The Top 10 Python-Based Data Science Skills

The Top 10 Python-Based Data Science Skills

As one of the most popular data science programming languages, Python is an incredibly helpful tool with a variety of applications in the field. To succeed in this field, devs have to understand not only Python as a language itself, but also its frameworks, tools, and other skills associated with the field.

1. Python fundamentals

A data scientist’s main work is to use data to extract actionable insights that help with various factors in a business, study, and so on. This process requires quite a bit of Python programming skills for each step. As such, data scientists must have a solid understanding of Python programming fundamentals to write the most efficient code for their job and to understand the codebases of other developers or teammates.

A few of the basic Python programming fundamentals that data scientists must master include:

Data types. Python offers many built-in data types, including floats, integers, and strings. Devs must know the difference between each and when to use them.

 Operators. Python features special operating symbols that help devs perform specific operations on one or more commands. These operators include addition (+), subtraction (-), and multiplication (*).

  Variables. In Python, variables allow developers to store values in a program. They also create variables by assigning them a value using the equal sign (=).

  Lists. Lists are ordered collections of items, and they’re useful for storing data that requires accessing in a particular order. Or, devs use lists for storing multiple items of the same data type.

  Dictionaries. A dictionary in Python is a collection of key-value pairs. They’re useful in storing data that requires accessing with a unique key.

  Functions. A function is a code block that performs a specific task and isn’t reusable multiple times in one program. Defining and calling functions is a vital part of Python development.

  Control structures. These are code blocks that determine the execution of other code blocks. Common examples of control structures include if statements, for loops, and while loops.

  Modules and packages. A module is a file containing Python code, and a package is a collection of modules. Devs have to know how to import and use modules and packages, especially when creating larger and more complex Python programs.

2. Data manipulation and analysis

Data scientists spend a significant amount of time preparing and manipulating data to ensure it’s ready for analysis and modeling. Thus, it’s essential for them to possess the ability to work with Python to clean and prepare data, including different data types and sizes.

Proficiency in using Python for efficient analysis of datasets of varying types and sizes is crucial for a data scientist. Additionally, data scientists must know how to use PySpark for large dataset manipulation and employ libraries for different data types such as images, text, and audio when necessary.

3. Data visualization

Data visualization is an essential component of data science that helps facilitate exploration, comprehension, pattern identification, and effective communication of findings to diverse audiences. Data scientists need to have hands-on skills and a robust understanding of data visualization tools to use them effectively. Among the numerous libraries and tools available in Python for data visualization, Matplotlib is a widely used library for creating static, animated, and interactive visualizations with an intuitive interface for generating statistical graphics. Seaborn, built on top of Matplotlib, provides a more polished interface for creating statistical graphics. Devs have many other options as well, including Plotly, Bokeh, Altair, and Vega.

4. Data storage and retrieval

Efficient data storage and retrieval skills are essential for data scientists who work with large amounts of data. Data scientists must know the various approaches for storing and retrieving data, depending on the nature of the data and their needs.

In Python, there are multiple ways to store and retrieve data. Common approaches include flat files, CSV files, JSON files, relational databases, NoSQL databases, and cloud storage services. Relational databases are powerful systems that store structured data and can be queried using SQL. Cloud storage services such as Amazon S3, Google Cloud Storage, and Microsoft Azure Storage provide scalable options for storing large amounts of data in the cloud. Python provides libraries such as boto3 and google-cloud-storage for accessing these services.

5. pandas

The pandas package is a crucial tool for data scientists and analysts working in Python. It is an open-source Python library that enables the handling of tabular data by exploring, cleaning, and processing it. Pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive. pandas is one of the essential libraries for any data science workflow, allowing for data processing, wrangling, and munging.

6. NumPy

NumPy is a Python library that enables the handling of large-dimension arrays through mathematical functions. It offers a variety of methods for array manipulation, metrics, and linear algebra. NumPy stands for Numerical Python and allows for the vectorization of mathematical operations on NumPy arrays, enhancing performance and speeding up execution. The library makes working with large multidimensional arrays and matrices effortless, allowing for efficient data analysis and manipulation.

7. Artificial intelligence and machine learning

Data scientists of any kind require a good grasp of artificial intelligence and machine learning. Algorithms in machine learning aim to create systems capable of learning from data patterns automatically. Mastery of Python is absolutely vital in working with machine learning algorithms effectively as it’s the language of choice for data science. Check out the guide on how to learn AI for more details.

8. Deep learning

Deep learning is a crucial component of data science that involves using artificial neural networks to extract higher-level features from data through multiple layers of processing. Python plays a vital role in this field, as it offers a wide range of powerful libraries and tools, such as TensorFlow and PyTorch that allow developers to build and train deep learning models effectively.

9. Web frameworks

Developers looking to successfully create and deploy web apps while taking advantage of their Python know-how must have a solid understanding of web frameworks. The most popular frameworks used by Python developers are Flask and Django. Django is a high-level web framework that prioritizes clean, rapid, and pragmatic design while offering many libraries to assist with the creation of high-quality web apps without building everything from scratch.Flask is the opposite of Django in that it’s a micro-framework that doesn’t rely on any particular tools or libraries. It doesn’t include a database extraction layer, form validation, or any other common functions provided by third-party libraries. However, it’s considered a template engine with its own modules and libraries. This allows developers to create web apps without needing to write low-level code. Both of these frameworks are highly versatile and allow developers to create useful web apps with Python. By leveraging the tools and libraries within these frameworks, devs focus on writing high-quality code without getting bogged down in lower-level details.

10. Front-end technologies

To successfully develop web apps to help with data science endeavors, Python developers must have a solid understanding of front-end technologies. This requires three primary front-end markup languages: CSS, JavaScript, and HTML. Python can generate all three markup languages through compilers, parsers, and transpilers. Python devs must hone their skills in these front-end technologies in order to fully utilize their Python knowledge.HTML helps dev build the basic structure of a web page, CSS helps style layouts and content, and JavaScript adds interactivity and dynamic behavior to web pages. By developing skills in all three, Python devs ensure that their apps and data science projects are not only functional but also visually appealing.