1. Paper Title: Introduction to Python for Data Science
- Link: [https://www.analyticsvidhya.com/course/introduction-to-python-for-data-science/](https://www.analyticsvidhya.com/course/introduction-to-python-for-data-science/)
- Description: This paper introduces the basics of Python programming with a focus on data science applications. Topics covered include data types, control structures, functions, and modules. The paper also covers fundamental data analysis and visualization techniques using Python libraries such as NumPy and matplotlib.
2. Paper Title: Object-Oriented Programming in C++
- Link: [https://www.coursera.org/learn/object-oriented-programming](https://www.coursera.org/learn/object-oriented-programming)
- Description: This paper covers the concepts and techniques of object-oriented programming (OOP) using C++. Topics include classes, objects, encapsulation, inheritance, polymorphism, and virtual functions. The paper also provides hands-on experience with OOP through programming exercises and projects.
3. Paper Title: Data Structures and Algorithms
- Link: [https://www.geeksforgeeks.org/data-structures-and-algorithms/](https://www.geeksforgeeks.org/data-structures-and-algorithms/)
- Description: This paper covers fundamental data structures and algorithms, including arrays, linked lists, stacks, queues, trees, and graphs. It explores the implementation and analysis of these data structures and algorithms, focusing on their time and space complexity. The paper also discusses real-world applications of these concepts.
4. Paper Title: Introduction to Machine Learning
- Link: [https://www.edx.org/course/introduction-to-machine-learning-3](https://www.edx.org/course/introduction-to-machine-learning-3)
- Description: This paper provides an introduction to the field of machine learning. Topics covered include supervised and unsupervised learning, linear and logistic regression, decision trees, clustering algorithms, and neural networks. The paper also covers practical tips for building and evaluating machine learning models.
Remember, these papers are just a starting point, and you might find other resources that better align with your learning preferences and objectives.