Data Science using Python

Data Science using Python

A blog about understanding the language of data science by implementing algorithms and leveraging the code to help one become a confident, competent coder in python.

Data Science using Python

A blog about understanding the language of data science by implementing algorithms and leveraging the code to help one become a confident, competent coder in python.

What is Data Science?¶

The term Data Science has been around for some time now. However, it has become more prevalent over the past several years—especially with companies like Netflix, Facebook and Google utilizing it to improve their products, services and advertising. But what exactly is Data Science? According to this definition on Analytics Vidhya:

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

In other words, Data Science involves analyzing large amounts of data to discover trends or patterns that can be used to make predictions or decisions. This process involves tasks such as data cleaning (to remove erroneous data), feature engineering (to add additional features), exploratory data analysis (to find correlations between variables) and machine learning (to make predictions). Here is an example from Analytics Vidhya illustrating how Data Science can be used to improve customer service:

This is one blog that is focused purely on python. The blogger has a very good grasp of the language, and spends some time going over the basics, as well as some more advanced techniques. He also has a nice style of writing and makes everything very clear.

If you’ve been learning python for a while and are looking to go beyond the basics, this blog is a great place to start.

It’s also worth mentioning that there are lots of articles on data science using python, a subject which many other blogs don’t often cover.

The landscape of data is changing at a rapid pace. As a result, several data science languages are being developed. Python is one such language that is gaining popularity because of its versatility and open-source codebase.

This blog aims to explain the basic Python concepts that will help one understand the implementation of data science algorithms in python.

The language of data science is Python. Data Science has been an emerging field in the recent times and the world is more dependent on data than ever before. Data Science has been a buzzword for a while now and it has generated a lot of interest among people who have made careers in other fields.

But what does it take to become a data scientist?

The answer is simple. You must be comfortable working with data and you must be extremely good at coding in Python. There are other skills too, like machine learning algorithms and mathematical concepts that you need to know but they always come secondary to the two core competencies mentioned above. So, how can you become proficient in Python? By using it as much as possible!

If you are starting out with Python, I highly recommend you to use an IDE like Spyder or Pycharm to write your code and run them in the interactive console. The advantage of using an IDE over a text editor is that you can directly run your scripts from there itself instead of having to open the command line every time. You will save a lot of time that way.

I’m going to do all my examples in Spyder because it’s very easy to use, but feel free to use any IDE that

The first question I often come across from people interested in data science is: what language should I learn?


If you don’t know python, go learn it. In fact, if you don’t know python, stop reading this and go learn it. You can come back to this later.

Why Python? It’s one of the most popular languages used in data science (and general programming), so whether you get a job or start your own company, chances are you will need to know how to use python. We want to be able to look at a set of instructions and understand what is happening. A key aspect of becoming a better coder is being able to read code and understand how it works. The reason we choose python is because it is one of the most human-readable programming languages out there. (That doesn’t mean that writing code is easy! But python makes it as easy as possible.)

Let me give you an example: imagine I am trying to teach you about functions by describing them without showing any examples. Here are my descriptions for two different functions:

The second language I learned was Python. It is easier to learn than R and provides a variety of tools to do data science quickly and easily. It is the language of choice for a number of popular packages such as numpy, pandas, and scikit-learn (to name a few).

Python is a very general purpose language that can be used for a variety of tasks. That being said, I am going to focus on using it as a tool for data science. This means we will be focusing on importing data, cleaning data, visualizing data, and analyzing it. Using Python for these tasks is not necessarily better than R or other languages. However, the versatility of this language makes it easy to jump from one part of the project to another without having to change languages or learn new syntax (at least most of the time).

This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.

Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.

You will learn how to use Python’s built-in data structures and functions, as well as how to create your own. You’ll also learn how to take advantage of the powerful Python libraries that are available for you to use.

After completing this course, you’ll be ready to move on to intermediate and advanced courses on machine learning and data science. You’ll also be able to put your new skills into practice by building your own applications!

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