A vast and wonderful programming language, Python for Data Science is suited for nearly all your machine learning requirements. And, if you’re a beginner, you’ve come to the right place to begin your journey. 

While we don’t promise to make you a Python expert in 5 minutes (because it’s not possible), through this guide you will know everything that a beginner needs to get started with Python for Data Science.

In this article, we have outlined the various reasons for the popularity of Python for Data Science and have discussed its several packages. Here, we also tell you exactly how much of Python you need to know to start your data science journey. 

Note: Even though this article is focused on beginners, we’ve included some amount of code. But there’s no reason to be scared. The INSAID Research team has written this article keeping you in mind.

So let’s get started then, shall we?

Part I: Why Python Is Popular In Data Science

If not the first, Python is the second most popular programming language you’ll hear about in the data sciences.

What’s the other popular one? It’s called R.

Here’s why Python is so popular among developers, enthusiasts and the overall data science community, and why we particularly teach Python in our GCD® program at INSAID.

1. Simple, Elegant And Easily Readable

Python is easy to learn and understand due to its consistent syntax. Python seems familiar because it reads almost like a regular language with trivial mathematical notations, as we shall see in this article itself.

2. Easy Data Processing and Manipulation

Python is very rich when it comes to input and output operations. The programming language offers multiple interfaces because of which data can be parsed in a couple of lines of code, while languages like Java, C++, might require a whole lot of time just to build basic utilities that will help in reading the data.

3. Easy Access

Most of the common operating system distributions (especially those built on top of Unix) come with the built-in programming language of Python. So, you don’t have to worry about environment setup or any dependencies. In the case of Windows, there are package installers which can get you going in a couple of minutes.

4. Fantastic Ecosystem

Python not only offers ease of implementation and scalability, but it has amazing libraries for Data Science. It has a large ecosystem of ready-made libraries for Data Analysis and Machine Learning.  

Now that you know why Python is one of the most popular languages used in the machine learning ecosystem, it’s important to understand how much of Python you need to learn to start with machine learning algorithms.

Part II: How Much Python Should I Know?

It’s of course very beneficial to know Python extensively, but you really don’t have to be perfect to apply them in machine learning algorithms. Three to four months into training will equip you with enough Python skills to kickstart your career in Data Science and ML.

Even an understanding of basic programming concepts is adequate. For example, at INSAID GCD® we prepare the students with these basics even before the course starts and we have observed that most students irrespective of their background are able to learn Python well.

So, here are 8 things to know in Python to begin your career in Data Science and Machine Learning.  

1. Conditional Statements:

The ability to check the conditions of your statements is essential for any program. Read about what’s needed to make it possible in Python by looking up ‘if’, ‘else’, ‘elif’ and ternary operators to start with.

2. Loops: 

Computers are excellent at doing repetitive work. Loops make this work possible. Familiarise yourself with for loops, while loops and do while loops in Python.

3. Object-oriented programming: 

If you’re completely new to the subject, object-oriented programming can take a bit of time to grasp as a subject.

For now, it’s sufficient to note that this form of programming attempts to blend data with the functions that will operate on them, to ensure that the data remains inaccessible to everything besides the function.

Familiarize yourself with the terms classes, functions, objects, inheritance and polymorphism.

4. Recursion: 

In Python, any function that calls itself in the course of implementation and has a termination function is a recursive function.

It aids in reducing the length of the code.

Another concept that you must only know (rather than understand fully) for now.

5. List: 

A list is an array-like data structure, such as myPythonList=[1,2,3,4,5,6].

The only difference between an array and a list is seen when you perform mathematical operations on them. Their behavior is slightly different, but it’s not essential that you know this right now.

6. Stacks: 

What do we call a pile of books? A stack, right? 

It’s the same in Python.

A stack is a neatly ordered data structure that follows the Last in First out approach.

Therefore, addition and removal of items happens at the top end (just like in the case of a stack ordered from the ground up).

To give you an example with the Internet, think of all the web pages that are “stacked” over each other as you go deeper into a website.

7. Queues: 

A queue is a data structure where the addition of items happens at one end, but the removal happens at the other.

Therefore, it follows a First-in First-out approach. It comes with built in operations like enqueue and dequeue.

To give you an example with the Internet, think of a playlist on YouTube. The last video added will be played last.

8. Trees: 

Trees may be a bit hard to understand comprehensively if you’re a beginner.

For now, just think of it on an abstract level.

Try to visualize a tree as an ordered, but non-linear, data structure (or just see Figure 1 below). 

The various branches have hierarchies and nodes to represent the data.

Trees are commonly used in a variety of data science operations. They can represent various things, such as a decision-making process, the layout of a city, etc.

Once data is structured in the manner of a tree, data science algorithms can be used to solve a variety of problems.

9. Graphs: 

A graph, as you can see below, is a finite set of nodes called vertices, which are connected by ‘edges’.

Just like trees, graphs can be used to represent any connection (thoughts, communication patterns, networks, etc).

Using algorithms to work with data structured within graphs, many difficult problems can easily be solved.

Part III: Python Libraries For Data Analysis & Machine Learning

Python libraries are widely used in Data Analysis and Machine Learning applications. The INSAID Research team highly recommends these libraries to anyone who genuinely wants to learn Python.

But, if you are wondering why these libraries are important when Python is so easy, then you should know that by using these libraries you won’t have to reinvent the wheel each time you implement machine learning algorithms.  

Secondly, Python libraries are often specifically built to perform specific operations. For example, NumPy is used for large, multidimensional arrays and matrices, while SciPy is used for scientific calculations.

