the relevance of IT in the context of strategy development will be discussed. These might be IT enabled business potential (e.g. by using Cloud Computing or mobile solutions) or the influence of IT on strategic decisions (e.g. during M&A Assessments).

The content will be discussed based on real business scenarios. The students have to develop or validate their own IT driven business plans or IT concept. They need to validate a given strategy for an existent company (the “client”) from an Strategic IT perspective.

Learning Content

Part A: Project Management and Customer Interaction

• Project Management and Procedure Models
• Consulting Approaches
• Demand vs. Delivery
• Client Interaction
• Setup of a Consulting Team
• Stakeholder Analysis

Part B: Basics of strategic management

• Basic terms and techniques of the strategic management process
• Analysis of the strategic environment, application to case studies or examples
• Strategic options, strategic choice and methods of assessing strategies
• Strategy-Life-Cycle: Development, implementation, execution and control
• Start-Ups and Entrepreneurship
• Relevance of IT aspects in the classical strategic management discipline: IT as Input and Design area

Part C: IT and Business Strategy

• IT as product (e.g. Software companies, hardware supplier, IT consulting, it service provider)
• IT based Business Models (core processes based on IT, e.g. Online-Shops or direct banking)
• IT as support function (IT as secondary activity, typically bricks and mortar companies)
• IT Governance (especially Business-IT-Alignment)
• Establishment of IT responsibilities within a company
• Strategic IT decisions, e.g. Make-or-Buy, Offshoring, Architecture Management, IT Standards

After completing this course, the students shall be able to:

• Explain the relevance of IT issues against the background of business and IT strategy
• identify central strategic issues in given practical cases and examples
• explain proven standard tools of strategic analysis and development and apply them to specific cases
• recognize and critically reflect on various stakeholder perspectives (informatics as well as business view)
• evaluate IT enabled business opportunities and develop innovative IT based business models
• manage complex client situations.

Data Science

We are drowning in information and starving for knowledge.

With the advent of computers and the information age, statistical problems have exploded both in size and complexity. Challenges in the areas of data storage, organization and searching have led to the new field of "data science"; statistical and computational problems in biology and medicine have created "bioinformatics." Vast amounts of data are being generated in many fields, and both scientists and business majors need to understand how to learn from data. The learning problems that we consider can be roughly categorized as either supervised or unsupervised. In supervised learning, the goal is to predict the value of an outcome measure based on a number of input measures; in unsupervised learning, there is no outcome measure, and the goal is to describe the associations and patterns among a set of input measures. While some mathematical details are needed, we emphasize the methods and their conceptual underpinnings rather than their theoretical properties.

Learning content:

Overview of Supervised Learning

• Linear Models and Least Squares
• Nearest-Neighbor Methods
• Statistical Decision Theory
• Bias-variance tradeoff
• Classification o GAMs, bootstrap

Regression

• Linear/Additive Models.
• T-tests and F-tests

Decision Trees

• Regression Trees
• Classification Trees
• Random Forests

Modern Software

• Python

After completion of the course students should:

• have a thorough understanding of supervised methods such as (linear) regression, decision trees, generalized additive models (GAM), nearest neighbors, Kernel methods.
• fully appreciate and understand fundamental general statistical principles such as type I/II errors, bias-variance tradeoff and shrinkage.
• be familiar with Bayesian ideas and terminology.
• apply validation methods such as cross-validation and the bootstrap.
• be easily able to implement a data science project in a modern language such as python.

Data Warehousing

We are all living in the digital age, where we are faced with a continuous change in technology. As a result we witnessed an explosive growth of the amount of data that is created and we are able to store large amount of data. How can we make sense of these huge mountains of data and how can we enabling better and faster decisions for solving real-word problems?

This course will teach students how to design data warehouse systems, visualize data with dashboards, and steer an organization in a data-driven way with corporate performance management.

Learning Content

• Overview of Business Intelligence Systems
• Business Intelligence Lifecycle Management
• Data and Analytics as a competitive advantage: The data-driven organization
• Corporate Performance Management, Metrics, Key Performance Indicators and Indicator systems
• Organization and Governance of BI - BI Competence Centers - Zgile BI - Management of BI in an Organization
• Data Integration and Data Quality
• Multi-Dimensional Modeling
• Implementing and Administering Data Warehouse Systems
• Techniques for Information Distribution and Analysis - Multidimensional Analysis - Enterprise Reporting - Data Visualization - Dashboards and Portals - Mobile BI
• Process Intelligence and Process Mining
• Platforms for Data Storage and Analysis - Enterprise Data Warehouse and Data Marts - Relational and Multi-dimensional Databases - In-Memory Databases - Big Data
• Usage of BI in - Marketing, Customer Relationship, Sales Management and Web Analytics - Controlling, Finance and Human Resources - Production, Logistics and Supply Chain Management - Strategic and Competitive Intelligence

After completion of the course students should:

• have an overview of the different elements of Business Intelligence
• have gained an in-depth understanding of the conceptual foundations of BI
• be able to choose appropriate Data Warehouse Systems for different business problems
• be able to design and implement Data Warehouse Systems
• have gained hands-on experience with different Data Warehouse Systems by solving practical cases in teams
• be able to perform a process mining project

Business Process Management

The course aims at preparing students for shaping process-oriented companies, which are both technology-centered as well as people-centered.

