Wonderful Tech @Taiwan

Kaggle itself provides some great free resources and online courses, which can help you prepare for their contests. Here are a few: 1. **Kaggle Learn**: Kaggle's own "Learn" platform has a number of free, fast-paced courses designed to get you up to speed with the most important tools and techniques used in data science:    - **Python**: Teaches the basics of Python programming and data science.    - **Intro to Machine Learning**: Covers the fundamental concepts required to get started with machine learning.    - **Intermediate Machine Learning**: Introduces more complex machine learning models, including handling missing values and categorical variables, and using pipelines.    - **Data Visualization**: Teaches how to create beautiful and informative visualizations.    - **Pandas**: Introduces the Pandas library, which is fundamental for data analysis and manipulation in Python.    - **Deep Learning**: Covers the basics of dee...

Yes, Kaggle is a popular platform for data science competitions, but there are several others that you might be interested in as well. Here are a few: 1. **Zindi**: Zindi is a platform for data scientists in Africa to connect and compete. They run competitions (both for profit and non-profit) focused on solving Africa's most pressing problems. 2. **DrivenData**: DrivenData hosts competitions that aim to address some of the world's biggest social challenges. The challenges range from health, education, climate, and more. 3. **CodaLab Competitions**: CodaLab is an open-source web platform that enables researchers, developers, and data scientists to collaborate. They have a section dedicated to competitions. 4. **CrowdANALYTIX**: CrowdANALYTIX hosts data science and AI-related contests and also has an active community of data scientists. 5. **DataScienceGlobal Impact Challenge**: This is an annual competition aimed at data scientists and non-profits. The goal is to showcase how da...

Exploratory Data Analysis (EDA) is a critical step in any data science project. It involves understanding the data you're working with, discovering patterns, identifying anomalies, testing hypotheses, and checking assumptions using statistical summaries and graphical representations. Here's a bit more detail: 1. **Understanding the Data**: Start by checking what each column represents, the types of values (categorical, numerical, binary, etc.), and get a general sense of the data structure. 2. **Summary Statistics**: Pandas provides a `describe()` function that gives a useful summary of the numerical columns. It shows the mean, standard deviation, min, max, and quartiles. For non-numeric data, you can use the `value_counts()` method to see the distribution of categories. 3. **Visualizing the Data**: Graphical representations can help you understand the data better. Histograms and box plots are useful for visualizing distributions, scatter plots can show relationships between va...

Participating in Kaggle competitions is a great way to learn and apply data science techniques. Here are some steps and tips to get you started: 1. **Create a Kaggle Account**: The first step is to create a Kaggle account, if you haven't already. 2. **Find a Competition**: Browse the Competitions section on Kaggle to find one that interests you. If you're a beginner, you might want to start with one of the "Getting Started" competitions, such as the "Titanic: Machine Learning from Disaster". 3. **Understand the Problem Statement**: Read the competition details carefully to understand the problem you need to solve, the data you have to work with, and the metric on which your solution will be evaluated. 4. **Download the Data**: Download the provided datasets. Kaggle competitions usually provide a training set, which includes the target variable, and a test set, which you'll use to make predictions for submission. 5. **Explore the Data**: Use techniques su...

Yes, there are several best practices you can follow when writing code in Jupyter Notebooks for data analysis: 1. **Clear and descriptive naming**: Use clear and descriptive names for variables, functions, and so on. This makes your code easier to understand and maintain.  2. **Commenting and Documentation**: Make sure to document your code well. This includes adding comments to explain complex code blocks, and using docstrings for functions and classes. 3. **Modular and Reusable Code**: Encapsulate code that performs a specific task into a function. This makes your code more readable, reusable, and maintainable. 4. **Minimal use of global variables**: Try to avoid using global variables where possible, and pass variables to your functions instead.  5. **Consistent Coding Style**: Following a consistent coding style can make your code much easier to read and understand. You can follow the PEP 8 -- Style Guide for Python Code. In addition to these, here are some Jupyter Noteboo...