Our world is revolving around data. Every image, text, video, or document in your device or your cloud is data. Even when you are browsing, your device is fetching data from a database. With the exponentially increasing amount of data, career options that are based on data science are at their zenith.
Data science with python course by KnowledgeHut adds up to its already increasing value. Out of all the programming languages, Python is considered the best to analyze and manipulate data.
Even if Python was not a compulsory subject in your graduation, it is being recognized as one of the most flexible and powerful languages. Websites like Reddit that are completely programmed on Python are proof of its immense potential.
In this article, we are going to discuss the benefits of using Python in data science. Even if you are already graduated, you can learn Python from scratch without any hassle. And if you are an advanced Python developer, it’s time to try your hands in the rising field of data science.
What is Data Science?
Data science is a systematic approach that uses the combination of scientific data, statistics, programming, analytics, AI, and behavioural study to extract the pattern that will help in making decisions that will benefit the organization.
Every organization emits an insane amount of data regularly. Data analysts manipulate the data and process it to make predictions. These predictions are further tested on scientifically designed tests. Then comes the data visualization.
The analysis is laid out using advanced data visualization tools, which makes it easier to grab the pattern and make your move. It takes a lot more than being a developer to become a full-fledged data scientist. You should know computer science as well as science at the back of your hand.
Why should you go for Python?
Here are some of the reasons to choose Python as a primary language for data science:
Python is Easy
When we say python is easy, we are not saying it from a coder’s perspective, we are saying it from a layman’s perspective.
Even if you have no background in coding, you can easily learn python. It is significantly easier than other prominent coding languages like C++ and Java. Whenever people hear about KnowledgeHut data scientist training, they imagine a large incomprehensible code.
However, coding languages like python are free from unnecessary tags. The large code that you imagined can be created within minutes using Python. These factors add up to its simplicity:
- It is a free open-source coding language.
- It is an advanced-level coding language, making it capable of handling complex problems.
- It can be interpreted with ease, adding up to its flexibility.
- It is a growing language with a huge and supportive community to guide you in your journey with python.
- It is fast to write as well as execute.
If you are good at C++ and you switch to Python, you will clearly understand the ease of the Python language in the programming world.
Python is Fast
Every organization that hires a data scientist, deals with a massive amount of data daily. On top of that, if you have a slow programming language, things can get incredibly slow. Large codes take more time to execute but when it comes to Python which consists of a few lines of code, it can be surprisingly fast. In addition, you also save time in coding. It’s a win-win situation as a coder as well as a data scientist.
Old versions of Python were criticized for their slow execution speed. However, the introduction of the Anaconda platform turned the tables. In the current scenario, it is a complete language covering all the aspects of a programming language for data science.
Powerful Libraries, and Frameworks
Python was already one of the easiest coding languages. Its growing popularity bestowed it with hundreds of libraries and frameworks.
Instead of writing the code manually, you can just refer to different libraries and decrease the length as well as the complexity of the solution. Inbuilt libraries like Pandas are specifically designed for data handling and analysis. Machine learning and big data are some other focus points of python libraries.
Some of the most common python libraries are:
- Pandas: It increases data control and makes data analysis and data handling significantly easier.
- NumPy: It performs numerical computing by providing high-level math functions.
- SciPy: It performs technical computing. It plays a major role in data optimization and modification.
Some of the libraries support the integration of Python with other programming languages. It increases the flexibility of Python, which in turn increases its relevance with data science.
Python is a growing programming language for web development. It is capable of reducing the amount of PHP code without compromising the functioning and layout. If you want to learn web development, go for Python.
It is an advanced, easy, and growing career option. There are a lot of python frameworks that will make it fun and interactive for you.
Full-stack frameworks for web development are:
Python also consists of micro frameworks like:
Python is powerful enough to surpass the current web development languages.
Python has a Huge Community
This might sound irrelevant but having a large supportive community can make a great difference. It is frustrating when you are stuck on a problem and you have no one to look up to. While working on Python, you will never face such issues.
There is a large community willing to help you all the time. Many community members are working on new libraries and frameworks to enhance the data science functionalities of Python. In addition to community forums, you will find plenty of facts, tutorials, and updates on various websites.
Advanced Visualization Tools
Better visualization leads to a better understanding of data. Despite using small codes, python can create stunning visuals. You can also integrate some big data visualization tools in Python.
Hadoop is one of the most popular big data platforms which is compatible with Python. Python package PyDoop can access the API for Hadoop. Python can solve complex problems with minimum coding using PyDoop.
In the last few years, there is a huge spike in career opportunities for data scientists. Python being the best programming language for data science can give you an edge over others.
Python is not limited to data science, it is independently a great career grosser. There are many additional jobs that you can land if data science is not your cup of tea:
- Python Educator
- Product Manager
- Data Journalist
- Python Developer
- Financial Advisor
Python is a rising domain. In the upcoming years, there will be significant growth in career opportunities for Python developers. Python along with data science will be the icing on the cake. Data science will increase the range of career opportunities that will come your way. Even today, IT companies are paying Python developers handsomely.
In this era, data is never-ending. Even if you remove all the data in the world, you will have an immense amount of data created in an instant. If you are capable of using that data for the betterment of your organization, you will never run out of job opportunities in the future. There is no better way than Python to kickstart your career.
About the Author!
Rajesh Jujare has extensive experience with Inbound marketing for various industries like eCommerce, Manufacturing, Real-estate, education, and advertising. Having worked with a reputed Jootoor digital marketing agency, he has a stronghold on Content curation, SME acquisition, and White hat link building techniques. Rajesh has hands-on experience in Influencer marketing and worked with International influencers