Introduction to Python and its relation with data science
Did you know that 87% of developers and data science professionals use Python? Due to its versatile applications, Python is a high-level programming language that is known for its readability and simplicity. Both freshers and experienced professionals can benefit from using Python as it offers a wide range of functionalities that cover various applications in data science. It is even used in web development and artificial intelligence applications. Python can be easily integrated with machine learning applications, large language models, and other generative AI-powered systems. It provides a plethora of possibilities in developing different data science programs that help in achieving personalized results that are tailored to every organization’s unique needs.
Writing a Python Program
Start by downloading the latest version of Python from the official Python website and installing it. Once done, use an integrated development environment (IDE) or a simple text editor to write your code. Coding has now become a lot easier. Gone are the days when you need to memorize those long lines of code overnight and type them incessantly. Now, most IDEs are integrated with auto prompters and gen AI systems, which help you build syntax easily. You can now build a program with just a few prompts that are relevant to your requirements. However, this doesn’t mean that you should shy away from learning any code. Understanding the basics of Python and learning code is essential, as it will help you debug and correct errors in syntax and implementations more easily. After all, an AI is bound to make mistakes and needs a human touch. For instance, an AI-powered system can generate a Python program only based on your prompts and could leave out certain fundamental functions that are essential. That’s when you can help fine-tune the program to achieve the desired output.
Variables in Python
Variables are essential in Python programs. For instance, a variable in Python is represented in a way that serves as a pointer to an object. If the object is assigned to a variable, it can be referred to by that name. As an aspiring data scientist, you need to understand the rules for naming variables before writing the code. The rules are as follows.
- ● It must begin with either an underscore (_) or a letter (a-z, A-Z). cannot begin with a digit.
- ● It can only have underscores and alphanumeric characters.
- ● Name, Name, and NAME are distinct variables; this is case-sensitive.
- ● It is not possible to name a variable in Python using the reserved terms (keywords).
Data Types in Python
Data items are often classified and categorized as data types, which represent the value and the operations that could be performed on a particular type of data. Since everything is an object in Python programming, data types are classes and variables of these classes. There are numeric, sequence, and list data types.
Input and Output in Python
Python provides simple input and output functions that are quite useful and help you to better enhance your requirements in a seamless manner. The input() function returns data as a string, regardless of what the user enters. If you want to store the input as another data type, you need to explicitly convert it.
Operators in Python
Generally speaking, operators are used in Python programming to manipulate variables and values. These are common symbols that are employed in arithmetic and logic operations. You can carry out basic mathematical operations, including addition, subtraction, multiplication, and division, using Python arithmetic operators. Arithmetic operator types include +, -, *, /, //, %, and **. Do not forget to precede the arithmetic operators with the following:
- ● P - Parentheses
- ● E - Exponentiation
- ● M - Multiplication and division with the same precedence
- ● A - Addition and subtraction have the same precedence
Uses of Python in Data Science
Python is the most popular programming language according to the TIOBE and PYPL indices, and it has been in demand for a few years, according to a recent survey. However, there are five specific justifications for this.
- ● Python is an open-source platform with an easy-to-understand syntax that makes it straightforward to read and learn. Because of this, it's an excellent language for novices learning data science.
- ● You don't have to worry about data types if you are a developer, as Python is cross-platform compatible. The rationale is that Python enables programmers to run their programs on Linux, UNIX, Mac OS X, and Windows.
- ● Because Python is a simple and beginner-friendly programming language, developers can run their code on multiple computers without making any further changes.
- ● Python comes with a number of strong libraries that facilitate data visualization and analysis. Matplotlib is a data visualization package, NumPy is a numerical computation library, and Pandas is a data manipulation and analysis library.
- ● The creation of several data science libraries and tools is encouraged and supported by Python's sizable and vibrant community. Numerous helpful libraries, such as Pandas, NumPy, matplotlib, and SciPy, which are extensively utilized in data research, were developed by this group.
Benefits of Using Python in Data Science
Python is simple to learn and comprehend. It is ideal for programmers of all skill levels because of its flat learning curve and emphasis on readability and simplicity. Python tasks need fewer lines of code, which means you spend less time tinkering with code and more time developing. It is a great option for novice programmers who want to learn data science because of its ease of use.
It's open source. When it comes to cost, "free" is unbeatable. Both Python and the libraries that support it are freely available for use and distribution. Scalability and adaptability are key components of Python. Python allows one person or 5,000 people to collaborate on the same project. Due to its versatility, the language can be used for a wide range of tasks. It integrates with the majority of important libraries and API-powered services and runs on nearly every platform and operating system. Python is useful for data analysis activities and functionalities that need to be integrated into larger, more complicated projects or with web apps and cloud computing platforms. Furthermore, Hadoop, the most important and well-liked open-source big data platform, is compatible with Python.
Python performs exceptionally well in the domains of algorithms and machine learning. With so many machine learning packages available, Python is a perfect tool for machine learning tasks. TensorFlow, a machine learning toolkit from Google, was created with Python and libraries such as PyBrain and scikit-learn to create algorithms for regression, classification, and clustering. Additionally, Python's inherent simplicity allows machine learning engineers to "do the math," which is essential when putting algorithms into practice.
You can learn Python for data science from Eduinx, a leading edtech institute with non-academic mentors with over a decade of industry relevant experience. Eduinx offers a data science and generative AI course that will cover all the complex concepts of data science through a practical, hands-on approach. Our mentors will guide you through every step of your journey and help you land a job with a decent salary package. Learn more about our courses here.
