Why Should You Learn Data Science Using Python?

It is a great choice to learn data science using Python. Though there are many tools with numerous options, the choice often comes down to two programming languages – Python and R. Python has uses apart from data science, but it is slowly becoming a popular choice as the programming language to use for data scientists.

Why choose Python Instead of R?

One main reason for choosing Python instead of R is because of the range of data science or data analytics libraries that are available. Some of these libraries are popular in the data science community like SciPy, Pandas, StatsModels, NumPy, and Scikit-Learn. There are almost 72,000 of these libraries in the Python Package Index or PyPI and this number keeps growing.

What is data science?

Data science, that is, data-driven science, is a complex field of scientific methods, processes, and systems. It extracts knowledge or insights from data in various formats, unstructured or structured. It can be compared to data mining. Data science then employs different techniques on the data to extract necessary insights from it.

Why choose Python over other data science tools?

Python is powerful when the code is written naturally. It has many other features that are attractive to data scientists. Being a data science tool, Python is useful for exploring the concepts of machine learning in the best way possible. Machine Learning deals mainly with probability, mathematical optimization, and statistics, and Python makes all of these easy. Let us explore some reasons why you should opt for Data Science using Python training.

Easy Learning

Python is a popular data analysis tool. It leaves other tools like SQL and SAS behind, and only R can match it with 35% of data analysts using Python.

Python is easy to learn and start coding. It has an easy syntax compared to other data science languages, like R and the learning curve is not too steep.


Python is more scalable as a programming language than R and is faster to use than Stata and Matlab. This is the reason that Youtube migrated to Python. Expert data scientists in different industries use Python to develop a variety of applications successfully.

Data Science Libraries

One of the main reasons for the success of Python is the availability of data science libraries for aspiring developers. These libraries are upgraded continuously.

Here are some of these libraries that you can refer to while you do a Data Science using Python course:

NumPy: This is important for performing scientific computations with Python. It includes an assortment of high-level mathematical functions that can be used on multi-dimensional arrays and matrices.

SciPy: This works together with NumPy arrays and has effective routines for numerical integration and up-gradation.

Pandas: This is also developed on top of NumPy. It delivers data structures and operations to modify numerical tables and time series.

Matplotlib: This is a 2D plotting library. It includes data visualizations in the form of histograms, bar charts, power spectra, and scatterplots with minimal coding lines.

Scikit-learn: Developed on NumPy, SciPy, and Matplotlib, this acts as a machine learning library that leads to regression, classification, and clustering algorithms that involve logistic regression, support vector machines naive Bayes, random forests, and gradient boosting.

A Large Community

The popularity of Python is because of its ecosystem. Today, there are innumerable volunteers developing Python libraries because Python has extended its reach to the data science community. This has enabled the development of advanced tools and processes in Python.

The development community helps people learning Python to look for solutions to their coding problems. There are also code mentors and stack flows available for finding answers to queries.

Multiple Graphics Options

Python offers many graphical options. Matplotlib is the base for developing libraries like Pandas Plotting, Seaborn, and ggplot. Developers can analyze data, develop charts, graphically plot and develop web-ready plots using data visualization packages.

Python is easy, innovative, and powerful due to its wide usage in many contexts, not just with data science, but in other areas as well. As a professional, it is key that you understand the tool and how to use its strengths for working in data science. Every data scientist must be versatile and if you want to master it, Python can be a big help. If you are new to Python, learn it using the online resources available and you can also enrol in a course for a more formal approach and expert guidance.

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