Once you answered all the questions you can simply ask Chat GPT to review your answers by typing any of the below prompt

Can you please tell me how I performed?

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🚀 𝐏𝐲𝐭𝐡𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬

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Steps to become a successful data scientist

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Data Analysis vs Data Science

Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization.
On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming.

In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data.

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SQL vs Python

SQL is great for managing and querying structured databases, especially when dealing with large datasets. It excels in tasks like filtering, sorting, and aggregating data.

Python, on the other hand, is a versatile programming language used for a broader range of tasks. In the context of data, Python is powerful for data manipulation, analysis, and machine learning. It offers libraries like Pandas for data manipulation, NumPy for numerical operations, and Scikit-Learn for machine learning.

In summary, SQL is essential for efficient database querying, while Python provides a more comprehensive solution for various data-related tasks, making them often used together in data-related workflows.

SQL Practice Questions with Answers -> https://t.iss.one/learndataanalysis/596

Python Roadmap for Data Analysts -> https://t.iss.one/pythonfreebootcamp/207

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Importance of Data Cleaning

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