What is Python for Data Science?

Python is one of the most popular programming languages worldwide, especially for data science. Web Developers and Software Engineers use Python, but so do Data Scientists, Business Analysts, and Cybersecurity Analysts.

In this overview, you’ll learn more about what Python for data science is, what it can do, who uses it, and how to learn it so you can determine how to add this skill to your professional toolbox.

What Can You Do with Python for Data Science?

Python is advantageous for data science professionals of all kinds. Its ease of use and scalability make it the top choice for Data Scientists, Data Engineers, and Data Analysts in virtually every sector of the economy.

Because Python is both easy to learn and powerful, its libraries and frameworks can be ideal for dealing with mathematical functions, data structures, and visualization. Here are some of the most common uses for Python in data science.

• Data Analysis - Python is easy to read and write, so it’s commonly used for complex data analysis—particularly handling large datasets. Top Python libraries for data analysis include:

• NumPy
• Pandas
• SciPy

• Data Visualization - Data science often requires visualization tools. Data professionals use charts, graphs, and even maps to present data in easy-to-digest ways. Top Python libraries for data visualization include:

• Matplotlib
• Plotly
• Seaborn

• Artificial Intelligence and Machine Learning - Machine learning, or ML, is a subset of artificial intelligence (AI). Data science pros use ML libraries like Scikit Learn for data classification and linear regression. Top Python libraries for AI and ML include:

• Scikit Learn
• PyBrain
• TensorFlow

How Much Does Python Cost?

Most programming languages can be used free of charge, and Python is no exception. Its libraries and frameworks are 100% free to download and use for private and commercial purposes.

Open-source programming languages like Python have become popular resources for companies worldwide, but not simply to save money. Over time, Python has become the programming language of choice among data science professionals.

Massive online communities develop from open-source languages like Python, providing support and other resources to novices and even more advanced users.

Top free Python libraries for data science include:

• NumPy
• Pandas
• Matplotlib
• Scikit Learn
• SciPy

Other popular resources for Python users include:

• Jupyter Notebook - Jupyter is mainly a collaboration among Python users. Offered as an open-source platform, commercial use may require a monthly fee.

• Anaconda - One of the most popular data science platforms, you can use Anaconda for Python installation. It provides an easy way to use Python’s scientific modules when installed before Python downloads.

• Google Colab - Colab is short for Colaboratory, Google’s cloud-based alternative to Jupyter Notebook. You can store Colab notebooks in Google Drive or import Jupyter notebooks into Colab.

What Are the Benefits of Learning Python for Data Science?

As Forbes noted in its Top Five Data Science Trends That Made An Impact In 2022, Python has emerged as “the go-to programming language for data science.” The article references multiple dominant data science libraries, including:

• NumPy
• Pandas
• Matplotlib
• Scikit Learn
• PyTorch

If you’re planning to study Python for data science, you can go into it knowing you’ll learn crucial skills and information. Top roles typically requiring Python for data science today include:

• Software Engineer
• Data Scientist
• Coding Engineer
• Software Developer
• Engineering Manager
• Data Analyst

Python for data science isn’t only for IT or software development, either. Sectors as diverse as banking/finance, manufacturing, agriculture, and media require Python data science expertise, and public sector roles like government and academia can require it, too.

Read more about why you should learn Python for data science.

Python Data Science Careers

Python for data science is ubiquitous today, with virtually every industry using it where possible. According to the Bureau of Labor Statistics, industries with the highest level of Data Scientist employment include:

• Computer Systems Design and Related Services
• Management of Companies and Enterprises
• Management, Scientific, and Technical Consulting Services
• Scientific Research and Development Services
• Credit Intermediation and Related Activities

These categories illustrate several things.

First, companies need Python data science expertise at the highest levels, including management positions like Senior Engineer, Tech Lead, and Product Manager. Second, C-suite executives need data science expertise at their fingertips from trusted managers. And third, science is only one area where data science skills are needed, while business and finance, along with high tech, lead the pack.

A complete list of what industries use Python for data science and for what tasks is beyond the scope of this article. Some of the most well-known examples include:

• Healthcare imaging analysis
• Speech or facial recognition
• Drug development in the pharmaceutical industry

How to Learn Python for Data Science

Python for data science novices often find it challenging when they try to determine the best way to master the popular programming language. Whereas busy professionals may already have some data science skills, students beginning their careers may want to get in-depth training in a concentrated time frame.

Fortunately, Python data science students can find many educational options, both in-person and live online. Bootcamps, certificate programs, and self-paced classes provide information and, in many cases, hands-on experience. Consider the following:

• Live Coursework - Most Python for data science training students prefer to learn in person or live online through teleconferencing. You can use Noble Desktop’s Classes Near Me tool to compare and contrast all the options, or look for live online courses if you’ve already narrowed your choices to virtual training alternatives.

