Python for Data Science: A Complete Guide
Updated Jan 23, 2025
Table of Contents
Data science is a large and growing field that empowers organizations to make data-driven decisions and improve their operations. Those just getting started with data science will need to learn a programming language like Python to interact with computers, work with data, and build powerful AI and machine learning models.
Python’s popularity as a programming language stems from its versatility and ease of use, making it an excellent choice for a range of projects including machine learning and AI. While getting started with Python is relatively straightforward, new data scientists will still need to learn how to set up their environments and install the appropriate libraries for their specific projects. With the right training and guidance, Python’s adaptable nature allows data scientists to thrive across many different domains.
Read on to learn more about Python for data science and the most essential tools for data scientists.
What Is Data Science?
Data science is a critical field that combines different tools and techniques to extract knowledge and insights from structured data (i.e., organized in a predefined format or schema, such as databases or spreadsheets) and unstructured data (i.e., text-heavy or multimedia data that lacks a consistent structure, such as emails, videos, and audio recordings).
There are also many subdomains of data science, including:
- Data engineering is a practice that involves collecting and managing data for use within other data science disciplines.
- Data analytics is a subfield that focuses more on analyzing past performance and enabling data-driven decisions.
- Artificial intelligence (AI) is an aspect of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence.
- Machine learning is a subfield of AI that enables systems to learn from data without explicit programming.
- Deep learning is a branch of machine learning that uses neural networks to identify complex patterns in very large datasets.
Depending on the specific use case, data scientists may choose to use different programming languages. Python or R are languages that are useful for performing analysis, applying statistics to data, and building AI/ML models. Many data scientists also use SQL to manipulate data and extract relevant insights, especially for data analytics and business intelligence use cases.
Why Is Python Used for Data Science?
Python is one of the most popular languages for data science and machine learning because it’s versatile and has a vibrant open-source ecosystem. As such, there is a large community of developers who create libraries and tools to make Python easier to use. In fact, the Python Package Index (PyPI) has hundreds of thousands of open-source packages available.
Many open-source Python libraries provide data scientists with additional capabilities for manipulating and analyzing data, processing large datasets, building new AI/ML models, creating interactive visualizations, and more. This extensibility and versatility make Python ideal for both beginners and experienced data scientists.
Essential Python Tools for Data Science
Let’s look at some of the most popular Python tools, libraries, and frameworks for various data science projects.
pandas
pandas is an open-source data analysis and manipulation library designed to make it easier to work with structured data. The library offers fast and flexible data structures and analysis tools that extend the functionality of Python. pandas is popular for analyzing, cleaning, and exploring large data sets.
NumPy
NumPy is a data analysis library for more advanced numerical calculations. The library provides a powerful array and mathematical operations functionality, which is useful for machine learning and other data science use cases. Many of the most popular Python libraries rely on NumPy for numerical operations because it’s fast and efficient.
PyTorch
PyTorch is an open-source platform developed by Meta AI for machine learning projects. The library includes comprehensive features for working with machine learning models and a rich ecosystem of tools and libraries that extend its functionality. PyTorch is particularly useful for building and deploying deep learning models to power computer vision and natural language processing applications.
TensorFlow
TensorFlow is an open-source machine learning framework for building deep neural networks with very little code. The framework provides end-to-end machine learning capabilities, with a focus on model training and inferencing. TensorFlow is ideal for large-scale machine learning applications because it can be deployed across a variety of platforms and can run on multiple CPUs and GPUs.
Apache Spark
Apache Spark is an open-source unified analytics engine for large-scale data processing. It supports data science, data engineering, and machine learning use cases. The platform is particularly useful for streaming processing in real time and batch processing large data sets.
Keras
Keras is an open-source deep learning framework for working with deep neural networks. The library offers a simple Python interface that’s designed for fast experimentation with deep learning models and integrates with the JAX, PyTorch, and TensorFlow frameworks. Keras is a popular choice because the API is easy to learn and it can reduce the time needed to build prototypes.
Matplotlib
Matplotlib is a library for creating static, interactive, and animated visualizations. It is one of the oldest data visualization libraries and includes a wide range of 2D plot types and output formats. Matplotlib is a great choice for projects that require fine-grained control and highly customized visualizations.
Seaborn
Seaborn is a statistics data visualization library that extends the functionality of Matplotlib. The additional features include even more plot types and advanced options out of the box. Seaborn is great for quickly creating data visualizations with minimal code.
Scikit-learn
Scikit-learn is a popular library for predictive data analysis built on NumPy, SciPy, and Matplotlib. It provides numerous clustering, classification, and regression algorithms, as well as decision trees. Scikit-learn is ideal for building and deploying machine learning models in Python.
Anaconda
Anaconda is a Python distribution and comprehensive platform that comes with many data science and machine learning packages, as well as a package manager called Conda to easily install more. The platform has an IDE called Spyder, which is tailored for scientific computing and data analysis in Python. Many data scientists choose Anaconda for its interactive console, debugging tools, data exploration capabilities, and support for ML and AI models, as well as its ability to facilitate the secure use of open source for enterprises.
Jupyter Notebook
Jupyter Notebook is an interface for creating and sharing documents that combine code, text explanations, visualizations, and more. Jupyter Notebooks are useful for a variety of data science tasks, including exploratory analysis and collaborating on data science projects.
Python for Data Science with Anaconda
Python is invaluable for data science because there are so many free, open-source libraries and tools to accelerate data workflows and projects. At the same time, this makes it critical to choose the right resources and solutions when learning how to use Python for data science.
Tools such as Anaconda Notebooks, AI Assistant, and AI Navigator make it even easier for data scientists to get started with Python, as well as share code and collaborate on data projects. This is a community for learning and working together to accelerate innovation with data science, Anaconda, and Python.
Request a demo to see if Anaconda is right for your data science and machine learning projects. Or, if you’re curious to experiment with Anaconda on your own, you can get started for free.