A majority of today’s programmers and data analysts use Python. This popular language never ceases to surprise its users when it comes to big data and analysis tools. Many data analysts use Python’s advanced features to conduct statistical analysis and create deep learning models.
This popular language is easily accessible to anyone who wants to learn. In this Python for data analysis tutorial, we walk you through the process of mastering this programming language. We tell you the top reasons to learn Python and show you the learning options available
What Is Python?
Python is a general-purpose programming language. This interpreted, object-oriented, and dynamic language is used by millions of data scientists, data analysts, educators, and financial advisors around the world. Its high-level built-in data structure makes it attractive when it comes to rapid application development.
This language boasts a large and active community, as well as a wide range of resources and libraries. Python makes using emerging technologies such as machine learning and data science on a programming platform much easier. When it comes to data science applications, using Python makes tasks more manageable.
Because of its versatility, professionals can use Python with all kinds of data and for all kinds of mathematical computations. Python syntax is straightforward, making it easy to learn and use, whether you are a novice or an expert analyst.
What Is Python Used for in Data Analysis?
With countless mathematical, statistical, and scientific applications, Python is one of the most popular languages among data scientists. In data analysis, Python can be used to build models and retrieve, clean, and visualize data.
Moreover, this language boasts a wide variety of libraries that are great for data science and data analysis projects. NumPy and Pandas, for example, are widely in use among data scientists and data analysts.
How Long Will It Take to Learn Python for Data Analysis?
There is no clear consensus on how long it may take to learn Python for data analysis. Most people would agree that it takes two to six months to learn the fundamentals of this language. However, mastering some of its most complex libraries can take years. When it comes to learning Python for data analysis specifically, you can assume that it takes significantly longer than learning Python for general use.
Why Should You Learn Python for Data Analysis?
Python is considered one of the key languages to learn in the field of data science and there is a growing demand for Python experts. In addition to enabling rapid improvements, Python can interface with high-performance algorithms written in Fortran or C, making it useful for data mining and scientific computing, among other applications.
The following are the top five reasons Python is so popular among data scientists and machine learning specialists and why you should learn Python if you want to become a data scientist.
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, Python has a straightforward syntax and vocabulary, so it’s relatively easy to learn.
Non-coders can learn Python for free or at a low cost. It is easy to understand and can be added to your programming arsenal fairly quickly. In short, Python provides an easy entry point into data science.
Countless Tools, Libraries, and Applications
A data scientist’s primary responsibility is to analyze data, which in the real world comes in many forms. Data must be cleaned and transformed before being analyzed and modeled, and Python is an excellent tool for that. In addition, Python experts use data wrangling techniques to manipulate raw data so that it is fit for analytics.
Python’s open-source libraries let data scientists carry out these tasks. Python has many libraries that are regularly updated. NumPy, Pandas, and Matplotlib are among the best ones for data analysis and visualization.
You can learn NumPy and Pandas easily, as long as you are familiar with applying some mathematical formulas, running some statistical equations, and cleaning up data. For example, it takes only a few minutes to learn how to import a Python module. If you are wondering which Python module to use for a specific job, just Google it. You’ll find the information you need.
Huge Community
Python is very popular among data analysts, which means you have an active community of people to turn to if you get stuck during a project. With the support of this community, you can master this language and its most complex libraries.
Rising Popularity
If you want to get into data analysis, you won’t get far without knowing some Python. Many companies rely on this language to build their frameworks. For instance, Facebook and Netflix are increasingly using it for their data analysis projects. Additionally, Google is the creator of TensorFlow, a popular Python library used in machine learning and artificial intelligence.
Job Security
A career in Python is more stable than many other tech occupations. This language has been around for thirty years and it’s constantly adapting to fit new market needs. Your career goals might change and the job market may experience a decline, but knowing Python will always be a useful skill that will make you more employable.
If you develop expertise in Python and are able to apply it to the field of data analytics, you can become a data analyst. According to the US Bureau of Labor Statistics, the job outlook for data analysts is 25 percent from 2020 to 2030, which is much faster than average.
How Can I Learn Python for Data Analysis?
Professionals can learn Python in a variety of ways these days. Whether you’re interested in learning online or in-person, aspiring data analysts have many options to learn the fundamentals of this programming language.
Coding Bootcamps
Coding bootcamps are short but intensive programs that instill the skills needed to land a high-paying job in the tech industry. The bootcamp curriculum focuses on the acquisition of the skills needed to join the workforce through real-world projects.