• Self-Paced Programs - On-demand or self-paced courses can be helpful if you’re starting out with Python for data science. Some offer training at the beginner level, whereas others require intermediate skills. Check out options like Exchanging Excel for Python or Making a Twitter Bot in Python for more information.

• Seminars, Tutorials, and Articles - If you want to learn a few things about Python for data science before you commit to formal training, check out some free resources first. Noble Desktop’s YouTube channel includes a playlist of Python data science tutorials and webinars. You can also find helpful Python and data science training in the Free Seminars section of Noble’s website.

Read the full guide on how to learn Python for data science.

A Brief History of Python for Data Science

Guido van Rossum developed Python in the late 1980s as an improved version of the ABC programming language. With Aaron Watters and James Ahlstrom, van Rossum co-authored Internet Programming with Python. This guide was the only available book on Python for many years.

As Python’s popularity grew, it became the primary language for data science, data analysis, and machine learning. Its open-source ecosystem also made it an ideal choice for web development, prototyping, and scripting. Top companies known to use Python include:

• Google
• Cisco
• Intel
• JP Morgan Chase
• Netflix

With its easy syntax and massive worldwide support community, Python’s popularity continues to grow among data science and development professionals. If you want to work in the data science field, consider starting with Python.

Comparable Programming Languages

Many Data Scientists, Data Analysts and Machine Learning Engineers use Python more than other programming languages. While some data professionals also have to learn R, Java, and C/C++, Python typically stands at the top of the list for most of them.

One popular programming language that offers an interesting contrast with Python is JavaScript. Many tech pros learn both of these languages and may even use them together. Python provides an attractive array of data science and machine learning visualization tools.

Additionally, many tech pros do not consider JavaScript a primary data science language, whereas they do use Python for data science. Automation in data science, in particular, can be easily accomplished with Python.

Comparing and contrasting these languages may be useful only in learning which ones to use for which tasks. When it comes to data science, you’ll most likely need to learn at least one or two other languages. Consider starting with Python before moving on to other languages like:

• JavaScript
• SQL
• R
• C/C++
• Java

Learn Python for Data Science with Hands-on Training at Noble Desktop

Because Python for data science involves two potentially different disciplines—Python programming and the broader data science field—not every student approaches it the same way. How and where you plan to use the knowledge you gain from Python for data science training may dictate your approach.

Noble Desktop offers multiple avenues to learn data science. Their Data Science Certificate includes Python programming fundamentals, machine learning, SQL to query databases, and plotting and dashboard libraries. This program prepares attendees for entry-level positions in data science and Python engineering.

Another option is Noble’s Python for Data Science Bootcamp. A hands-on 30-hour course, the bootcamp includes training in Numpy, Pandas, Matplotlib, and linear regression. Students can save by taking the Python for Data Science Bootcamp as part of the Data Science Certificate program as well.

If you prefer to peruse all the Python for data science training Noble Desktop offers, check out the Python Classes page. Here you’ll find bootcamps and certificate programs as well as shorter courses. Top certificate programs include:

• Data Science Certificate
• Software Engineering Certificate
• Data Analytics Certificate

Popular bootcamp options include:

• Python for Data Science Bootcamp
• Python Programming Bootcamp
• FinTech Bootcamp
• Cybersecurity Bootcamp

Other training options include:

• Python for Automation
• Cybersecurity with Python
• Python for Network Security

Noble Desktop’s bootcamps and certificate programs earn high marks from graduates. They are available live online or in-person in New York City. Additional perks include a verified Certificate of Completion and free retakes within a year after graduation. Many certificates and bootcamps also include 1-on-1 mentoring: check course descriptions for more information, including any prerequisites.

Key Insights

• Python for data science is both easy to learn and powerful.
• Python’s libraries and frameworks are ideal for:
• Mathematical functions
• Data structures
• Data visualization
• Top uses for Python data science include:
  • Data analysis
  • Data visualization
  • Artificial intelligence (AI) and machine learning (ML)
• Popular Python libraries for data analysis include:
  • NumPy
  • Pandas
  • SciPy
• Top Python libraries for data visualization include:
  • Matplotlib
  • Plotly
  • Seaborn
• Python is an open-source programming language, meaning its frameworks and libraries are free to download.
• Top free Python libraries for data science include:
  • NumPy
  • Pandas
  • Matplotlib
  • Scikit Learn
  • SciPy
• Top programs to learn Python include Noble Desktop programs like:
  • Data Science Certificate
  • Software Engineering Certificate
  • Data Analytics Certificate
  • Python for Data Science Bootcamp
  • Python Programming Bootcamp
  • FinTech Bootcamp
  • Cybersecurity Bootcamp
  • Python for Automation
  • Cybersecurity with Python
  • Python for Network Security
• The best ways to learn Python for data science include:
  • Live in-person or virtual bootcamps or certificate programs
  • On-demand or self-paced classes
  • Video seminars or tutorials
  • Blog posts or articles
  • You can get comprehensive Python for data science training through an in-person or live online course from Noble Desktop.