semasaf/data-science-using-python

This Git repository serves as a comprehensive resource for Data Science using Python. It covers a wide range of topics, from data cleaning and exploration to machine learning and model deployment.

Data Science using Python

This Git repository serves as a comprehensive resource for Data Science using Python. It covers a wide range of topics, from data cleaning and exploration to machine learning and model deployment.

1. Learn Python Basics

• Understand data types, loops, and functions in Python.

2. Master Libraries

• Gain proficiency in essential libraries:
  • NumPy for numerical operations
  • Pandas for data manipulation
  • Matplotlib for basic data visualization

3. Explore Data Cleaning

• Handle missing data, outliers, and clean datasets effectively.
• See here in details Link

4. Statistical Concepts

• Learn basic statistical concepts:
  • Mean, median, and standard deviation
  • Hypothesis testing

5. Data Visualization

• Explore advanced visualization libraries:
  • Seaborn for statistical data visualization
  • Plotly for interactive visualizations

6. Machine Learning Fundamentals

• Study the basics of machine learning:
  • Supervised and unsupervised learning
  • Classification and regression

7. Scikit-Learn

• Gain hands-on experience with Scikit-Learn for machine learning algorithms:
  • Linear regression, decision trees, and more

8. Deep Learning (Optional)

• Delve into neural networks using:
  • TensorFlow or PyTorch

9. Feature Engineering

• Learn techniques to create meaningful features for machine learning models.

10. Model Evaluation and Selection

• Understand metrics for evaluating models:
  • Accuracy, precision, recall
  • Techniques for model selection.

11. Big Data Technologies (Optional)

• Explore tools like:
  • Apache Spark for handling large datasets

12. Deployment and Production

• Learn how to deploy models and integrate them into production systems.

13. Version Control

• Use tools like Git for version control and collaboration.

14. Advanced Topics

• Dive into advanced topics based on interests:
  • Natural Language Processing (NLP)
  • Time Series Analysis

15. Stay Updated

• Keep up with the latest developments in the field through:
  • Blogs, conferences, and online courses.

Remember, adapt the roadmap based on your interests and career goals. Continuous learning and hands-on projects are key to mastering data science in Python.

License

This project is licensed under the MIT License - see the LICENSE file for details. Copyright (c) 2023 AhemadSk71