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Data Science remains one of the hottest job titles in the 21st century. It’s no wonder it’s garnering so much attention. But first, what is Data Science?
Data science is an interdisciplinary field that includes elements from various fields such as data visualization, model building, and data manipulation.
In this article, we’ll take a closer look at these elements and explore the libraries that will allow you to implement these elements using Python. Whether you’re a professional or consider yourself a beginner, this article will definitely expand your knowledge. Let’s get started!
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Data collection refers to the process of combining information from across the web.
You can see various data projects that include synthetic datasets or datasets from Kaggle.
Even though this is good for beginners, if you want to get a competitive job you should do more.
There are a number of options in Python that allow you to do this. Let’s take a closer look at three of them.
Scrappy
It is a web mining framework for Python, ideal for large-scale data extraction.
It is more advanced than BeautifulSoup and allows you to collect more convoluted data.
The unique feature of Scrapy is its ability to handle asynchronous requests efficiently, which speeds up large-scale scraping tasks. If you are a beginner, the next one will be more suitable for you.
Attractive Soup
BeautifulSoup is used to parse HTML and XML documents. It is simpler and more user-friendly than Scrapy, making it ideal for beginners or simpler scraping tasks.
A hallmark of BeautifulSoup is its flexibility in parsing even poorly formatted HTML.
Selenium
Selenium is primarily used to automate web browsers. It is ideal for scraping data from web pages that require interaction, such as filling out forms or including JavaScript-based content.
Its recent feature is the ability to automate and interact with websites as if a human were browsing them, allowing it to collect data from lively websites.
Now you have the data, but you should examine it carefully to understand its characteristics.
Scipy
Scipy is used in scientific and technical computing.
Compared to Numpy, this tool focuses more on advanced calculations, offering additional functionalities such as optimization, integration, and interpolation.
A unique feature of the Scipy language is its extensive collection of submodules for various computational tasks in the exact sciences.
Numpy
It is one of the most essential Python libraries dedicated to data science.
The biggest part of its fame comes from the array object. While Scipy is based on Numpy, Numpy itself also works.
The standout feature is the ability to perform proficient array calculations, which is basically why it is so essential in data science. However, another feature is just as essential.
Pandas
Pandas offers easy-to-use data structures such as data frames, as well as data analysis tools that are best suited for manipulating data using data frames.
The novel feature of Pandas that distinguishes it from other data manipulation tools is DataFrames, which provide extensive data manipulation and analysis capabilities.
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Data manipulation is the process by which you shape data to prepare it for subsequent steps.
Pandas
Pandas offers data structures like DataFrame which makes working with everything easier. Because pandas defines too many built-in functions which will turn 100 lines of code into 2 built-in functions.
It also includes data visualization and exploration features, which makes it more versatile than other Python libraries.
Data visualization allows you to tell the whole story on one page. To do that, in this section we will discuss 3 of them.
Matplotlib Library
If you’ve visualized your data using Python, you know what matplotlib is. It’s a Python library for creating a wide range of types of graphics, such as immobile, interactive, and even animated.
It’s a more customizable data visualization library than most. You can control almost every element of your chart with it.
Seaborn
Seaborn is built on top of the Matplotlib library and offers a different type of view of the same graphs, such as a bar chart.
Compared to Matplotlib, it is easier to operate for creating convoluted visualizations and is fully integrated with Pandas DataFrames.
Plotly
Ploty is more interactive than others. You can even create a dashboard with it, as well as integrate your code with Plotly and see your graphs on the Plotly website.
If you want to know more, here it is Python Data Visualization Libraries.
Building a model 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.
Learn Science Kit
The most famed Python library for machine learning is Sci-kit learn. It offers overly plain but powerful functions to build a model in seconds. Sure, you can develop many of these functions yourself, but do you want to write 100 lines of code instead of 1?
Its novel feature is a comprehensive set of algorithms in one package.
Tensor Flow
TensorFlow, created by Google, is better suited for high-level models such as deep learning and offers high-level features for building large-scale neural networks compared to Scikit-learn. In addition, there are many free tools available online, also created by Google, that make it easier to learn TensorFlow.
Difficult
Keras offers a high-level neural network API and is able to run on Tensorflow. It focuses more on enabling rapid experimentation with deep neural networks than Tensorflow.
Now you have your model, but it’s just a script. To make it something more meaningful, you should turn your model into a web app or API to make it production-ready.
Django
The most famed web framework allows you to develop a model in a structured way. It is more complicated than Flask and FastAPI, but the reason for this is that it has many built-in features, such as an administration panel.
For example, there are many things you should be building from scratch in Flask, but if you don’t know much about web frameworks, this is a good starting point.
Flask
Flask is a micro-web framework for Python that makes it easier to develop your own web application or API. It is more adaptable than Django and more suitable for smaller applications.
FastAPI
FastAPI is rapid and straightforward to operate, which has contributed to its greater popularity.
A unique feature of FastAPI is the automatic generation of documentation and built-in validation using Python type hints.
If you want to know more, here it is 18 best python libraries.
At this point you have everything but in your own environment. To share your model with the world and test it even more, you should expose it to people. To do this, your web app or api should run on a server.
Heroku
Cloud Platform as a Service (PaaS) supporting multiple programming languages.
It is more user-friendly for beginners compared to AWS, also offering simpler deployment processes for web applications. If you are a complete novice, it may be better for you, like Python anywhere.
PythonAnywhere
PythonAnyhwhere is a web-based programming environment that also offers website hosting services, based on the Python programming language, as can be inferred from its name.
It is more focused on Python-specific projects compared to other tools. If you selected Flask in step 6, you can upload your model to pythonanywhereand also has a free feature.
AWS (Amazon Web Services)
AWS has too many different options for every feature it offers on the platform. If you’re planning on choosing a database, even for that, there are too many options.
It is a more convoluted and comprehensive tool than others, and works well in large-scale operations.
If you chose Django in the previous section and have taken the time to build a large-scale web application, your next choice will be AWS.
In this article, we have looked at the main Python libraries used in Data Science. When working on Data Science projects, remember that there is no single definitive method. I hope this article has familiarized you with the various tools.
Nate Rosidi is a data scientist and product strategist. He is also an associate professor of analytics and the founder of StrataScratch, a platform that helps data scientists prepare for interviews with real questions from top companies. Nate writes about the latest job trends, provides interview advice, shares data science projects, and covers all things SQL.