Data Analysis Fundamentals
Machine learning, deep learning, neural networks – behind all these cutting-edge systems lies a single basic building block: data analysis. In order for these technologies to function, you need to produce valid models based on high-quality data. In this course, you’ll learn the foundations of data transformation and analysis and how to apply them.
At a glance
Certificate of attendance "Data Analysis Fundamentals" (2 ECTS)
The first and last event will be held on site at ZHAW near HB Zurich to get to know each other, form project groups, facilitate networking and celebrate the project outcomes of the participants. Intermediate events will be held online for higher efficiency and convenience.
Language of instruction:
22.02.2022 17:30 - 20:00
01.03.2022 17:30 - 20:00
08.03.2022 17:30 - 20:00
15.03.2022 17:30 - 20:00
22.03.2022 17:30 - 20:00
29.03.2022 17:30 - 20:00
Objectives and content
To develop models using machine learning and neural networks, data scientists first need to acquire data, explore data, evaluate data quality then perform data transformation as a pre-requisite to learn from their dataset and produce models from the most basic linear regression models to advanced deep learning models. Without a solid understanding of data and data quality, scientists are unable to develop models or worst, they will produce invalid predictive analysis. This course focuses on the first steps in the data pipeline and provides the necessary foundation to progress in their data science journey.
This course is aimed at decision makers and scientific or engineering personnel such as data scientists, software engineers, researchers or research managers from all fields, who would like to acquire the know-how and skills of state-of-the-art methods to solve decision making problems in order to develop their own RL applications. This course is aimed at professionals who want to orient their careers towards data science.
After completing the module, students will be able to:
- Understand, select when and how to use the main Python libraries for data analysis (NumPy, Pandas)
- Display an advanced understanding of data acquisition
- Explore a dataset and apply data quality techniques
- Perform data transformation for machine learning
- Produce statistical data analysis
- Create data visualizations
- Reflect on the characteristics and suitability of a dataset for ML
The module covers the following topics:
- Getting started with Python and Jupyter notebook
- Introduction to Numpy & Pandas
- Data exploration
- Data cleansing
- Data transformation for ML
- Working with databases
- Visualization with Matplotlib and Seaborn
There are 6 lessons organized once a week on Thursday Afternoon. After 5 lessons the students work on their final project for about two weeks. The final project presentation will be held during the last lesson.
CAS in Digital Life Sciences
This module is part of the CAS in Digital Life Sciences continuing education programme, but can also be attended independently of the CAS. Credit points earned for this module can be credited to the CAS course at a later date, provided the relevant general conditions are fulfilled.
More information here: CAS in Digital Life Sciences
The module will consist of lectures and practical exercises. In addition to lectures, students will be required to self-study selected topics. Students will work in groups on a project and present their results at the end of the course.
- Exercises during the course: 50%
- Project: 50%
Enquiries and contact
Nicolas Vu Huu has 20+ years of international experience in the corporate world and has held executive positions in global financial institutions (Deutsche Bank, Morgan Stanley, Julius Baer, Vontobel) as well as medical devices and implants for hearing care (Sonova Group). He has served in the roles of Head of Engineering then COO at Infonic AG and is currently Head of People Analytics at Bank Vontobel. Nicolas Vu Huu has a pluri-disciplinary education background BSc. Maths & Physics (France), MSc. Computer Science (Polytech, France), Adv. Studies in Finance (NYU, USA), BA in Literature and Sociolinguistics (France) and Adv. Studies in Applied Data Science: Machine Learning (EPFL, Switzerland).
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