🔰 Learning Python for Data Analysis and Visualization

⏱ 21 Hours 📦 110 Lessons

Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video!

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𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 : 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐥𝐢𝐬𝐭𝐬, 𝐭𝐮𝐩𝐥𝐞𝐬, 𝐚𝐧𝐝 𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬

𝐋𝐢𝐬𝐭𝐬:
- Lists are ordered collections of items.
- They are mutable, meaning you can change their content after creation.
- You can have duplicate values in a list.
- Lists are defined using square brackets [ ].

Example:
my_list = [1, 2, 3, 'apple', 'banana', 'cherry']

𝐓𝐮𝐩𝐥𝐞𝐬:
- Tuples are ordered collections of items, similar to lists.
- However, they are immutable, meaning once created, their content cannot be changed.
- Tuples are defined using parentheses ( ).
- You can have duplicate values in a tuple.

Example:
my_tuple = (1, 2, 3, 'apple', 'banana', 'cherry')

𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬:
- Dictionaries are unordered collections of items that are stored as key-value pairs.
- They are mutable.
- Dictionaries are defined using curly braces { }.
- Each key in a dictionary must be unique, but the values can be duplicated.

Example:
my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}

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What is Python?

- Python is a programming language 🐍

- It's known for being easy to learn and read 📖

- You can use it for web development, data analysis, artificial intelligence, and more 💻🌐📊

- Python is like writing instructions for a computer in a clear and simple way 📝💡

- Python supports working with a lot of data, making it great for projects that involve big data and statistics 📈🔍

- It has a huge community, which means lots of support and resources for learners 🌍🤝

- Python is versatile; it's used in scientific fields, finance, and even in making movies and video games 🧪💰🎬🎮

- It can run on different platforms like Windows, macOS, Linux, and even Raspberry Pi 🖥️🍏🐧🍓

- Python has many libraries and frameworks that help speed up the development process for web applications, machine learning, and more 🛠️🚀

Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:

1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.

2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.

4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.

5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.

6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.

7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.

8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.

9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.

10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.

By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.