Python for Analytics

Whether you are a novice in programming or have experience in different programming languages, In this series of posts, which I call the Python for Analytics course, you will learn the required knowledge and skills to write code in Python by building real applications. This course is designed for beginners willing to work hard to become professional Python programmers, data analysts, data engineers, or data scientists.

After years of computer science and data science education, I believe the best method of learning programming -and any other skills in general- is building applications and applying the knowledge you learn, simply learning by doing. This method helps you accelerate the process of learning Python for Data Analytics. In this course, you will get a hands-on introduction to Python by seeing how to install Python on your computer. Next, we’ll examine the basics of the language, variables, data types, Python syntax, loops, functions, and object-oriented programming. Also, you will learn to work with dates, times, and file formats. In addition, we will look at lists, dictionaries, list comprehension, and Pandas library for data analysis. In this course, We are starting from scratch, from the very beginning. Suppose you’ve never written a line of code in your life, no problem. If you don’t even know where you begin to write a line of code, that’s where we are starting. However, if you have done some programming in the past, that’s great. The course intends to help you learn everything that makes you a professional Python programmer.

What is Python, and Why?

Python programming language has become one of the world's most popular multi-purpose programming languages. The reasons that make Python one of the best programming languages are; first, it’s easy to learn, and Python’s programming structures are more understandable than the other programming languages. The second reason is that Python is a concise programming language; your programs will do more in fewer lines of code than many other languages require. The third reason is that it has an extensive ecosystem of libraries and tools that help developers deal with different scenarios. Python is an interpreted, high-level, general-purpose, cross-platform, open-source, and object-oriented programming language.

Let’s talk about each of these descriptions: Suppose the sequence of instructions called source code is executed directly by an interpreter. In this case, we say, it’s interpreted, but if it’s first converted to machine-level primitive operations by a compiler, then executed, we say, the source code is compiled. Python natively is an interpreter, but it’s possible to compile your Python code.

A High-Level Programming Language

A high-level programming language manages memory and the other aspects of computers for us. It provides a lot of abstract operations that help us write more efficient code. On the other hand, low-level programming languages like C/C++ require you to allocate and manage memory and use instructions and data objects at the level of the machine.

A General-Purpose Programming Language

A general-purpose programming language is designed to build software in a wide variety of application domains. However, a targeted language like SQL is designed to facilitate extracting information from relational databases, but you wouldn’t want to use it to build an operating system.

A Cross-Platform Programming Language

Python is cross-platform, which means Python can run equally on different platforms such as Windows, Linux, UNIX, and Macintosh. So, we can say that Python is a portable language. It enables programmers to develop the software for several competing platforms by writing a program only once. Also, Python is free and open-source. You can download, install, and use it without paying any penny.

An Object Oriented Programming Language

Python supports object-oriented concepts. It supports inheritance, polymorphism, encapsulation, and other advanced concepts in object-oriented programming. The object-oriented procedure helps programmers to write reusable code and develop applications with less code.

Python Libraries

Python provides a vast range of libraries for various fields such as machine learning, web development, and scripting. We can use different machine learning libraries to implement machine learning pipelines, such as Tensor flow, Pandas, NumPy, Keras, and PyTorch. To develop web applications, we can use Django, flask, and pyramids which are the popular frameworks for web development in Python.

Python is one of the most popular and fastest-growing programming languages, and it’s used for front-end and back-end development, data analysis, and data science. In other words, you can make a good living by coding in Python, as the average salary of Python developers in the United States is around 110K-120K USD. The other thing that I need to mention here is that I don’t only teach you how to write code; the primary intention of this series of posts is to teach you how to think like a programmer.

Alright, I think it’s enough for the first post. If you like this post, please comment below. In the next lesson, you will learn how to install Anaconda and Python on your computer.