More and more employers are beginning to favor bootcamp grads because they come to the job with plenty of practical experience. Moreover, bootcamp programs come in many formats to fit the needs of every student. You can find full-time, part-time, online, in-person, self-paced, and instructor-led options.
In addition, coding bootcamps boast extensive career support services, such as mentorship and access to a hiring network, to help students land a job as soon as possible after graduation. In short, the best Python bootcamps are an ideal option to get the training you need for a career in software development, data science, and data analysis.
Online Courses
Due to Python’s popularity, online courses are popping up to deliver quality education to millions of people around the world seeking to learn the fundamentals or bring their programming skills to the next level. Online courses can help you understand the basics of Python’s predictive models, box plots, numeric variables, and digital content.
Online Python courses make it simple and easy to learn, develop, and advance your programming skills. Many of these courses allow you to work on challenging individual and group projects where you get to apply what you are learning to real-world data analysis problems.
In addition, many come with access to useful resources such as video tutorials or step-by-step guides to accomplish certain tasks. Popular platforms for Python-related courses include edX, Coursera, Udemy, and Udacity.
Books
Books can be a great way to learn this programming language. A book allows you to learn at your own pace and go as deep as you want into a particular Python-related topic. There are plenty of excellent books out there that can teach you different aspects of Python and its applications in data analytics.
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Top Python for Data Analysis Libraries
There are numerous Python libraries for data science built into Python, which analysts use every day to solve problems. These libraries provide you with unlimited access to helpful information and peers in data science.
Below, we’ve compiled the top four Python libraries for data science:
- TensorFlow. TensorFlow is a framework that allows you to define and run computations involving tensors, which are partially defined computational objects that eventually produce a value.
- NumPy. The NumPy library is a general-purpose package that provides multidimensional objects called arrays and tools for working with them.
- SciPy. Since it extends NumPy and provides several user-friendly and efficient routines for scientific computations, SciPy is extensively used in data science and data analysis.
- Pandas. This Python library is popular for data analysis and cleaning. Data frame CDs, provided by the Pandas library, are a fast, flexible, and intuitive method for working with structured data.
How to Learn Python for Data Analysis: A Step-by-Step Guide
Your journey to learning Python for data analysis is going to be a long one, and it all starts with installing the latest version of the language and choosing a course or program that’s right for you. Follow the steps below, and you’ll be on your way to learning the ins and outs of using Python for data analysis.
Install Python
The easiest way to download Python is to go to the official site. CPython, the Python implementation in C, is available on the downloads page. It is recommended that you download Python 3. Expect a file size of 30MB.
Learn OOPs Concepts
Object-oriented programming (OOP) in Python refers to structuring software design using objects and classes. Some OOP concepts you want to learn as soon as possible are inheritance, polymorphism, encapsulation, and styles.
Study Data Structure and Algorithms
Once you know the basics of Python, you should learn data structures and algorithms. Get familiar with sorting algorithms, which will improve your understanding of concepts such as loops, nested loops, conditions, and recursion.
When you study 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. At this stage, you should also aim to get a good handle on trees, queues, stacks, and linked lists.
Do Projects
Practice makes perfect, as the old adage goes. The next step in your data science journey is to apply what you have learned thus far. To do that, we recommend starting for own projects. Neat project ideas include creating a simple to-do list or habit tracker.
Once you are done with your initial project, move on to more challenging ones. Your next project could be to build a dataset that others can add to but make sure to have the option to moderate from a separate server.
Choose data analysis and data science projects that you are passionate about. If you come across an interesting project that you think you can build, give it a try as soon as possible. Get help when necessary by asking the Python community or reviewing a tutorial.
Never Stop Learning
When it comes to Python, there is enough to learn to fill a lifetime. If you want to be at the top of your game, continuous learning is a must. A good way to do this is by joining coding challenges or helping other users with their problems. The point is to always strive to expand your expertise.
Start Learning Python for Data Analysis Today
Python is a key skill in the world of data science and data analysis. Its rising popularity among data analysts is a testament to this. If a career in this industry sounds appealing, then learning Python should definitely be on your mind.
There are many avenues at your disposal to take your first steps in using Python for data analysis. There are plenty of coding bootcamps out there to equip you with the knowledge and practical skills you need. Likewise, you’ll have no trouble finding engaging and easy-to-follow courses as well as books.
Once you’ve learned the basics of this language and become familiar with more advanced concepts, try your hand at some of the projects we suggested in this guide. In no time you will develop a portfolio of projects and be ready to become a data analyst.
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