Data Science from Scratch: First Principles with Python

Data Science from Scratch: First Principles with Python

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Data Science from Scratch: First Principles with Python

In this post, we will be exploring the foundational concepts of data science and how to implement them using Python. From gathering and cleaning data to building predictive models, we will cover it all from scratch.

We will start by discussing the importance of data in today’s world and how it drives decision-making in various industries. We will then delve into the process of collecting and preparing data for analysis, including dealing with missing values and outliers.

Next, we will move on to exploring different types of data visualization techniques to gain insights from data. We will cover basic statistical concepts and how to apply them using Python libraries like NumPy and Pandas.

Finally, we will dive into building machine learning models for predictive analysis. We will cover popular algorithms like linear regression, logistic regression, decision trees, and more. We will also discuss how to evaluate the performance of these models and fine-tune them for better results.

By the end of this post, you will have a solid understanding of the foundational principles of data science and how to apply them using Python. Stay tuned for more in-depth tutorials and hands-on projects to further enhance your skills in data science.
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