Mastering Data Science: Essential Python Libraries for Data Scientists

Data Science continues to be a highly sought-after profession in the 21st century, generating significant interest. But what exactly is Data Science?

Data Science is an interdisciplinary field that encompasses components from diverse domains, including Data Visualization, Model Building, and Data Manipulation.

This article delves into these components and examines the libraries that enable their application using Python. Whether you're an expert or a novice, this article promises to enhance your understanding. Let's begin our exploration!

Step 1: Data collection

Data collection involves gathering information from various sources, such as the web.

In the realm of data projects, you may encounter synthetic datasets or datasets from platforms like Kaggle. While these are beneficial for beginners, aspiring to secure competitive roles demands more comprehensive efforts.

In Python, there are several options for data collection, and we'll focus on three of them.

Scrapy

Scrapy is a Python framework designed for web crawling, making it well-suited for large-scale data extraction. It offers advanced capabilities compared to BeautifulSoup, enabling more intricate data collection. Notably, Scrapy efficiently handles asynchronous requests, enhancing its speed for extensive scraping tasks.

BeautifulSoup

BeautifulSoup is utilized for parsing HTML and XML documents. It's known for its simplicity and user-friendliness, making it an excellent choice for beginners or simpler scraping endeavors. One of its standout features is its adaptability in parsing even poorly structured HTML.

Selenium

Selenium is primarily used for automating web browsers. It excels at scraping data from websites that necessitate interaction, such as form completion or JavaScript-driven content. Its unique capability lies in its capacity to automate and interact with web pages in a manner akin to human browsing, enabling data collection from dynamic web pages.

Step 2: Data Exploitation

After acquiring the data, it's crucial to explore its characteristics.

Scipy

Scipy is tailored for scientific and technical computations, emphasizing advanced functionalities such as optimization, integration, and interpolation. Notably, it boasts an extensive array of submodules catering to various scientific computing tasks.

Numpy

Numpy stands as one of Python's most crucial libraries for Data Science. Its array object is a key contributor to its widespread acclaim. While Scipy extends Numpy's capabilities, Numpy itself is a powerful standalone library. Its efficiency in performing array computations is a defining factor in its significance to Data Science.

Pandas

Pandas introduces user-friendly data structures like data frames and robust data analysis tools, making it an essential tool for data manipulation. A standout feature of Pandas is its DataFrames, which offer extensive capabilities for data manipulation and analysis, setting it apart from other data manipulation tools.

Step 3 : Data Manipulation

Data Manipulation is the process where you are shaping your data, to get ready for the next stages.

Pandas

Pandas offers data structures like DataFrame, which makes everything easier to work with. Because there are too many built-in functions defined in pandas, which will turn your 100 lines of code into 2 built-in functions.

It also has data visualization capabilities and data exploration functions, making it more all-purpose than other Python libraries.

Step 4: Data Visualization

Data Visualization enables you to tell the whole story on one page. Tto do that,? in this section we will cover 3 of them.

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Matplotlib

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If you visualized your Data with Python, you know what matplotlib is. It is a Python library for creating a wide range of types of graphics, like static, interactive or even animated.

It is a more customizable data visualization library than others. You can control pretty much any element of a plot with it.?

Seaborn

Seaborn is built on top of Matplotlib, and offers a different kind of view of the same graphs, like bar plot.

It can be simpler to use for creating complex visualizations, compared to Matplotlib, and it is fully integrated with Pandas DataFrames.

Plotly

Ploty is more interactive than others. You can even create a dashboard with it and also you can integrate your code with Plotly and see your graphs on the Plotly website.

Step 5 : Model Building

Model Building is the step, where you can finally see the results of your actions, to make predictions. To do that, we still have too many libraries.

Sci-kit Learn

Most famous Python library for machine learning is Sci-kit learn. It offers too simple, yet efficient functions to build your model in a couple of seconds. Of course, you can develop many of these functions by yourself, but do you want to write 100 lines of code instead of 1?

Its novel feature is the comprehensive collection of algorithms in a single package.

TensorFlow

TensorFlow, created by Google, is better suited for high-level models such as deep learning and offers high-level functions for building large-scale neural networks compared to Scikit-learn. Additionally, there are many free tools available online, also created by Google, which make learning TensorFlow easier.

Keras

Keras offers a high-level neural networks API, and it is capable of running on top of Tensorflow. It focuses more on enabling fast experimentation with deep neural networks than Tensorflow.

Step 6: Model on Production

Now you have your model, but it is just script. To make something more meaningful from it, you should turn your model into web application or api to make it ready for production.

Django

The most famous web framework allows you to develop your model in a structured way. It is more complicated than Flask and FastAPI, yet the reason behind it is that it has many built-in features, like an admin panel.

In Flask, for example, you should develop many things from scratch, but if you don’t know much about web frameworks, it's a good place to start.

Flask

Flask is a micro web framework for python, with it you can develop your own web app or api, easier. It is more flexible then Django and more suitable for smaller applications.

FastAPI

FastAPI is fast and easy to use, which made it more popular.

A unique feature of FastAPI is its automatic generation of documentation and its built-in validation using Python type hints.

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