Python continues to be a cornerstone in the data science landscape. JetBrains’s 2024 State of Data Science report shows that 48% of Python developers are involved in data exploration and processing, 44% use it for data analysis, and 34% for machine learning tasks. This widespread adoption underscores Python’s versatility and critical role in various data science activities. Whether you’re just starting or looking to sharpen your skills, mastering Python fundamentals is essential to thrive in this field.
This blog will discuss the most critical fundamentals you need to master Python for Data Science.
Also Read: Top Companies Hiring Data Scientists in Singapore
Python for Data Science: Fundamentals Developers Need to Know For Success
Python is one of the world’s most popular programming languages, especially in data science. Here are the most critical Python programming basics developers need to know:
Data Types
Python has several built-in data types, such as floats, strings, and integers. Developers need to know how these types differ and when to use them, which is crucial to learning Data Science with Python.
Operators
Python has various special operating symbols that assist developers in performing specific operations on one or several commands. The most prominent among these operators are addition (+), multiplication (*), and subtraction (-).
Variables
In Python, variables help developers store values in programs. They can also create such variables by assigning them a value using an equal sign (=).
Lists
Lists are ordered collections of items that are handy for storing data that data professionals can access in a specific order only. Developers can also use these lists to store multiple items in the same data type.
Dictionaries
You can define a dictionary in Python as a collection of key-value pairs. They help store data that data professionals can access only with unique keys.
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More Python for Data Science Fundamentals That Developers Need to Know
Beyond data handling and visualisation, there are core Python concepts every data science developer must master to write efficient and scalable code:
Functions
Functions are reusable blocks of code designed to perform specific tasks. Defining and calling functions is crucial for writing clean, modular Python code.
Control Structures
These determine the flow of a program. Familiarity with if statements, for loops, and while loops is essential for handling logic and decision-making in data workflows.
Modules and Packages
Modules are individual Python files, while packages are collections of modules. It is vital to know how to import and use them for building scalable, organized, and efficient data science projects.
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Most In-Demand Python Skills for Developers
It’s not enough to just understand the language to succeed with Python in data science. You must also be familiar with its frameworks, tools, and complementary skills that support real-world data science work.
Skill | Brief Description |
| Preparation and manipulation of data so that data scientists can use it for modeling and analysis. |
| Data exploration, pattern identification, comprehension, and effective communication of findings to diverse audiences. |
| Data storage and retrieval are based on data scientists’ needs and the data’s nature. |
| Open-source Python library that enables the exploration, cleaning, and processing of tabular data. |
| A Python library that helps data scientists work with large-dimensional arrays by using mathematical functions. |
Best Practices for Developing Strong Python Fundamentals for Data Science
If you want to make it big in data science, you will need strong SQL and Python fundamental skills. To do so, you must follow the best practices while working on data science projects using Python.
Practice | Benefit |
| Isolation of project dependencies and prevention of conflict between projects. |
| Making the code more maintainable. |
| Modeling the data and using its inherent data validation capabilities, too. |
| Identifying performance bottlenecks and optimizing applications for performance. |
| Helping you perform everyday operations on vectors with greater efficiency. |
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- Master of Science in Data Science, Liverpool John Moores University
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FAQs on Python for Data Science
Q: What Python skills are essential for data science?
Ans: The most essential Python skills for developers are:
- Python fundamentals
- Data analysis and manipulation
- Data visualisation
- Data retrieval and storage
- Pandas
- NumPy
- Machine Learning and Artificial Intelligence
- Deep learning
- Web frameworks
Q: Which Python libraries should every data science developer know?
Ans: When it comes to Python for developers, there are several essential libraries that every developer should be familiar with:
- Pandas
- Matplotlib
- Seaborn
- Plotly
- Requests
- Beautiful Soup
- Scikit-Learn
- Statsmodels
- XGBoost
- FastAPI
Q: Is Python enough to get a job in data science?
Ans: Knowing Python fundamentals and more about data science is essential, but this is not the only skill you need in this context. As a data scientist, recruiters expect you to possess programming skills, domain expertise, and statistical knowledge.
Q: What’s the difference between Python for data science and general Python programming?
Ans: The difference between Python for data science and general Python programming lies in the modules and libraries used for the work rather than the language itself.
Q: How much math do I need and Python for data science?
Ans: If you wish to be successful in data science by using Python, you will need a firm grasp of the basics of mathematics. The most critical components in this case are linear algebra, statistics, and calculus.
