This learning path provides a comprehensive journey into Python programming, starting with foundational concepts and progressing to advanced applications. Learners will master core Python syntax and then delve into specialized areas such as statistical analysis, machine learning, data visualization, and business analytics using Python. This path is designed to equip learners with the skills to not only code effectively in Python but also apply it to real-world data-driven challenges.
Master fundamental Python syntax, data types, and control structures. Develop and apply functions and object-oriented programming principles for modular code. Utilize Python for statistical analysis, including data manipulation and hypothesis testing. Implement core machine learning algorithms and models using Python libraries. Create effective data visualizations to communicate insights using Python. Apply Python programming to solve business analytics problems, including data processing and reporting.
Python is an open-source, easy to learn programming language. This skill will teach you how to setup and code in Python programming language.
Developed with the contribution of the OEduverse Erasmus Plus Project. www.oeduverse.eu
This skill helps you understand the main concepts of Statistics and how to implement them in the Python language. Developed with the contribution of the OEduverse Erasmus Plus Project. www.oeduverse.eu
Machine learning is a hot topic these days. This course teaches you the most important topics in Machine Learning and how to implement them in the Python language.
This learning path provides a comprehensive introduction to text mining techniques, focusing on practical implementation using Python. It covers essential steps from text preprocessing, such as lower case conversion, punctuation removal, stopword elimination, tokenization, stemming, and lemmatization, to advanced topics like Bag-of-Words, TF-IDF, Part-of-Speech tagging, Word2Vec, Doc2Vec, sentiment analysis, Latent Semantic Analysis, and Latent Dirichlet Allocation. Learners will gain hands-on experience with various text mining concepts and their application in Python.
Understand the fundamental concepts and applications of text mining.
Apply various text preprocessing techniques, including lowercasing, punctuation removal, stopword elimination, tokenization, stemming, and lemmatization, using Python.
Implement and utilize Bag-of-Words and TF-IDF models for text representation.
Perform Part-of-Speech tagging on text data with NLTK.
Understand and apply word embedding techniques like Word2Vec and Doc2Vec.
Conduct sentiment analysis on text data using Python.
Explore and implement topic modeling techniques such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).
Due to increase in the amount of data we use in our daily activity, Data Visualization is getting more and more important these days. In this skill, you will learn to visualize your data in the Python language.