Individual presentations topics:

Individual presentations topics:#

A list of presentation topics is given below, and you are required to choose at least one to present: #

Listed in decreasing order of priority, from the most important to the least important:

Priorities

Topics

0

  1. Study of regular expressions
  2. File management modules and methods in Python
  3. Error and exception handling
  4. Advanced Python decorators
  5. Python metaclasses
  6. Python’s Global Interpreter Lock (GIL)
  7. Memory management and garbage collection in Python
  8. Writing efficient Python code with Cython

1

  1. Testing in Python
  2. Version control with Git
  3. Writing unit tests with unittest and pytest
  4. Test-driven development in Python
  5. Continuous integration with Travis CI and GitHub Actions
  6. Profiling and performance optimization in Python
  7. Concurrency with asyncio in Python
  8. Parallel processing with multiprocessing module
  9. Writing thread-safe programs in Python

2

  1. Introduction to NumPy
  2. Introduction to Matplotlib
  3. Vectorized operations in NumPy
  4. Broadcasting in NumPy
  5. Advanced plotting with Matplotlib (3D plots, subplots)
  6. Interactive visualizations with Plotly
  7. Handling missing data with Pandas
  8. Time series analysis with Pandas
  9. Data cleaning and preprocessing with Pandas

3

  1. Introduction to Pandas
  2. Introduction to Seaborn
  3. Introduction to Scikit-Learn
  4. Dimensionality reduction techniques (PCA, t-SNE)
  5. Implementing machine learning pipelines in Scikit-Learn
  6. Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
  7. Building and evaluating regression models
  8. Building and evaluating classification models
  9. Decision trees and random forests in Scikit-Learn

4

  1. Introduction to SciPy
  2. Introduction to Sympy
  3. Numerical optimization in SciPy
  4. Solving differential equations with SciPy
  5. Signal processing with SciPy
  6. Symbolic mathematics with Sympy
  7. Advanced symbolic manipulation with Sympy
  8. Fast Fourier Transform (FFT) with SciPy
  9. Sparse matrices and linear algebra in SciPy

5

  1. Object-oriented programming in Python
  2. Functional programming in Python
  3. Advanced list comprehensions and generator expressions
  4. Iterator and generator patterns
  5. Context managers and the with statement
  6. Abstract Base Classes (ABCs) in Python
  7. Writing Python extensions in C
  8. Implementing data structures in Python (stacks, queues, graphs)
  9. Dynamic programming algorithms in Python
  10. Sorting and searching algorithms in Python

6

  1. The URLLIB module
  2. The BeautifulSoup module
  3. The NLTK module
  4. Web application development with Django
  5. Web scraping with Scrapy
  6. RESTful APIs with Flask
  7. Building GraphQL APIs with Graphene
  8. Introduction to FastAPI for web development
  9. Asynchronous web applications with aiohttp
  10. Deploying Python web applications with Docker

7

  1. Data visualization with Altair
  2. Advanced geospatial analysis with Geopandas
  3. Image processing with Pillow
  4. Natural Language Processing (NLP) with spaCy
  5. Machine learning with TensorFlow and Keras
  6. Deep learning with PyTorch
  7. Recurrent Neural Networks (RNNs) in Python
  8. Convolutional Neural Networks (CNNs) in Python
  9. Reinforcement learning with OpenAI Gym
  10. Time series forecasting with Prophet

Optional:#

  1. A tour of Markdown in a Jupyter notebook
  2. Creating custom Python libraries and distributing them via PyPI
  3. Automating tasks with Celery
  4. Building CLI applications with Click
  5. Using Jupyter notebooks for interactive data exploration
Contents