Why Learn Python for Data Science?
Python is considered best suited and as a valuable skill needed for a career in Data Science.
Python has been shortlisted as the programming language of choice for Data Science as:
- It is the most popular language in the world and has a passionate community of users.
- It is free and flexible.
- It offers easy syntax that cuts the development time.
- It provides machine learning libraries for scientific computations.
Development in the Python ecosystem is expected to increase in the field of DS, and so the employment opportunities are abundant as well. So the future is pretty bright for those who study Python for Data Science. Although steps to learn Python for Data Science are simple, still, it requires hard work to put in. Python offers the potential to bring one’s career to a new level if learned dedicatedly.
Developed in 1989 by Guido Van Rossum, it is a general-purpose language that is high level, easy to learn, and dynamically initialized. With the rise of machine learning and artificial intelligence, Python has come into the spotlight because it makes the work more productive and much easier. It is the fastest-growing language in terms of developers, libraries, and applications that can be used.
Why is Python Popular?
Features Of Python
- Simplicity: It is simple and makes you think more about the problem than the syntax.
- Open Source: It is free for anyone to use, and modifications to Python are also allowed without any problems.
- Portability: Python supports portability, which means writing code and sharing it with anyone.
- Embeddable and Extensible: Python supports adding code of other languages into itself to run those functions making Python more powerful.
- Interpretation: Python is interpreted line by line, which means the management of memory.
- Huge Libraries: Python has huge library support, which helps obtain solutions to problems easily.
- Object Orientation: Python supports OOPs concepts. i.e., any real-world problem can be modeled into code and have security to it, such as access is restricted.
Steps To Learn for Data Science
Step 1: Fundamentals of Python
Getting familiar with data science involves learning Python programming basics. Let us see a few of the basics, to begin with, Python.
Basics of Python for Data Science
- Variables: Variables refer to the location in the memory to store data values. But Python does not require a variable declaration or type declaration.
- Data Types: Various data types are supported by Python, which defines various operations possible on the variables and storage.
- Operators: The value of the operands can be manipulated with the help of operands. Some of the operators include Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, and Identity.
- Conditional Statements: Conditional statements execute a set of statements inside the block if the given condition is true. If, Elif and Else are the conditional statements in Python.
- Loops: Code statements that need to be executed recursively can be placed inside loop blocks.
- Functions: Code can be divided into useful blocks called functions, allowing them to organize the code and reuse it, thus saving time.
Apart from learning basics in Python, you can also start by joining online tutorials and communities. This way, you will be surrounded by like-minded people and would help you learn more.
You can also start by reading some books and blog posts.
You can also start your search for the Best Python books. Of course, some students prefer video courses with project-based learning. That's why our editor Dr. Johns created a full course on Python fundamentals. It covers everything you need to know to get started, including what we're about to discuss in step two.
Step 2: Practice Mini Python Projects
Practical implementation is a plus while you learn Python, so try your hands on some Python projects and learn as you go. Try programming and building projects like calculators for an online game, or a program fetching weather forecast in your city projects like these would enhance your skills and solidify your basics.
After you are well versed in basic projects, next, you must build your experience with APIs and begin web scraping that would also help to gather data later.
Gain knowledge by finishing solutions to programming challenges you encounter.
Step 3: Learn Python Data Science Libraries
Python is significantly important for Data Science as it offers numerous libraries for scientific computing or analysis, visualization, and more. Some of the best and most important Python libraries are listed below:
- NumPy: NumPy, which stands for “Numerical Python,” is a core library of Python for Data Science. It is used for scientific computing and as a multidimensional container for generic data to perform various NumPy operations and functions.
- Pandas: It is an important library of Python for Data Science used for manipulation and analysis. It is much compatible with tabular data, matrix data, ordered and unordered time series.
- Matplotlib: This is a powerful library in Python for visualizations. It can be used in Python scripts, web application servers, and other GUI toolkits. Different types of plot and multiple plots working can be used in Matplotlib.
- Seaborn: It is a statistical plotting library in Python. It offers beautiful default styles and a high-level interface to draw statistical graphics.
- Scikit-Learn: It is one of the main attractions as it is a free library where we can implement machine learning using Python as it contains simple and efficient tools for data analysis and mining purposes. Algorithms such as Logistic Regression, Time Series Algorithm can be implemented using scikit-learn.
Step 4: Build a Data Science Portfolio as You Learn
If you are an aspiring Data Scientist, then you must build your portfolio. Your mentioned projects should consist of several datasets, and you should share interesting insights that you have gleaned. The theme of your project must be something that interests you that you enjoy working on it.
On showing project portfolios, other Data Scientists get something to collaborate on showing future employers that you have the required programming skills.
Step 5: Apply Advanced Data Science Techniques
While learning Python for Data Science, your journey would be full of constant learning. We recommend getting a solid understanding. It gives you a mindset to provide valuable insights rather than just executing the code. You can also opt for advanced courses to ensure that you are updated. You can program projects using the live data feeds. You could start getting comfortable with techniques like regression, classification, and k-Means clustering models.
Resources and forums like Quora, Stack Overflow are full of people excited to learn more and also share their knowledge to help you learn to program.
Data Science spans numerous industries and is an evergrowing field. At this point, where the demand is increasing exponentially, there are vast opportunities to learn. It is best to continue learning by reading, collaborating, and conversing with others to compete with other fellow data scientists over time.
Do you have any other steps to share in this roadmap for Python for Data Science? Share Below!
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