Python Developer: From Basics to Case Studies Specialization

This course offers a hands-on, case study-driven introduction to classical and modern cryptography using Python. Through a progression of real-world cipher implementations, learners will understand foundational encryption principles, apply cipher algorithms programmatically, and analyze vulnerabilities in both classical and modern encryption schemes.

Starting with basic reverse and Caesar ciphers, the course advances through brute force attacks, transposition techniques, and affine-based cryptography before culminating in public key cryptosystems like RSA. Learners will gain practical experience in building encryption and decryption tools, evaluating cryptographic strength, and creating secure systems using libraries like PyCrypto. By the end of the course, learners will be able to construct, experiment with, and critically evaluate cryptographic systems for secure communication using Python programming, while also demonstrating fluency in key cryptographic concepts such as hashing, key generation, and symmetric vs. asymmetric encryption.

This hands-on course equips learners with the practical knowledge and technical skills to develop, implement, and evaluate a sentiment analysis model using Python. Beginning with an introduction to sentiment analysis and its real-world applications, learners will explore and identify appropriate tools including IDEs and essential libraries used in natural language processing (NLP).

As the course progresses, learners will analyze the use of various algorithms suitable for sentiment classification and gain experience in constructing a full analysis pipeline—from data preprocessing and cleaning to model training and evaluation. Each lesson is crafted to reinforce applied learning, enabling participants to demonstrate mastery through building a working sentiment analysis system capable of classifying textual data based on emotional tone. By the end of the course, learners will be able to: • Identify key concepts in sentiment analysis. • Select and configure appropriate tools and libraries for text classification. • Implement code for data cleaning, transformation, and feature extraction. • Train and evaluate machine learning models for sentiment classification. • Assess model performance using standard evaluation metrics. This course is ideal for learners with basic Python knowledge who want to delve into NLP and machine learning through a practical, project-based case study.