Learn Python with Free Lessons & Tips

Both R and python are good for data analysis. The best language depends on the type of data you are working with.

R is a good choice for statistical learning,research and data visualization. Its specially good for statisticians who need to develop static models. But R has single thread processing,which one may cause delays when working with large set of data.

Python is a good choice for Machine learning, Large scale applications and data analysis in web applications. Its a general purpose language which can be used for a wide range of tasks.It also supports web scraping and data processing.Pandas library in python is more famous for data analysis. Meanwhile python supports multi threading which makes it easier to handle huge data.

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Python is popular in hacking because it is: 1. **Versatile**: Can automate tasks, exploit vulnerabilities, and create tools.2. **Easy to learn**: Python’s simple syntax makes it accessible to both beginners and experts.3. **Extensive libraries**: Offers libraries like Scapy (networking), Nmap (port scanning), and others for various hacking activities.4. **Cross-platform**: Works on multiple operating systems, making it flexible for different environments. Here is my number Call 073-1485-0321. read less

Python 2.x and Python 3.x are two major versions of the Python programming language. The main difference is that Python 3.x introduced changes to improve the language and fix inconsistencies, but these changes are not backward-compatible with Python 2.x. For example, in Python 3.x, the `print` statement became a function (e.g., `print("Hello")` instead of `print "Hello"`). Python 2.x is no longer supported as of 2020, so Python 3.x is the recommended version for new projects. Here is my number Call 073-1485-0321. read less

Yes, it's possible to learn C++ and Python at the same time, but it can be challenging since they are quite different in syntax and concepts. C++ is more complex and requires a deeper understanding of memory management and low-level programming, while Python is simpler and more abstracted, making it easier for beginners. Learning both together could help you understand different programming paradigms, but it requires good time management and a clear study plan. Here is my number Call 073-1485-0321. read less

Python has several libraries that are widely used by data scientists: 1. **NumPy**: For numerical computations and handling large arrays.2. **Pandas**: For data manipulation and analysis.3. **Matplotlib**: For creating static, animated, and interactive visualizations.4. **Seaborn**: For statistical data visualization.5. **Scikit-learn**: For machine learning algorithms and data preprocessing.6. **TensorFlow/PyTorch**: For deep learning models.7. **SciPy**: For scientific and technical computing.8. **NLTK/Spacy**: For natural language processing. These libraries help data scientists efficiently analyze and visualize data, build models, and make predictions. Here is my number Call 073-1485-0321. read less

• Python Cookbook: Recipes for Mastering Python 3 (3rd Edition)
• Introduction to Machine Learning with Python: A Guide for Data Scientists (1st Edition)
• Fluent Python: Clear, Concise, and Effective Programming (1st Edition)
• Programming Python: Powerful Object-Oriented Programming (4th Edition)

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• Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming. ...
• Python Pocket Reference 5ed: Python in Your Pocket. ...
• Python Programming: An Introduction to Computer Science. ...
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython – 2nd Edition.

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• Python Programming MOOC 2023.
• Introduction To Computer Science And Programming In Python(MIT)
• Programming for Everybody (Getting Started with Python)(Coursera)
• Crash Course on Python(Google)
• Learn Python (scrimba)
• Courses-SkalskiP.
• CS50's Introduction to Programming with Python(Harward Uni)

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