Data Science Foundations in Python

About the course

Výuka probíhá v angličtině. 

The course will be held in English.

Data science combines principles from statistics, artificial intelligence, and machine learning, aiming to extract valuable information and discover patterns in data.

This course builds on the Introduction to Data Science course and provides the essential foundation that every data scientist needs, preparing you for further advanced learning in the field. Through real-world examples, you'll learn to select appropriate methods to solve different problems, understand how they work, and apply them effectively.

Python, known for its versatility and efficiency in data manipulation, plays an important role in data science. You'll learn to formulate hypotheses, test them statistically and understand the principles of regression. You'll also learn the basics of machine learning, understanding classification, text analysis, clustering and visualisation techniques to effectively present your findings.

This course is part of the Data & AI Scientist learning path.

Who is this course for?

Prerequisites:

Did you take Introduction to Data Science course? Good! If not, be aware the following knowledge is required for this course:

• Basic knowledge of statistical methods and concepts essential for data analysis (mean, median, variance, standard deviation, normal distribution).
• Basics of Python (variables, operators, conditions, loops).
• Very basic knowledge of Data Manipulation Libraries such as pandas.
• Awareness of fundamental machine learning concepts and techniques, even if not in-depth.
• English Proficiency: Ability to read, write, and communicate effectively in English, as the course will be conducted in English.
  
  

By the end of the course you will:

• Know how to prepare and pre-process data,
• have the skills for feature engineering,
• be familiar with regression models, clustering and classification,
• have a broad understanding of machine learning principles, 
• be able to interpret and evaluate the quality of prediction models,
• be able to implement models in Python using core libraries such as pandas or scikit-learn.
  
  

Course content

• Python Basics for Data Science
• Data Exploration and Descriptive Statistics
• Regression and Classification Techniques
• Clustering and Dimensionality Reduction
• Time Series Analysis and Forecasting
• Model Evaluation and Validation
• Advanced Topics in Data Science

Please note that the first and last lecture will be given in person in Prague. Except for the first and last lecture, all lectures will be held online.

How do you finish the course?

You will receive a certificate, if you:

• meet the attendance requirements – attend at least 9 of 11 lectures,
• submit the homework assignments. 
  
  

Meet your trainers

Aneta Havlínová currently works as a data scientist in the European Commission, where she focuses mostly on the implementation of large language models to support the work of the Directorate-General for Competition. Previously, she worked in Workday as a python developer/data scientist, contributing to the People Analytics application – a tool that provides clients with automated analytics of HR trends. She also worked as an HR analyst in the Council of the EU and as a data scientist in MSD, where she used her knowledge for example to help lab scientists with biological processes modeling, or provided oncology marketing teams with insights based on financial data. She has a master's degree from the Institute of Economic Studies at Charles University in Prague.

Andrea Štefancová is a graduate of University of Economics in Prague with masters' in Econometrics and Operations Research. She works as a senior data scientist in MSD Animal Health with prior experience from clinical research and quality assurance. Andrea mainly enjoys solving optimization problems. Within her current project, she works on finding optimal safety stocks of animal medicine products in MSD distribution centers. She also has a lot of experience in creating web-based applications in R software, which allows business users an easy access to statistical models developed by data scientists.

Jakub Kantner is a graduate from FNSPE at Czech Technical University in Prague with a master's degree in Mathematical Modelling. He works as a senior data scientist in MSD. In the past he focused mainly on computer vision tasks, nowadays he has shifted focus to the assessment and implementation of LLMs for various tasks. He has lectured several Python courses including a course for kids and a Spotfire Ironpython course.  

Jiří Pešík is a graduate of University of West Bohemia with a masters' degree in Applied Statistics. He works as a Software Engineer at a cyber security company Rapid7 as a part of a team that gathers and processes data about vulnerabilities of network assets. Previously, he worked on an Archeological Map of the Czech Republic, a system for data processing from the weighthing-in-motion system and he worked as a data analyst and IT consultant. He has lectured several Python courses.

Josef Švec currently works as a data scientist in Workday. As a part of his work, he contributes to the development of augmented analytics engine that detects interesting stories and anomalies in human capital data. Moreover, he analyses data about customer adoption and interactions with the product. He has already participated as a coach in two Czechitas courses Python 1 and Introduction to Data Analysis. He has a master's degree in Applied Economics from CERGE economic institute that is a joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences.

Justina Ivanauskaite is an experienced data scientist with a background in statistics. Her expertise lies primarily in econometric modeling, statistics, and simulation. Currently, she is data science lead of Animal Health Advanced Analytics team, which supports research, new product development, manufacturing, and commercial aspects of animal health in MSD. Justina is interested in creating data science solutions with an emphasis on reusability and reproducibility, which delivers value to the client. Justina is interested in creating data science solutions that bring value to the client, with emphasis on reusability and reproducibility.

Martin Koryťák is a data scientist and Python developer at Workday. He is one of the key contributors to its proprietary engine which provides enthralling insights into HR data in a narrative form. Prior to joining Workday, Martin was an IBM Great Minds intern at IBM Research in Zurich, working on accelerated inference of tree-based models capable of handling large-scale data sets. He holds an M.Sc. degree in data science with specialization in artificial intelligence from Czech Technical University in Prague. His interests span algorithms, neural networks and interpretability of machine learning algorithms. He is also a member of the local AI community and an enthusiastic teacher of Python programming language.

Václav Hlaváč is a data scientist working at MSD Animal Health. He graduated with a master's degree in Data Science from Czech Technical University in Prague. He currently works on machine learning projects focused on making use of data collected at livestock farms. His interests include programming, deep learning, and real world applications of machine learning. In his free time he enjoys playing guitar, climbing, or going for an occasional run.

Related courses

• Introduction to Data Science
• Data Science and AI

This course is part of the Data & AI Scientist career learning path.