Importance of Python For Data Science

Why Learn Python for Data Science?

importance of python in data science

Python is known as the best programming language for a career in Data Science. Python added as the programming language of choice for Data Science as:

  • It is the most popular language in the world and has a strong community of users.
  • Python is free and flexible.
  • It offers easy syntax that cuts the development time.
  • It provides machine learning libraries for scientific calculations.

In the Development of the Python, the ecosystem expects to increase in the field of DS, and so the employment opportunities are more 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 make one’s career to a new level if learned with Dedication.

Python

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 light 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.

Features of Python :

Simplicity: It is simple and makes you think more about the problem than the syntax.

Open Source: Without any problems python is free for everyone for its change.

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 read line by line, which means the management of memory.

Huge Libraries: Python has large library support, which helps obtain solutions to the problems easily.

Object Orientation: Python supports OOPs concepts. i.e., any real-world problem could use into code and it has security also like 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 variable information or type information.

Data Types: All data types hold by Python Data Types to explain about various services possible on the variables and storage.

Data types: Numeric, List, Strings, tuples, Sets, and Dictionary.

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 do 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 place inside loop blocks that need to perform recursively.

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 improve your skills and set 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 find data later. Gain knowledge by finishing solutions to programming challenges you face.

Step 3: Learn Python Data Science Libraries

Learn Python Data Science Libraries

Python is significantly important for Data Science as it offers many libraries for scientific computing or analysis, visualization, and more. Some of the best and

Following Are Important Python Libraries:

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 general data to perform various NumPy operations and functions.

Pandas: It is an important library of Python for Data Science. it has used for use and analysis. It is very common with ordered, tabular data, matrix data, and unordered time series.

Matplotlib: This is a powerful library in Python for visualizations. Python script is used by 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 mathematical graphics.

Scikit-Learn: It is one of the main attractions as it is a free library where we can perform machine learning using Python as it contains simple and practical tools for data analysis and opening purposes. Algorithms such as Logistic Regression, Time Series Algorithm can achieve using scikit-learn.

Functions: Code can divide into useful blocks called functions, and for saving time allowing them to make the code and reuse it.

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