What Makes Python Libraries So Important For Data Science Learning?

'Python equals data science.'- although it's nothing but a busting myth, still we can say python is near equals to data science.  But, why so? Even most advanced artificial intelligence and deep learning projects are based on R and other programming languages like C++, Java, etc. But do you know that mastering the python libraries can be the entrance ticket for you towards the most complex and lucrative sub-domain of data science and AI? Yes. While other non-technical aspirants can't even think of deep learning or complex machine learning scenarios, acquiring adequate knowledge about the python libraries, you can easily solve basic issues. So, let's explore the five key reasons that make learning python libraries for data science so significant.

1. Python is the most popular language for data science

The majority of a data science project is carried out with the help of python. In the case of SMEs, they favour remaining stick with python programming for their projects. Because 

● Python programs are easily available at affordable salaries.

● Due to the presence of huge libraries, manual coding is hardly required

● The language is easy to learn. Hence training an employee to further level of complexity in python data science projects is easier. 

2. Most of the libraries are open-source.

The majority of python libraries are open-sourced, giving you the freedom of 

● Sharing the associated tools and libraries within the workspace for free.

● You are free to modify the parts of libraries for algorithms and codes as per your requirements and without any obligations of copyright issue. 

‘Edit and optimise’ strategy works like a champ for data science new bees too. They can handle complex to moderate projects with an impressive degree of ease. 

3. Expanded compatibilities with several third-party tools

Adequate knowledge of python libraries helps you to work fluently with several third-party apps like 

● Microsoft Power BI

● IBM Cognos

● Tensor Flow

● Seaborn etc. 

While R and other programming languages have limitations regarding the compatibility of the third-party app, most pythons libraries offer you multi-dimensional opportunities for data analysis and visualisation, irrespective of the third-party apps under consideration. 

4. Generation of powerful business insights becomes time-saving

Concluding with powerful and highly data-driven business insights is a matter of time. The majority of time gets invested on 

● Data filtering

● Data cleaning

● Data verifications 

Next comes the complexity of drawing the maximum possible number of valuable insights. Using different python libraries such as Scikit-Learn, PyTorch, Pandas, etc., complications of data analysis can be solved within a minute. And the complexity associated with visualisation gets handled by other data visualisation libraries like Matploitlib, PyTorch, etc.

 

5. Take you 1-step ahead towards the promising possibilities of deep learning. 

Several advanced libraries can handle deep learning problems, such as image-emotion recognition, Natural language processing, artificial neural network designing, etc. For other programming languages, you need to go with manual coding and algorithm designing in the majority of such cases. But different python libraries offer precise solutions to such issues within a coding-free environment. Few of such advanced libraries capable of handling deep learning issues are

● Keras

● TensorFlow

● Seaborn, etc. 

Where to learn these demanding python libraries?

You can join Learnbay data science and AI certification courses, where you will learn each of the demanding python libraries and their specialised usage strategies as per your domain requirements. Besides, you'll get to know about the trick and tips that will help you become a knowledgeable but smart data science professional. 

To obtain the ongoing lucrative offers on course fees, book your telephonic career counselling session here. 



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