Today, Python is by far the most popular programming language. Python programming has several advantages over other languages, including its ease of use. Statisticians regularly use Python because of how versatile the language can be when analyzing data. In this guide, we’ll show you the best and most popular ways you can master Python for statistics.
What Is Python?
Python is a general-purpose programming language. It is an interpreted, object-oriented, and dynamic programming language. Its high-level built-in data structure is one of the features that makes it attractive for developers for rapid application development, statistical functions, artificial intelligence, and analysis tasks.
Because of its versatility, Python can be used with all kinds of data, coding, and even mathematical computations. Python’s syntax is straightforward to read, making it easy to learn and use by novice and expert programmers alike.
What Is Python Used for in Statistics?
Python has long been considered a simple programming language to learn, at least from a syntax perspective. In addition to its active community, Python has a wide range of resources and libraries. Working with statistics doesn’t require complex programming, and using languages like Python and Ruby will make tasks easier.
Descriptive statistics are included in the Python language by the built-in statistics module. This is useful In cases where your datasets aren’t too large, or when you’re unable to rely on other libraries being imported. NumPy, a third-party library for numerical computation, enables users to work with arrays of single- and multidimensional data.
Python is one of the most popular languages among statisticians because it can be used to manipulate and visualize data as well as build statistical models. Python is a high-level language that is open-source, interpreted, and provides a great approach to programming objects. Python offers a wide range of mathematical, statistical, and scientific capabilities.
How Long Will It Take to Learn Python for Statistics?
According to different estimates, learning Python can take anywhere from three to 12 months. A statistician’s use of Python differs from that of a programmer, so the length of time it takes to master the skills depends on your objectives.
Python is often used in statistics as a tool for retrieving, cleaning, and visualizing data. In Python-based statistical analysis, you should spend more time learning specific modules and libraries such as Numpy and Pandas.
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Why Should You Learn Python for Statistics?
Python is easily the most popular programming language used by statisticians. Check out the list below to learn more about the top five reasons why you should learn Python if you’re hoping to start a career in statistical analysis.
Python is easy to learn
For a beginner, coding may seem intimidating. Python, however, is an exception. Compared to more complex languages like C, C++, and Java, it has a remarkably simple syntax and vocabulary. You can take advantage of a built-in Python statistics library to help you master statistical data analysis tasks quickly.
Python Has a Huge Community
There is a large community of Python developers, which means there are numerous resources available to help newcomers learn basic statistics functions. There are many websites that work as a massive platform for the entire population of Python developers to collaborate. One of these Python community websites is GitHub.
As a massive repository of code, GitHub not only gives you knowledge of Python, it also gives you the experience of working with a source code management platform. This versatility makes Python a natural choice for a wide variety of projects today.
Job Growth and Career Options
A statistician can find multiple career opportunities with Python. According to the US Bureau of Labor Statistics (BLS), the overall employment of statisticians is projected to grow 33 percent from 2020 to 2030, much faster than the average for all occupations.
Some of the biggest companies in the world use Python as their primary programming language for data analytics and statistics. With so many companies relying on data analysis to drive business outcomes, Python skills are in high demand and these professionals can earn high wages. BLS also reports the average salary of a statistician is $110,860.
How Can I Learn Python for Statistics?
Analyzing statistical data with Python has a lot of benefits. However, Python has limitations when dealing with numerically heavy algorithms and large amounts of data. For these tasks, use Numpy or Pandas. Here are three of the best methods an aspiring statistician can use to master Python.
Coding bootcamps provide intensive knowledge and skill training in a compressed time frame. Learners can gain coding proficiency without enrolling in a long-term college program. The best Python bootcamps will help you gain the statistical and coding skills you need to start your tech career in just a few months.
Like any programming language, it can be a challenge to learn without any additional guidance. Online Python courses make it simple and easy to learn, develop, and advance your programming skills. Many online learning platforms frequently offer special offers to keep courses affordable, and some are even free.
Online classes such as Udemy’s Python for Statistical Analysis are a great way to develop the Python skills for your specific career ambitions. In a series of courses you can learn about include statistic variance, linear regression, linear function, cumulative distribution function, as well as how to work with random variables, maximum values, and categorical variables, and more.
Books can be a great resource to help you learn Python programming because they are often more detailed than online courses, and allow you to study on your own time. For example, An Introduction to Statistics with Python by Thomas Haslwanter is a great resource to study topics from survival analysis to linear regression analysis and bayesian statistics.
Top Python for Statistics Libraries
Python has been built with extraordinary libraries for statistics used by programmers and statisticians alike. NumPy, Pandas, and TensorFlow are the best tools for statistics and data visualization.
Here are the top four Python libraries for statistics:
- TensorFlow. This library can be used in a variety of fields. Tensors are partially defined computational objects that eventually produce a value, and TensorFlow is a framework allowing you to define and run computations involving tensors.
- NumPy. This package provides multidimensional objects called arrays and the tools needed to work with them.
- SciPy. Around 600 contributors actively contribute to SciPy’s GitHub page. Since it extends NumPy and provides a number of user-friendly and efficient routines for scientific computations, it is extensively used for scientific and technical computations.
- Pandas. This library is heavily used for data analysis and cleaning. Dataframe CDs provided by Pandas are a fast, flexible, and intuitive method for working with structured data.
It only takes a few minutes to learn how to import a Python module. If you are wondering which Python module to use for a specific job, Google can be a great service. You’ll find the information you need.
How to Learn Python for Statistics: A Step-by-Step Guide
If you are a beginner interested in the statistics field, getting started can be overwhelming. The following guide will help you navigate what tools and skills you should focus on first on your journey to learning Python for statistics.
Learn OOPs Concepts
Object oriented programming (OOP) in Python refers to structuring the program using objects and classes. Object oriented programming concepts provide essential features such as inheritance, polymorphism, encapsulation, and styles.
Data Structure and Algorithms
Once you know the basics of Python, you should learn data structures and algorithms. This will introduce you to sorting algorithms, trees, queues, stacks, and linked lists. When you code for data structures, you will explore predefined classes and objects, which will allow you to familiarize yourself with the language before you tackle real-world projects.
Get to Know the Tools and Libraries
You will become familiar with essential Python libraries as you become acquainted with Numpy, Pandas, and MatPlotLib. All of these are tools that will help you become more efficient and effective in your work, and are essential to know if you wish to start a career in the field.
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Creating projects on your own once you know some basic skills is the best way of building your abilities. Creating a simple to-do list or habit tracker may be a good first project. Once you have a starting point, you can add new features or technical sophistication as you gain more skills. This also helps you build a portfolio that can prove your skills to potential employers.
For example, you might start by making a large data set and organizing it into confidence intervals or continuous intervals. Next, you could build a data set that allows others to add to it and have the option to moderate from a separate server. Fill in any gaps in your knowledge with a review of the Python fundamentals, or turn to the Python community for guidance.
A statistician’s primary responsibility is to organize and analyze data, which in the real world comes in many forms. Data wrangling is used to manipulate raw data so that it can be used for analytics. Data needs to be cleaned and transformed before it can be analyzed and modeled.
As you learn to code, start with the basics, such as variable types, loops, conditional statements, classes, and methods, and build your way up to mastering the rest. Join coding challenges and problems online to gain a whole new level of confidence and hands-on experience.
Start Learning Python for Statistics Today
Python can be challenging to learn if you don’t have a tech background, but it’s certainly not impossible. With perseverance and help from the numerous educational resources available, such as coding bootcamps and online courses, you’ll be ready to start applying for available statistician positions before you know it.
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