'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 visualization 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
● 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 specialized 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.
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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In this Data Science With Python Training video, you will learn everything about data science and python from basic to advance level. This python data science course video will help you learn various python concepts, AI, and lots of projects, hands-on demo, and lastly top trending data science and python interview questions. This is a must-watch video for everyone who wishes o learn data science and python to make a career in it.
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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
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Python is the most widespread and popular programming language in data science, software development, and related fields. The simplicity of codes in Python, which helps learners avoid any confusion, is the key to this popularity. Python has constantly been developing, and it keeps getting updated for more ease in using. With 137,000 plus libraries and tools, Python has always provided its users with the solutions to problems of any complexity level. This reason makes Python the ideal language for Data Science operations. This article focuses on some of the essential and must-learn libraries in Python used heavily by Data Scientists. I have tried to cover different libraries used in various stages of a data science cycle, such as Data Mining, processing and modeling, Data Visualization.
Learn Data Science in Python from here!
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A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting.
I’m aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that’s ok, the important thing is to learn and enjoy it.
So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.
As you will see, my favorite way to learn is going from simple to complex gradually. This means starting with practical examples and then move to more abstract concepts.
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