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6 Reasons Why Python Is Used For Data Science

Today, almost any job description for a data-related position requires Python. Why is that? Is it really that important for data science? In this article, I explore the reasons behind Python’s domination in the data science world.

Python and Data Science

There is a lot of buzz around data science and data science careers. As organizations recognize the value a data-driven approach can bring them, the demand for data scientists continues to grow. As a result, many people from different professions explore opportunities to build their careers in data.

Naturally, there are a lot of questions about this career move. Do you need a master’s degree to become a data scientist? What kind of software do you need to learn? Is it possible to become a data scientist without an IT background? Do you need to learn Python?

In this article, I want to focus on the importance of Python for a successful career in data science. The relationship between Python and data science is a two-way street. Data science has had a key role in Python’s booming popularity, and Python has helped newcomers understand and master data science.

Data science is about extracting actionable insights from data, and Python is arguably the most effective tool to accomplish this goal. Check out this article to learn what data scientists use Python for. And here, I want to elaborate on WHY they choose Python.

6 Reasons to Learn Python for Data Science

Data scientists choose Python for a reason. This programming language is dominant in data science and required in almost any job posting related to data analytics and modeling. Here is why Python has taken over the data science world.

1. Python is beginner-friendly.

Data scientists should be tech-savvy but not necessarily programmers. People from academia, marketing, HR, and finance commonly move into data science and acquire new skills in the middle of their careers. Tools that are easier to master are more likely to win in data science.

Python, with its ease of use and simple syntax, is a perfect solution for people who have no IT experience. It is very accessible to professionals of different backgrounds. Just a couple of weeks may be enough to learn how to process data and build simple models in Python.

Not sure where to start? Here’s an interactive course that gently introduces you to Python for data science even if you have no IT background and have had zero exposure to programming languages.

2. Python has a toolset to deal with mathematics and statistics.

Python has great functionality to process mathematical calculations, get descriptive statistics, and build statistical models.

The basic mathematical calculations can be performed with built-in mathematical operators, such as addition (+), subtraction (-), division (/), and multiplication (*). For higher-level mathematical operations, such as exponential, logarithmic, trigonometric, and power functions, you can use the math module. This module allows performing complex mathematical operations with just a few lines of code. For example, with Python’s math module, you can easily calculate combinations and permutations using factorials, apply trigonometric and hyperbolic functions, and simulate periodic functions.

Python has several libraries (statistics, NumPy, SciPy, and Pandas) that provide direct access to a rich selection of statistical tools. You can easily get detailed descriptive statistics such as mean, median, mode, weighted mean, variance, correlation, outliers, etc. There are libraries (e.g., scikit learn) to deal with linear regressions, logistic regressions, and many other statistical models. You can explore causal relations and carry out hypothesis testing – all with open-source Python libraries.

3. Python is great for visualizing data.

Many data insights come from data visualization. After mastering Python for data science, you’ll be able to draw useful and professional-looking visualizations to explore your data, understand possible correlations, spot outliers, non-obvious relationships, trends, etc.

matplotlib is the basic data visualization library in Python. It provides a wide range of opportunities in terms of available plots and their flexibility. However, it can be time-consuming to build anything complex with this library. Luckily, many other data visualization tools are built on matplotlib but are much more user-friendly. If you want to build advanced plots with Python, check out seaborn, Plotly, and Bokeh libraries.

4. There is a huge ecosystem of Python libraries for data science.

Python offers a rich selection of open-source libraries with functionalities that go far beyond mathematics, statistics, and data visualization. There are different modules to import data from a variety of sources (CSV files, Excel, etc.). Then, there are packages for processing and structuring data from different formats (e.g., Scrapy and Beautiful Soup to extract structured data from websites and NLTK to process unstructured text data).

Finally, there are PyTorch and TensorFlow frameworks, developed by Facebook and Google, respectively. They are widely used in academia and industry to build complex deep learning models for facial recognition, object detection, language generation, etc.

5. Python is efficient and scalable.

Python is perfect for data science applications in terms of its efficiency and scalability. You can work with databases that have a few hundred records or a few million records – Python is a good solution in any case.

Furthermore, models developed with Python are easy to deploy in production. As you probably already know, the process to deploy data science models in production is usually iterative, with a model developed, validated, then deployed, tested for production, evaluated, and updated. With Python, you can handle this iterative process effectively and smoothly.

6. Python has a strong community.

Finally, Python has a great community. This community works continuously on developing and improving Python libraries for data science while enriching this open-source ecosystem.

If you are a beginner, you can always get support from the community. If you cannot find answers to your questions online, there are many forums where you can ask questions, get recommendations, and find possible solutions from more advanced Python users. A strong and supportive community is one of the key reasons for Python’s success in the data science world.

Read more on the advantages of using Python in data science in this article.

It’s Time to Learn Python for Data Science!

Python is an effective and must-know tool in data science today. You know now there are good reasons for this:

  • Python is easy to learn.
  • There are many open-source Python libraries for mathematics, statistics, data visualization, and data modeling.
  • Leading tech companies are using Python for their advanced applications, including face recognition, object detection, natural language processing, and content generation.
  • Python programming language is efficient, scalable, and production-ready.
  • Python has a strong and supportive community.

So, let’s get on board!

I recommend starting with the Introduction to Python for Data Science course. It includes 141 interactive exercises that cover basic data visualization and data analyses, simple calculations, working with missing values, creating variables, filtering data, etc.

If you want to go beyond the basics, make sure to check this Python for Data Science learning track. It includes four interactive courses covering the foundations needed to start working in the field of data science. In addition to the topics covered in the introductory course, you learn how to work with strings in Python and how to process data coming from CSV, Excel, and JSON files.

Bonus. Here are some ideas for your next data science project in Python.

Thanks for reading, and happy learning!