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 projects, 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 took 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. 
  
  

Related courses

• Introduction to Data Science
• Data Science and AI

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