Now, let’s explore these libraries individually.

1. NumPy:

What it is: NumPy is a library for the Python programming language. Numpy extends usability of Python by offering support for processing large, multidimensional arrays and matrices. It comes pre-packaged with high-level mathematical functions to operate on the data arrays.

When to use: This library is mainly used to perform mathematical and logical operations on arrays, Fourier transform, shape manipulation, linear algebra operations and random number generation.

2. Pandas

What it is: The official Pandas website claims that it is the go-to Python library for data analysis. And this is true. In particular, it offers data structures and operations for data analysis which involve large numerical tables and time series.

When to use: Pandas is used in the data wrangling process. It is used for data alignment, missing data processing, reshaping data sets, label-based slicing of the data, indexing and shuffling.

It also allows you to insert and delete columns from the data set, perform aggregation and transformation just like a relational database.

3. SciPy

What it is: SciPy is used purely for scientific and technical computing. SciPy works with NumPy to deliver the solutions, where it offers the technical computation while Numpy seamlessly processes the large data sets.

When to use: SciPy offers out-of-the-box support for optimization techniques, linear algebra, calculus, signal and image processing.

4. Scikit-Learn

What it is: Scikit learn comes with various regression, classification and clustering algorithms which also include support vector machines, random forests, gradient boosting and k-means. All these algorithms come pre-packaged like built-in plugins.

When to use: Instead of getting into actual implementations of the algorithms, you can start fiddling with the data directly using scikit-learn. It works like a bridge between numerical libraries like NumPy and scientific libraries like SciPy.

5. Matplotlib

What it is: Matplotlib, a 2D plotting library, is considered to be an extension to NumPy.

When to use: It is mostly used for visualizing the results that are obtained after all the data analysis. Matplotlib generates line plot, histograms, scatter plots, 3D plots and image plot just like popular visualization tools like Matlab and Gnuplot do.

With all of this information, we can now go ahead and set-up Python.

We will also ensure that this environment setup is capable of installing the machine learning libraries that we discussed earlier.

Part IV: Setting up Python

As of now, Python 2.7.x and Python 3.5.x are the two most popular versions of Python. You can start with either of the two versions.

However, Python 2.7.x is more widely used but this is changing now and most Data Scientists are shifting to 3.5.X.

As mentioned earlier, Python is available for all platforms, such as Windows, Linux and Mac.

If you’re running a Linux-based system, Python is most likely pre-installed.

However, even if you’re running Windows, first check if your system has already got Python installed.

To do this, simply open ‘terminal’ in case of Linux based systems and ‘command prompt’, if you are on Windows and type ‘python’.

If you have got Python already installed, you will see an output somewhat similar to this:

If your system doesn’t have Python installed, we’ll walk you through the steps to install it (here we’re using Python 2.7.x).

How to Install Python in Windows OS in 60 seconds:

1. Visit the official Python site, download the installer and follow the steps as you usually would do when it comes to any standard Windows software installation.
2. Once you have successfully installed Python, it is important to set the path in Command Prompt.
3. Open Command Prompt and type the following command, path %path%;C:\Python. Here, C:\Python is the path of the Python directory.
4. Now close the Command Prompt and then open the Command Prompt again and type python in there. It should show an output similar to the one we saw earlier.

How to Install Python in Linux OS in 60 seconds:

Most Linux distributions come prepackaged with Python. Still, let us walk through the process of installing Python.

When it comes to Linux, you will come across mostly two types of Linux distributions, Ubuntu(Debian-based) or Fedora, CentOS(RHEL-based).

1. Open ‘terminal’.
2. In the case of Debian-based operating systems such as Ubuntu, enter command ‘sudo apt-get install python’ (in case, you want to install Python 3, enter ‘sudo apt-get install python3’).
3. In case of RHEL-based operating systems, enter command ‘sudo yum install -y python27’ (or ‘sudo yum install -y python3’).
4. Once done, enter ‘python’ in the terminal. You should see the output which would show you all the details about your python installation.

Note: Here we have learnt how to install Python using the package manager. However, you can also download the source code of Python from the official website and compile it on your machine if you’re feeling adventurous. Follow this link for more details.

How to Update Python for Mac OS in 60 seconds:

The latest versions of Mac OS come pre-bundled with Python.

But, most of the time it comes with a dated Python version. It’s better to have the latest Python installed. Here’s how to do it:

1. Install C compiler by typing, ‘xcode-select –install’.
2. We will use Homebrew as the package manager.
3. $ /usr/bin/ruby -e “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)“
4. Open file .profile to setup environment variable. Type ‘nano ~/.profile’. At the bottom of the file, add the line: export PATH=/usr/local/bin:/usr/local/sbin:$PATH
5. Enter ‘brew install python’ to install Python 2.7.x. For, Python 3.x, enter ‘brew install python3’ in terminal.
6. Once the installation is done, enter ‘python’ to check if it shows all the Python specific information on the console.

Final Thoughts

One of the best programming languages, Python for Data Science and Machine Learning is multi-faceted and highly functional. 

And because it has numerous libraries, it cannot only be used for data visualization, cleaning and creating structural data but can be used by even beginners after some basic learning. Additionally Python is also functional in diverse operating systems. 

We hope this article has helped you cultivate a basic understanding of Python for Data Science and are now more intrigued by the utilities of the popular programming language. 

And, if you want to have in-depth knowledge and practical acumen on not just Python but Machine Learning and AI, you can give us a shout out right here.