The Beginner’s Guide to Learning Python for Data Analysis

Here is your beginner’s guide to learning python for data analysis:

So let’s say you have built a good interest in data analysis, and you want to get started. After spending so much time trying to decide on the best tool for you, you finally narrowed down to Python. But now, How do you really go about it? Where do you start? This might take another time of you overthinking. But I don’t want you to be in that situation. Having had an experience of successfully learning data science in 3 months, I discovered an effective way around it. A way that I can always apply if I were to do it again. Therefore Today, I want to share with you my guide on how to learn Python for Data Analysis. This guide is best suitable for everyone who wants to start learning Python for Data Analysis, even when you are an absolute beginner. So if you are interested in this beginner’s guide from my own experience, keep reading. And let’s get started.

1. Learn The Python Programming Language First

How do you command your computer to write ‘Hello World’ in Python? That is what exactly I want you to find out at this stage. Probably the best way to tell you this is that: You should first learn the rules and laws, and how coding is done in Python. How do you even write a program in Python? How do you write a single Python’s command that a computer can understand to give you that output inside your mind, that you actually long to see? Heck, everything you are going to do from the time you write your first ‘Hello World’, is going to revolve around these rules and laws. It’s the anatomy of Python.

So, at this stage of your beginner journey, I want you to focus on at least 3 goals. Number one, learn the Python’s syntax. These are the rules that govern how every code in Python is written. Number two, I want you to learn the concepts of data types in Python. This basically teaches you what type of codes you are dealing with every time you are coding in Python. And last but not necessarily the least, I want you to focus on understanding the concept of Object Oriented Programming in Python (OOP). If you can understand what OOP is and how you can do it in Python, this will set you at a far stage of your learning curve. I love to say that the concept of OOP almost always takes you from being a coder to a programmer. These three concepts (syntax, data types, OOP), are in my opinion the key concepts to focus on if you want to learn any programming language.

2. Learn Python’s Numpy and Pandas Libraries

Now, when it comes to the world of data analytics, Python is one super big language with a lot of ‘techniques’ inside it for helping you do your things. In technical terms, Python has specific libraries and packages dedicated to helping its users to do whatever task they are interested to do with Python. And when it comes to data analysis and manipulation, there are two mega Python libraries that you cannot miss on the list. The two are: Numpy and Pandas. In my opinion, these two are the most amazing libraries you MUST learn if you want to have fun with data analysis in Python. And I say ‘MUST’ because the truth is, you can barely do any data analytics task in Python without involving these two libraries.

Numpy helps you understand the techniques of working with arrays. Well, that looks complicated. In simple terms, it helps you apply the techniques of Linear Algebra into your data analysis work. Okay, that must be super technical to the next level (I didn’t want it to reach there). The thing is: the Numpy library is going to give you the tools and knowledge to work with the rows and columns that you are always going to find in the data you are handling. So if you don’t want any mess or struggle in future analyzing and manipulating your rows and columns, you better learn the Numpy library.

The Pandas library on the other hand takes your data analysis skillset to the next level. While numpy focuses on rows and columns, this one now focuses on the whole data that you are handling. You want to make all the customer names on that data appear in capital letters, you better consult Pandas. Oh, you think there is some data values that are missing and you want to delete them? Did you consult Pandas? Because Pandas knows exactly how to help you do that.

With the above two libraries, you are always good for any data analysis and manipulation task.

3. Learn The Seaborn and Matplotlib Library

You have the knowledge of Python basics in syntax, data types and OOP, so you have the bedrock of the language. And you can also manipulate and analyze your data from this and that angle. That’s cool of you. I respect you. But there is always something you will always want to do: Visualize your data. In the world of working with DATA in Python, there are two libraries that have dominated the Visualization sector. We have Seaborn and Matplotlib. These two data visualization libraries work just more or less as the data manipulation libraries mentioned above.

Just as Numpy is most always used for analyzing and manipulating one-dimensional kind of data, Matplotib is most of the times also used for visualizing one-dimensional data. Seaborn on the other is good for handling visualizations that involve two-dimensional data. So if you have your data stored in an excel workbook somewhere, Seaborn will help you do a lot of magics with it.

A lot of the times, however, the Matplotlib and Seaborn visualization libraries work simultaneously together. So, having knowledge of both libraries is vital. It will give you tons of opportunities to have fun with your data.

Steal This One Secret EVEN Before You Start

One of the most valuable habit you can develop as you learn, is documenting your work. You need to keep a track of your work. All the many errors you see when you write your first Python program, up to your most satisfying project, need to to be recorded somewhere. And I will tell you now why this is just so important. I haven’t been actively doing coding for the past like 9 months. But still if I am to pick up a new data analysis project Today, I will still do it well. How do I do it? I have all my previous work very well documented. So, if there is a concept I have probably ‘rusted’ on, I can always go back and polish it up through my previous similar projects.

The game of documentation is the only game to winning when it comes to coding. I remember having a conversation with one of my friends who was interested to learn coding. And she asked, ‘Do you have to cram all the codes forever?’. I say, ‘NO. We don’t cram’. Every good programmer knows that. I don’t have to remember nothing. We don’t cram. We document. It’s how we are crushing it. So it’s all about building your skillset. You better focus on that.