Why Data Scientists love Python?
Table of Contents
OverviewIf you are related to the IT sector Data Science is a well known word. This is a welcoming course amidst the students and professionals due to its immense necessity in the market. Hence, it is listed among the must learn technology for IT professionals. Even though there are numerous skills in the industry Data Science course has a special status in the market due to its wide applications. In addition, Python has become the preferred programming language by the Data Scientist.
Why Python?There are various programming languages prevailing in the market but Python has its own priority due to its features. Data Science training will make you perfect for the industry. There are numerous Data Science tools and one among them is Python that plays a crucial role. Our blog on how to become a Data Scientist will be beneficial for you to get the in-depth roadmap. Some of the features of Python that make it’s deployment wide:
- Python has an extensive standard library, which showcases its presence in areas namely software engineering, string processing, and internet protocols.
- It can be extended easily with the addition of new modules that are implemented over C or C++.
- Its high stability has stable releases, which is of the latest technology for every 18 months.
- Python is also known for its ease of usage with a simple syntax that is readable in nature. Thus, making coding easy for anyone to understand along with its maintenance.
- Developers are free from the task of memory allocation due to its automatic memory management.
How to learn PythonAs we came across the importance of Python to become a Data Scientist, now it's time to checklist the process in learning Python. There are various blogs written for Data Science beginners, which will aid in clearing your doubts. According to a report of 2018, about 66% of data scientists make use of Python that marks it as the best analytic tool. Data Science online course offers students flexibility in learning. Python Fundamentals For any course getting a thorough knowledge of the course plays a vital role in establishing a firm hold in the industry. Invest time for learning Python programming basics. Hence, learn one-at-a-time and cover every topic with complete involvement. Mini Python projects Every course consists of two-way learning theoretical and practical. Theory can be covered in Data Science offline classes but practical sessions have to be covered by trying out mini-projects. This implements your acquired knowledge into real-time practice. You can do that through reading blogs, other’s code along with the command line interface. Python’s Data Science libraries Make use of the in-built database for your practice for better results. Mark your presence among the 40% of Data Scientist who make use of SQL in their work. Some of the well-known libraries included in this are NumPy, Matplotlib, and Pandas. Data Science course makes the process simple. Data Science Portfolio At times we learn a few courses just because it's used widely across the globe, make sure you understand the required statistical process behind acquiring knowledge of something. You can also deploy GIT for the version control of your project. Make sure you collect various databases and share the portfolio with recruiters along with some unique ideas Advanced Data Science techniques Learning never has any limit as we are under dynamic evolution with respect to the technology. As a Data Scientist, it is indeed necessary for you to be updated with the market trends. When compared with other courses Data Science courses are a bit vast. So, learn the part which excites you the most to maintain an enthusiasm till the completion of the course. It’s totally up to you whether you begin with machine learning, data visualization or artificial intelligence. Usage of Python libraries There are over 250 programming languages present in the IT sector and still Python is preferred by the Data Scientist due to its characteristics. Check our Python online course for your bright future. Python’s greatest power is the presence of libraries that are used for the analysis and visualization of data for futuristic usage. Some of the famous Python libraries are mentioned below:
- NumPy- This stands for Numerical Python, deployed for the scientific computing that consists of n-dimensional array objects. This can be used as a container with multi-dimension in order to perform various NumPy operations.
- Seaborn- This tool is a statistical plotting library present in Python, which is used for the default styles along with a high level interface for drawing statistical graphics.
- Matplotlib- It is considered for visualization in Python and widely used for Python scripts, web application server, GUI toolkits , and shell. This can be used for various types of plotting along with its working.
- Scikit-learn- The user is capable of implementing machine learning with the help of this in Python that is a free library consisting of simple tools for the mining process and data analysis. It is possible to implement different algorithm like time series, logistic regression with the assistance of Scikit-learn tool.
- Pandas- Another vital library used for both manipulation and analysis that are suitable for various data namely ordered time series, tabular, matrix data, unordered time series and many more.