Python is a great choice for the Data Science business. Due to its various features and advantages, Python has stood as the most preferable programming for Data Science. Many popular companies like Instagram, Spotify, and Amazon use Python for Data Science. It has become a must-have tool for anyone who is working with Data Science. So, if you are considering a Data Science career, you must check this Python for Data Science course. And, if you have any confusion about learning Python for Data Science. We are here to guide you through why and how to learn Python for Data Science. Keep reading.
The following are the topics we are going to cover in this blog.
Why is Python Necessary for Data Science?
- Easy-to-Learn.
- Great Library System.
- Python Community.
- Scalable and Flexible.
- Multiple Visualization Options.
How to Learn Python for Data Science: Step by Step
- Fundamentals of Python
- Python Libraries
- Pandas
- NumPy
- SciPy
- Scikit-learn
- TensorFlow
- Data Science concepts
- Data Science projects
Why is Python Necessary for Data Science?
Python is very popular and it is the best programming language for Data Science. However, there are many reasons for choosing Python over others. We have listed a few below.
Easy-to-Learn. Python is one of the most popular and easy-to-learn programming languages. The syntax of Python is readable and easy to understand. As it is easy to read and understand, it takes very little time compared to any other Data Science programming language.
Great Library System. Python has a huge library collection which makes it more choosable by many Data Scientists and Data Analysts. There are many Data Science libraries available in Python such as Pandas, NumPy, SciPy, and Scikit-Learn.
Python Community. Python’s popularity is continuously growing in the Data Science community. An increasing number of participants are developing Data Science libraries. It is easily accessible to tutorials, code snippets, and error resolution strategies by any participants.
Scalable and Flexible. Another advantage of using the Python programming language in Data Science is that it is highly efficient, scalable, and flexible. Python is scalable and versatile enough to be used in a variety of disciplines and for a variety of reasons.
Multiple Visualization Options. Python offers various visualization options. These visualization options assist you in gaining a clear understanding of data by creating charts, graphs, Histograms, and many more.
How to Learn Python for Data Science: Step by Step
The following steps will help you learn Python for Data Science easily. Read Carefully!!
Fundamentals of Python
First, you need to concentrate on the fundamentals of Python. Basics like if, else…if and else statements along with for and while loops. You should also have a basic understanding of functions, classes, objects, and recursions.
You can join a Python community where you will have exposure to various applications of Python. Tutorials and solutions for various real-time problems. Because of which you can develop your skills in Python coding.
Python Libraries
Python Libraries are the crucial part you need to master to enter the Data Science field. As we have discussed earlier, there are many libraries in Python which assist professionals to work with Data Science. The top 5 Python libraries used in Data Science are mentioned below:
Pandas
Pandas is one of the most popular and commonly used Python libraries. It is mainly used for data analysis and data handling. Pandas provide quick, versatile data structures, such as data frame CDs, that allow you to load, modify and analyze any type of structured data in a very simple and natural manner. Data manipulation, Data cleansing, Dealing with missing data, Aligning, and indexing are the key features of Pandas.
Numpy
NumPy is also known as Numerical Python. It is a fast framework mainly used for storing and manipulating data buffers. NumPy arrays enable efficient and faster secure data functions as the arrays increase. It facilitates the application of statistical measures by speeding up mathematical operations. NumPy employs array-oriented calculations to improve speed, making it simple to deal with many classes.
SciPy
Scikit-learn is probably Python’s most essential Machine Learning tool. Scikit-learn is being used to create a Machine Learning algorithm after filtering and processing your data using Pandas or NumPy. It includes a plethora of tools for predictive analysis and design. SciPy supports a wide range of Machine Learning activities, including data optimization, data integration, data interpolation, and data manipulation.
Scikit-learn
Scikit-learn is one of the open-source Python libraries that is used for the collection of data, reading that data, processing it, regression, classification, unsupervised clustering, and many other tasks. It is implemented based on Machine Learning libraries like NumPy, SciPy, and Matplotlib. Scikit-learn is a comprehensive and excellent resource. It has a plethora of built-in algorithms that help in various Data Science projects.
TensorFlow
TensorFlow is a free end-to-end open source library of Python. It has a large range of Artificial Intelligence toolkits which involves various tools, libraries, and resources. It is a framework that allows developers to design and perform operations with tensors by using data flow graphs to construct large-scale neural networks. It is also used to construct models of advanced Machine Learning and Deep learning algorithms.
Data Science Concepts
After learning about the libraries of Python and how they are used. Then it’s time to learn various Data Science concepts with the help of Python programming. Try to interact with data using Python. For example, try to read and write the data files using Python programming. While reading or writing the data using Python libraries, you will understand how exactly they work. So try to practice as much as you can. Gain in-depth knowledge of various Data Science concepts with this Data Science Tutorial Always start with writing and reading the data. If you become confident in writing and reading the data using Python only then you start work on analyzing the data. Again, try using Python libraries while analyzing and obtaining meaningful insights from the data. Try using libraries like NumPy and Pandas because they are easy and also they offer various functions, methods, and Machine Learning techniques. To always keep updating or keep learning, try to build and train Machine Learning models using Python libraries on a regular basis.
Data Science Projects
Then finally, after gaining all the required knowledge about Data Science and how to use Data Science in Python. Then you should start working on projects. You can create your own project and build your own solution for it or you can work on public projects to improve your skills. Projects based on data cleaning, data visualization, and data analysis will be more helpful to brush up your skills and also to give you hands-on experience.
Also, if you are interested in Data Science and want to make a career out of it then projects are the crucial part to crack your interview.
This is all about Python for Data Science. I think you may have come to the conclusion why you should consider Python programming for Data Science and also how to learn Python for Data Science.
