You're about to embark on your journey into data science, and everyone recommends the basics of programming. You've chosen Python and are now overwhelmed by the numerous learning resources at your available. Maybe you're overwhelmed, and due to your inability to think, you're putting off the first step of learning to program in Python. In this post, I'll act as your guide through the fundamental information you require to know to learn Python to get started with data science. I'll assume that you don't have any prior programming experience or be from a non-technical background. If, however, you're being educated in either a computer science or technical background and are familiar with previous programming languages, and are looking to switch to Python and its syntax, you can utilize this article as a basic overview of the fundamentals of Python language. Whatever the case, it's the goal in this piece to guide you through the maze that is the Python language and its intersection with data science. This can help you begin quickly or you can also join this course online to become Masters Python. 1. What is the reason for Python? The very first (and well-known) query you might be asked is: Python can be described as an inter- high-level programming language that's simple to comprehend and understand. syntax Python offers a variety of learning resources that are available to Python Python has a wide range of capabilities and many applications, as per Python Developers Survey 2020, which was conducted by Python Software Foundation and JetBrains 2. Use Cases to Python To answer this question, let's look at the lifecycle of data as illustrated below. In essence, there are five main steps, which include data collection, data cleansing as well as exploratory data analysis model development, and model deployment. Each of these steps can be executed in Python, and, once coded, the code generated is reusable and thus can be used to create other projects. 3. Mindsets to Learn Python Understanding the reason you're learning Python can aid you in staying motivated when life gets out of the way. Being consistent and having excellent habits will only get you to a certain point. A clear motivation for studying can increase your motivation and help you to get back on track. 4. Python Coding Environment Integrated Development Environment (IDE) is the space where you work that houses your code, but not just that, but it also offers additional features and convenience that will enhance your code. 5. Basics of Python This is the most crucial aspect since you'll be doing this for nearly all of your work. It's possible to think of this as similar to the alphabets that are the foundational elements are used to spell words. Define and use variables that let you save values for later use. Different types of data give you the ability to utilize data (i.e., whether it's categorical or numerical data that is either quantitative or qualitative). Operators allow you to apply filters and processing to data. Conclusion In a nutshell, this article examines the world of Python when applied to the field of data science. As a self-taught computer programmer, I am aware of how challenging it is to not just learn how to code but also apply it to solve problems with data. It's not going to be an easy journey; however, if you persevere and persevere, you'll be amazed by the amount you can accomplish using Python to help you on your data science adventure. I hope this article offered some ideas you can apply to your own learning as you progress through your journey of learning. Leave a comment and suggest any suggestion that works for you!