This is the English version of the Data Science / Analytics script. Work in Progress!
This is a growing collection of data science, data analysis and web analytics information and resources that are used for courses I offer.
Data Analysis/Analytics HAW SS 2018
Data analytics and, as a subset, web analytics have become existential parts in the development and optimization of websites and apps, but also in product development, research and business strategy. The digital transformation of industries will result in more and more data to be produced and in a higher demand for people who are able to translate business problems into data and back to business solutions.
The course will introduce into the basics of data analysis and statistics based on real-life projects which will require students to invest additional time between the course dates. The course will also prepare for the certification of the Digital Analytics Association.
- 24.3./10:00: We will meet in front of the building!
- 5.5. (cancelled)
- 30.6. New date
Please note that you need to be on the participants list in order to take part due to seating limitations.
- You will need to use R or RStudio respectively; please have a look at the tools section about R
- If you have trouble installing R, I can provide access to a RStudio server
- In order to pass the course, you will need to pass tests on EMIL. You will pass all tests by attending or reading this script. Attending has the advantage that you will practice the stuff.
- Please be on time
- Please don’t talk when I am talking; you cannot listen when you are talking, and it is also bad for my voice
- Please leave your mobile in your pockets and don’t chat; I do notice, also if you use Whatsapp on your computer etc. We are going to deal with complex stuff, so you will need to focus.
- Please don’t copy stuff from Wikipedia etc. If you do, you will not pass.
- Phase 1: Understanding the Business Problem
- Phase 2: Acquiring Data
- Phase 3: Analyzing and modeling Data
- Statistics Basics
- Analyzing Data in Google Analytics
- Custom Dimensions
- Pulling Data from the Google Analytics API
- Using R to analyze Google Analytics data
- Basic Data Science Approaches
- Supervised and unsupervised Learning
- Classification and Clustering
- Predictive Analytics
- Support Vector Machines and Neural Networks
- Phase 4: Testing
- Hypothesis Testing
- Google Optimize
- Frequentists vs Bayesian Inference
- Phase 5: Deployment
- Visualizing Data
- Dashboards: Google Data Studio
- Shiny Apps