Data Science – Consulting Approach 2400-ZEWW949
Understanding consulting business model and the role of data science in this ecosystem.
- Project types and related data science engagements.
- Career paths: from management consultants to data roles: data engineer, BI engineer, data scientist, analytics consultant.
- Technology stack
Overview of real data science projects in consulting
Overview of strategy consulting projects
Coding best practices and Git version control
- How to write good code: classes, functions, documentation
- How to work with Git
- GitHub overview and setup
- Importance of GitHub repo to build portfolio
- Pull Requests
- Preparation to work in groups within a shared repository
Data Analysis with Python
- Focus on data processing (pandas), understanding challenges – how to prepare for common data issues.
- Solving a business problem.
- Final product – simple web app
Cloud – practical application
- Main providers and key considerations
- Working with Azure
- Setting up virtual machine
- Deploying web app to VM
Business Intelligence
- Role of Business Intelligence and data storytelling
- Power BI
- Data infrastructure - M language and DAX
- Techniques for building a visually appealing dashboard
Generative AI
- Ethical considerations and confidentiality
- Popular tools (paid vs open source)
- Chat GPT API (or Azure Open AI Services)
- HuggingFace
- Real-life examples of Gen AI applications
Generative AI Roundtable Discussion
- Is AI delivering on its initial hype?
- Main challenges for successful AI implementation (regulatory, certification etc.)
Location analytics in Python
- Real life examples
- Useful APIs and data sources
- State of the art methodologies
Mastering Presentation and Communication
- Why presentation and communication are crucial in business environment
- Strategy for structuring communication
- Presentation techniques and tips
Capstone project with AI
- End-to-end project based on discussed subjects and tools.
- Build data model
- Analyze and collaborate on git
- Leverage AI
- Power BI Dashboard
- Sales pitch
- Strong emphasis on collaboration and communication
Course coordinators
Learning outcomes
After the course participant should be better prepared to work in data related role in business environment. Essential outcome is understanding that good analytics must be explainable. It is not enough to write a working code. Documentation, storytelling, presenting and teamwork are equally important.
Assessment criteria
- Attendance (according to common University of Warsaw rules): 30%
- Capstone project and presentation: 70%
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: