Data Science is applied to gather multiple data sets to collect information and project the insight and interpret it to make an effective business decision. However, being a data scientist requires you to learn some of the best and most highly used programming languages, such as Java, C++, R, Python, etc. Among these, Python has been considered the preferred choice among data scientists throughout the globe.
In this Python for Data Science Tutorial, we’ll explore exciting world of Python and its wide-ranging applications in data science. We will also explore a variety of data science techniques used in data science using the Python programming language.
Prerequisites: In order to make the most of this tutorial, it is recommended to have a basic understanding of Python programming language. .
Why Use Python For Data Science?
Python is in demand for the past few years and the recent survey also suggested the same, Python leads the chart among the top programming languages in both the TIOBE index & PYPL Index. However, to support this, there are 5 concrete reasons behind this,
1. Easy To Learn: Being an open-source platform, Python has a simple and intuitive syntax that is easy to learn and read. This makes it a great language for beginners to learn data science.
2. Cross-Platform: Being a developer, you don’t need to worry about the data types. The reason is, Python allows developers to run the code on Windows, Mac OS X, UNIX, and Linux.
3. Portable: Being an easy & beginner’s friendly programming language, Python is highly portable in nature which means that a developer can run their code on different machines without making any further changes.
4. Extensive Library: Python has several powerful libraries that make data analysis and visualization easy. Pandas is a library for data manipulation and analysis, NumPy is a library for numerical computation, and Matplotlib is a library for data visualization.
5. Community Support: Python has a large and active community that supports and contributes to the development of various libraries and tools for data science. This community has created many useful libraries, including Pandas, NumPy, matplotlib, and SciPy, which are widely used in data science.
However, there are a lot more reasons to opt for Python for Data Science such as OOP, expressive language, the ability to allocate memory dynamically, etc. and that’s the reason for using Python Programming Language for Data Science applications.
Benefit Of Using Python For Data Science
In summary, Python is a popular language for data science because it is easy to learn, has a large and active community, offers powerful libraries for data analysis and visualization, and has excellent machine-learning libraries.
In terms of application areas, Data scientists prefer Python for the following modules:
- Data Analysis
- Data Visualizations
- Machine Learning
- Deep Learning
- Image processing
- Computer Vision
- Natural Language Processing (NLP)
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In terms of application areas, Data scientists prefer Python for Data Analysis, Data visualizations, Machine Learning, Deep Learning, Image processing, computer vision, and natural language processing (NLP).
Engineers coming from academia and industry are saying deep learning frameworks the scientific packages available with Python have made Python incredibly productive and versatile. Python deep learning frameworks have undergone significant evolution, and they are quickly evolving.
Learning Curve of Python
- Getting Started with Python:
- Python Operators:
- Python Data Types:
- Conditional Statement in Python:
- Loops in Python:
- Data structure in Python
- Reading and writing files with Python:
- Functions in Python
- Create functions in Python
- In-built functions in Python
- Object-Oriented Programming (OOPs)
- Introduction of OOPs
- Python OOPs Concepts
- Python Classes and Objects
- Encapsulation in Python
- Class Instance Attributes in Python
- Python Class Members
- Class method vs Static method
- Python Inheritance
- Types of Inheritance Python
- Inheritance, examples of object, issubclass and super
- Polymorphism in Python
- Abstract Classes in Python
- Exception Handling
- Python Decorators
Python Libraries for Data Analysis
- Numpy Tutorial
- Pandas Tutorial
- Scipy Tutorial
- Statsmodel Tutorial
- SweetViz
Python Libraries for Data Visualization:
Python Libraries for Image Processing:
Machine Learning:
- Sckit-Learn Tutorial
- Deep Learning Tutorial:
- Tensorflow Tutorial
- PyTorch Tutorial
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FAQs on Python for Data Science Tutorial
1. What to do after learning Python?
After learning Python, you can opt for the following domains to start your carrier:
- Web Development
- Mentor/Teacher
- Full Stack Developer
- QA Engineer
In case, if you want to learn further, it is suggested to go for the following domains after learning Python:
- HTML, CSS, or JavaScript to brush up your web development skills
- Start with Machine Learning, Deep Learning, NLP, etc. to start your carrier as ML Engineer
2. How Python is used in Data Science?
Backed up by large community support, Python entails more than 137,000 libraries which means you can possibly do anything alone with Python and one must focus on grasping the popular tools & frameworks that can make their work easier. Talking of Data Science, libraries such as NumPy, TensorFlow, SciPy, Pandas, Keras, PyTorch, etc are among them for different purposes and makes data science work lot more easier.
3. Is Python sufficient for Data Science?
Python alone is not sufficient for Data Science for sure. However, it can help you to start your journey but as per market demand and growing technology, it is mandatory to have a hands-on practice that includes machine learning, statistics, data visualization, data analysis, web scraping, numeric computation, etc.