Learning Content

1. Introducing Business Process Management (BPM)

• Roots of BPM
• Process-Orientation as a Paradigm
• BPM Today

2. Establishing a Methodological Framework for BPM

• Coping with Complexity
• A Hierarchical Model for Business Processes
• A Lifecycle Model for Business Processes
• The Case for Enterprise Architectures

3. Business Process Modeling

• Goals of Modeling
• Levels of Abstraction
• Types of Business Process Models
• Selected Process Modeling Methods

4. Business Process Analysis & Design

• Qualitative Process Analysis
• Quantitative Process Analysis
• Foundations in Process Design

5. Current Trends in BPM

Upon completion of the course the student has gained a sound understanding and hand-on skills in the following areas:

• Thinking and Acting Process-Oriented
• Aligning Organization, People, and Technology
• Differentiating Complexity and Complicatedness
• Understanding IT as an Enabler for Business Process Improvements (Opportunities & Risks)
• Managerial Methods for Modeling, Analyzing, and Designing Business Processes
• Practical Process Modeling (with a State-of the-Art Tool)

Business Process Innovation Lab

Students learn what it takes to „innovate business processes“, and how data can be used as a key enabler for process innovation. Designed as a „lab“, this course focuses on hands-on experiences.

Learning Content

Design Thinking

• Principles
• Procedure and selected techniques
• Applicability in (process) management
• Applicability for business innovation

Agile Management

• Principles
• Procedure and selected techniques
• Applicability in (process) management
• Applicability for business innovation

The "Lean" Paradigm & Customer-centered Work

• How to really take the customer perspective
• How to focus on the things that really count
• How to create a "minimum viable product

Agile work on ”innovation projects”

• A ”hackathon” in 3 sprints
• Innovating processes for real customers (customers are acquired during the semester)

Enterprise Architecture for Big Data

The phenomenon of Big Data and the strong emergence of analytics have changed the business world. Business strategies and their underlying processes are now closely related to data driven transformations. The challenge for companies is to integrate all the different types of data into an appropriate IT-Infrastructure. In this course we will look at and work with the new emerging technologies using massively parallel computation (Hadoop, Spark). Students will learn how to plan and implement enterprise architectures for large scale information systems.

Learning Content

• Designing Data-Intensive Applications
• The role of Big Data in Enterprise Architectures
• Big Data in the decision making process
• Enterprise Architecture and EA Artifacts
• Parallel and Distributed Data Processing
• IT-Architecture for Big Data
• NoSQL
• Hadoop Ecosystem
• Spark
• Stream Processing

IT Security and Privacy

Security threats are becoming more and more important for organisations. Additionally, governments and industry bodies getting descriptive around compliance requirements.

Today security is more than a technical theme. Security is about managing critical and sensitive informations in all kind of organisations.Therefore organizational, technical and legal requirements have to match with business processes. All aspects construct a multidimensional cube which enables a multilayer understanding and reflection of strategic, technical and business impacts as well as risks.

Compliance requirements like standards of IT security, the ISO family, national and international privacy laws reinforce the importance of information security management.

With the increasing use of modern information technology, the aspect of data avoidance is becoming more and more important. Therefore, the topics of the protection of personal data are also discussed.

Learning Content

• Security management ◦ Strategy and risk management ◦ Log management, vulnerability management, identity management, and configuration management ◦ IT-service and incident management
• Security goals ◦ Confidentiality ◦ Integrity ◦ Availability
• synchronous and asynchronous cryptography
• Access tokens
• Network security
• Data backup strategies
• High availability approaches
• Hack lab
• Segmentation and security architecture
• State of today's security systems
• Standards, compliance and controls
• National and international laws
• Security awareness

Text, Web and Social Media Analytics Lab

With the massive growth of unstructured data in companies, analytics are becoming more important. In the "Analytics Lab" students implement an analytics project with unstructured data.

Learning Content

Text Mining

• Text Preprocessing
• Text Representation
• Text Classification and Clustering
• Text Visualization

Web Analytics

• Web Content Analytics
• Web Crawling
• Analyzing Web Structure

Web Usage Analytics

• Web Tracking Tools
• Web Metrics
• A/B Testing
• Business Decisions based on Web Analytics

Social Media Mining

• Main Social Media Sites and Access
• Text Mining Applications in Social Media
• Mining Social Graphs