Why Python Programming Projects Shouldn’t Scare You
Unlike its namesake, the Python programming language isn't scary. You'll learn the details of this powerful tool during your SCU Online MBA program, but in the meantime, here are some key terms and concepts about Python programming you should know:
Python Programming for Beginners
What is Python? According to research from O'Reilly Media, more than half of data scientists use Python as a data science tool. As the years go by, the number of Python users only seems to grow. Data is increasingly utilized in making strategic business decisions, and Python helps make sense of data in a clear, concise, and organized way.
Python is a general purpose and high-level programming language. It allows users to develop a series of web applications, GUI applications, websites, and more. Python allows the user to focus on the core functionality of the task at hand by taking care of everyday programming tasks. The language uses simple syntax rules to keep the code readable and the application maintainable for each user. While Python can help with web application building, it is also an essential asset in data analysis.
Python for Data Science
Businesses like to work with Python for many reasons. Not only is the language robust in its building capabilities, but it also creates readable and maintainable data. This makes it easy for businesses to integrate into their daily operations with minimal training, even for non-programmers.
Commonly referred to as the Swiss Army knife of coding, Python can handle multiple functions, including various types of programming, data mining, and website construction using a single language. It’s straightforward and allows you to manage a breadth of applications and their data in one place.
Businesses also choose Python for reasons, such as:
- Python is compatible with many other programs
- The availability of libraries make it easy to function
- The language allows for easy and seamless communication between employees and departments
- Python has a broad and diverse user community to turn to for help and strategies
Python Libraries: The Heart of It All
Python has a few key defining terms that are crucial to the day-to-day use of the program.
Modules are files filled with some Python code, and packages are directories for sub packages and modules. And crucially, there are also libraries. The distinction between a Python library and package is sometimes blurred, but generally speaking, a Python library is a reusable string of code that you may commonly use in your projects and programs. These libraries do not pertain to any specific context in Python; they are loosely described as a collection of core modules.
Many standard Python libraries are specifically used for data science. A few of the most common are:
- Matplotlib: This library helps with data analysis and numerical plotting
- Pandas: This is an essential library for data science. It produces powerful, flexible, and fast data structures that work with structured and time-series data
- Requests: This is a Python library that lets you send HTTP/1.1 requests, form data, headers, multipart files, and parameters. It also enables you to access the response data in the same way
These are just a few of the many Python libraries that can be used to aid in your data science efforts.
Why Python Is Better Than Excel
The Python versus Excel debate never seems to end. Both of these tools are great for data scientists, but with all of the attributes of Python, it has clear advantages.
For example, as a data analyst, your job is essentially broken down into three parts: Getting the data, analyzing the data, and presenting the data.
If you are only relying on Excel, you can really just make the most of one out of these three tasks: analyzing the data. Excel only provides limiting and manual options for getting data and presenting it as well.
Python uses automated processes to secure data in a way that is readable and manageable for the user. It can obtain data from any source and connect to any database.
When it comes to analyzing data, many start with or rely upon Excel programming. Excel is excellent for one-time or simple projects, while Python can provide support for ongoing or robust projects. This is where the libraries are useful. These libraries can help create automated services that capture and present data in simple ways.
Why Business Students Should Know the Basics of Python
Business students specializing in data analysis are not required or expected to become masters of Python, but knowing the basics is a great asset to have. It has become one of the most widely used programs for data collection, organization, and analysis and can be an asset when making decisions.
Learning Python from a data analysis standpoint, rather than a developer’s, can help you better understand some of the core principles of data. Explore the Online MBA at Santa Clara University to learn more ways in which you can leverage this free, robust programming language in your career.