Python Basics for Data Science

Python is one of the most widely used programming languages in the world. Python’s syntax, or the words and symbols required to make a computer programme operate, is straightforward and easy to understand. It’s just a list of English terms! It supports a variety of programming paradigms, but most people think of it as an object-oriented programming language.

Everything you create in an object-oriented programming language is an object, and various objects have different features. You may also operate on different objects in different ways. It works well with various software components, making it a general-purpose language that can be used to create a whole end-to-end pipeline — starting with data, cleaning a model, and then building it directly into production. This is why Python is a popular language for coding and is used widely for data driven decision making.

Python over R as the most popular data science language?

Python is a general-purpose language used by data scientists and developers because of its straightforward syntax, making it simple to communicate across organisations. Python is popular among programmers because it allows them to communicate with others. The other argument stems from scholarly studies and statistical models.

R has superior statistical packages than Python, but Python offers deep learning, structured machine learning methods, and can handle larger data sets. Python is becoming more popular as people get more interested in deep learning.

Python for Beginners

Python is a great first programming language for novices since it has a basic syntax that helps you get started quickly. It is versatile and can be used for almost anything. It will try to figure out what you’re saying.

Before you can move on to coding via Python, you must first master the fundamentals of Python. Here’s a rundown of fundamentals to get you started:

  1. Understand the different data types (integers, strings, and floating-point numbers) and how they differ.
  2. Learn about loops and conditionals. Loops repeat a piece of code numerous times, while conditionals inform the computer when to stop.
  3. Learn how to manipulate data by reading data into your Python programme, performing calculations on it, cleaning it up, and possibly even exporting it out to a CSV file. Because data manipulation is such an important element of a data scientist’s job, you’ll want to know exactly how to do it.
  4. Algorithms – you may use algorithms to design models and even make your own.
  5. Data visualisations are an interesting aspect of data science. There are a number of Python modules or packages that can assist you with this.
  6. Communication – To reinforce your learning, start articulating what you’ve learnt in a way that others can understand.

Level of Python to learn for application in any data science bootcamp

Before moving on to something more sophisticated, there are a few fundamentals that you must master. Those fundamental Python constructs, such as data types and data structures, lists, the dictionary, and other similar constructions.

You should also be familiar with the following three fundamentals:

  1. True and false tests are conditionals. You’ll basically have some form of input, test it against a condition, and then run one block of code if the condition is true. If it’s untrue, you can end up with a completely different set of instructions. It functions as a sort of gatekeeper.
  2. Loops are code segments that can be repeated. A loop can be used whenever you need to repeat the same activities on a large number of items in a group. This would run over all of the different parts in your input group to get some sort of standard output.
  3. Reusable code (not to be confused with repeatable code) is what functions are. You’ll build a function if you wish to conduct the same type of computation multiple times in your code. You can use that code again and again to get the same results.

Python Libraries

It’s difficult to talk about Python without mentioning libraries. A library is a collection of preserved code that has been written for you by someone else. You can import different pieces of code, so you don’t have to do everything yourself!

Here are a few libraries that are ideal for newcomers:

  1. Random Number Generator – This is used to generate random numbers, which can be entertaining. You might use this to create your own game.
  2. Math – This one provides access to a variety of math functions such as square root, cosine, and sine, among others.
  3. Collections – This will assist you in interacting with your computer or collections, giving you actual access to additional information.


  1. How is Python useful for data science?

Python is a general-purpose language used by data scientists and developers that, because of its straightforward syntax, makes it simple to communicate across your organisation.

  1. Which Python version is best for data science?

Tensorflow and other popular and new frameworks and modules are supported in Python 3.

  1. Should I use R or Python?

R is considerably superior for statistical learning because it was designed as a statistical language. Python is a better choice for machine learning because of its production-ready flexibility, particularly when data analytic tasks must be connected with web applications.

  1. How long does it take to learn Python?

Learning the fundamentals of Python programming, such as object-oriented programming, basic Python syntax, data types, loops, variables, and functions, can take anywhere from five to ten weeks on average.


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