Why Python Language is important in Data Science and Machine Learning?

Today we learn about Why Python Language is important in Data Science and Machine Learning

 

Python is a programming language as well as a scripting Language. The benefits of Python are that it is easy to understand syntax and in comparison, with other programming languages, this language can be learned easily and quickly.

Python is an open-source language. Hence, the growth rate to learn Python is more than other languages (like Java) as per the analysis.

 

Inbuild libraries in Python make the work easy, which acts as an added advantage in its bag. Some of the most adored and widely used libraries in Python are:

1. Statistical Analysis Libraries: NumPy, SciPy, etc
2. Machine Learning: sci-kit-learn
3. Deep Learning: OpenCV, TensorFlow, etc

There are many more libraries that we can use in Data Science and Machine Learning.

 

Every library has its own set of pros and cons, where the pros outweigh the cons by a large margin.

It is due to these libraries that programmers love to use Python.

Not only the libraries, Python can also easily solve complex problems with less time complexity and space complexity, following all standards of required algorithms.

 

What is Jupyter-Notebook? 

 

It is an open-source application used by many Data Scientists. Notebook is a Graphical User Interface where you can write your code and can visualize 2D or 3D plots.

 

You can download the application either on your local server or can have it on an online platform.

 

Google provides Google collab where you can create your own notebook or can edit your pre-defined notebook.

 

Google collab is synced with Google drive apps where you can save your notebook. IBM studio also provides an online Jupiter notebook.

 

What are the steps in Data Science problem-solving process? 

 

Data Science is a large field. Hence, the steps involved in the process of solving complexities will also have too many.

 

Here are some of the major steps in the Data Science problem-solving process:

1. Data Collection
2. Exploratory Data Analysis (EDA)
3. Data Visualization

 

Data Collection:

It is the first step in the data science problem-solving process. This is the foundation upon which the entire data is set.

 

Careful planning is essential before collecting the data. There are different methods of collection of data such as census, sampling, primary, secondary, etc.

 

And the investigator should make use of the correct method. The method chosen is based on different criteria and an expert will know exactly how to pick the right one.

In Python, data can be easily collected from the database using different modules like PyMySQL, sqlalchemy, MySQLdb, etc.

 

These libraries allow us to connect with the SQL database and for NoSQL databases like MongoDB, we have library pymongo.

 

The other sources for collecting data are:

1. Using API:

We can easily use REST API in Python using requests and JSON modules. Using REST API we can easily fetch Twitter, Facebook, or Google data.

1. Web Scraping:

We can scrap the data from websites using BeautifulSoup in Python.

1. Online Repositories
2. Through Surveys

Exploratory Data Analysis:

The data presented should be carefully analyzed for making inferences from the presented data such as measures of central tendencies, dispersion, correlation, regression, etc.

In this step, the business question is converted into a data science question, and the solution to the question is figured out.

 

Statistical Analysis is performed using NumPy and SciPy. Pandas are also used for exploring and analyzing data.

 

Pandas can be used for either numerical data or categorical data.

 

Data Visualization:

The mass data collected should be presented in a suitable, concise form for analysis.

 

The collected data may be presented in the form of diagrams or in graphic form. In Python, there are many libraries that can be used to represent our data interactively.

 

Matplotlib is the most widely used library. We can use this library to generate all basic graphs and charts.

 

Multiple graphs on the same figure can also be plot using matplotlib. For advanced graphs, we can use libraries seaborn and plotly.

 

You can use Dash Framework if you want to create a dashboard using Python and relatable data science skills.

 

What are the three used in Machine Learning Process?

Machine Learning needs no introduction to those like you, who have come so far in this blog to know why Python language is important in Data Science and Machine Learning.

 

Hence, let us directly go onto the three steps that are further used in the process of Machine Learning:

1. Feature selection
2. Building Models
3. Evaluation and Deployment

Many top companies like Amazon, IBM, Google are using Python for data science and machine learning.

 

With this knowledge containing some of the biggest names in the world as users of Python, it is a no-brainer that anyone who wishes to become a part of these companies would take a long and hard look at Python too.

 

With the right training and certification in Python, you will have numerous opportunities to grow and become an inseparable part of the tech world.

 

The importance of Python language in Data Science and Machine Learning should not be undermined. '

 

With the increasing hold of these two throughout the globe, the need for Python programmers has gone up too.

 

This is enough to imagine the kind of opportunities and prospects the future will hold for you.