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Data Science From Scratch: First Principles With Python

Evan Simmons

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Data science is a rapidly growing field that combines elements of mathematics, statistics, computer science, and domain knowledge to extract insights from data. It has applications in a wide variety of industries, including healthcare, finance, retail, and manufacturing.

If you're interested in learning data science, there are a number of resources available to you. However, one of the best ways to learn is by ng. In this article, we'll walk you through the basics of data science, using Python. We'll cover the following topics:

Data Science from Scratch: First Principles with Python

by Joel Grus

4.4 out of 5

Language	:	English
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* Data collection * Data cleaning * Data exploration * Data modeling * Data visualization

We'll also provide you with practical examples and exercises to help you apply your knowledge.

Data Collection

The first step in any data science project is to collect data. This can be done from a variety of sources, including:

* Public datasets * Web scraping * Surveys * Experiments

Once you have collected your data, you need to clean it. This involves removing any errors or inconsistencies from the data. You can do this manually or using a data cleaning tool.

Data Exploration

Once your data is clean, you can begin to explore it. This involves looking for patterns and trends in the data. You can do this using a variety of data visualization techniques, such as:

* Histograms * Scatterplots * Box plots

Data exploration can help you to identify the most important features in your data and to develop hypotheses about the relationships between different variables.

Data Modeling

Once you have explored your data, you can begin to build models to predict future outcomes. There are a variety of data modeling techniques available, including:

* Linear regression * Logistic regression * Decision trees * Random forests

The best data modeling technique for your project will depend on the type of data you have and the goals of your project.

Data Visualization

Once you have built your models, you need to visualize the results. This will help you to communicate your findings to others and to identify any potential errors in your models. There are a variety of data visualization techniques available, including:

* Bar charts * Line charts * Pie charts * Scatterplots

Data visualization can help you to make your findings more accessible and to identify any potential errors in your models.

Data science is a powerful tool that can be used to extract insights from data. In this article, we've provided you with a comprehensive guide to learning data science from scratch, using Python. We've covered the fundamentals of data science, including data collection, cleaning, exploration, modeling, and visualization. Additionally, we've included practical examples and exercises to help you apply your knowledge.

If you're interested in learning more about data science, there are a number of resources available to you. You can find online courses, books, and tutorials. You can also find data science communities online where you can connect with other data scientists and learn from their experiences.

With a little effort, you can learn data science and use it to solve real-world problems.

Data Science from Scratch: First Principles with Python

by Joel Grus

4.4 out of 5