Python Skills for Data Scientists, Learn Python Skills in Data Science, Data Science course for Python

5 Python Skills to Become a Data Science Expert!

By Manas Kochar Category Data Science Reading time 7-5 mins Published on Jul 08, 2023

Gain These Python Skills to Become a Data Science Expert!

Today's industries are more focused on data-driven approaches and using data science to go beyond their capabilities, Data scientists have boosted the business's growth through their ability to find valuable data among piles of large unstructured data sets. To do their tasks, the most critical skill many data scientists opt for is Python. The data science online course with certificate helps in learning these programming skills and getting certified.

Python is a basic programming language that most data scientists employ for their activities. They can do various functions using Python, such as cleaning, importing, visualizing, and manipulating. Python is also a simple and easy-to-understand language, making it an integral part of any data scientist's portfolio.

Python skills will be helpful for a long time in data science. Those who desire to learn data science should also start learning Python.

Here are the five most essential Python skills you should learn.

1. Programming Knowledge

Data science requires knowledge of the basics of programming. Data scientists use data to get useful insights. However, they use Python to create codes that simplify the lengthy process. Python is an easy language to learn. Even though basic knowledge is enough, understanding advanced topics will be beneficial.

You may get data science online training to learn the basic concepts of Python. Make sure to hone your Python skills in the following topics:

  • Object-oriented programming (OOP)

The OOPs concept in Python is based on classes and objects. OOPs, in Python, aims to join functions and data, so it is not accessible by other parts of the code. In Python, classes, objects, inheritance, data abstraction, and other concepts are essential.

  • Data types and variables

Python uses many built-in data types These can be strings, floats, or integers. Data types represent the kinds of value present in the data. Python describes data types as classes and variables as objects (instances). A developer should be familiar with various data types and understand their practical uses.

  • Data structures

Data structure plays a vital role in managing the data. They help store vast amounts of data, create relationships between them, and execute actions on them. Python entails inbuilt data structures to maintain and access data. List, Set, Tuple, and Dictionary are some examples of Python data structures.

  • File handling

It is a significant aspect of programming that helps build, open, and close files in Python. Python offers built-in methods for file handling.

  • Generators

Generators in Python help in creating vast sets of values. They are helpful when we do not need to store these values in memory together.

  • Exception handling

You may use exception handling to differentiate the errors in code from regular code. Python has an object called an exception to indicate an error. It simplifies and makes the code easily understandable.

  • Iterators

Iterators are Python objects that help loop over data groups such as sets, tuples, lists, and dictionaries.

2. Data Visualization

Data scientists need to convey the results simply to others in the company. They use data visualization to help the non-technical staff easily understand the findings. It helps graphically present the results. The tools provide charts, maps, and graphs to help visualize data. Learn these data visualization tools along with their applications from a data science online course with certificate.

These tools make it easy to grasp the data's patterns, outliers, and trends. They can also analyze large data sets and create decisions based on data. Libraries in Python have various features to help users create interactive, personalized, and stylistic plots.

Individuals must have skills in visualizing data effectively. Many tools and libraries are present in Python that you can use. Some of these are listed below:

  • Numpy

It is a useful library in Python for managing large-dimension arrays. Many functions or methods are present in linear algebra, Metrics, and Arrays. This library is mostly helpful in speeding up the process in Python. Its vectorization features help boost the performance and execution of codes.

  • Pandas

Pandas is an essential tool data scientists use in Python. This Python library is available for free. It helps manage data in table form, such as process, clean, and explore. Furthermore, it also contains quick, expressive, and versatile data structures. These make it simple to deal with tabular data.

You will mainly work with rows and columns in Pandas. Therefore, it works similarly to a spreadsheet. Pandas is among the important data science libraries for effective workflows. It helps in the execution of data processing, munging, and wrangling. It is useful as data pre-processing is the most time-consuming (nearly 80%) stage in data science.

  • Matplotlib

This library is also widely used in developing interactive, steady, and animated visual elements using Python. It comes with a high-level user interface that tends to create instructive and visually appealing statistical graphs.

3. Machine Learning and Deep Learning

Data scientists need efficient knowledge of artificial intelligence, ML, and deep learning. A data science online course with certification teaches the basics of AI and ML, along with practical examples.

Machine Learning (ML) techniques aid in the development of machines capable of learning from past data.

Deep learning, a subset of ML, uses ANNs (Artificial Neural Networks) to develop its decision-making capabilities. Deep learning algorithms are used in fields like natural language processing (NLP), gaming, and speech/image recognition.

If you're using Python in data science, your ML, and deep learning knowledge will be constructive. As a data scientist, you may learn neural networks, data analytics, data visualization, and more topics essential to data science. Therefore, knowledge of applied deep/ machine learning is a vital Python skill.

To accomplish its tasks, ML uses statistical models and various algorithms. ML for data science contains the following algorithms:

4. Web Frameworks

Knowledge of web frameworks and their use is important if you're working on Python. You can use your Python skills to create and implement web applications. Through data science online training courses, try to learn and implement various web frameworks.

The two most used frameworks in Python are Flask and Django.

  • Flask

Flask is independent of any library or tool. This microweb framework comes with separate libraries and modules. Its features help users create web applications without coding at a low level. It also doesn't have any form validation, database abstraction layer, or additional typical features given by external libraries.

  • Django

Django is an extensive web framework. It offers a quick, clean, and practical structure. Additionally, it provides a huge number of libraries users can use to develop top-notch web apps without modifying existing codes.

5. Object Relational Mapper (ORM)

Object-oriented programming languages work on translating data across different systems. Object-relational mapper is the name given to this procedure. The ORMs help create a virtual object database using a programming language. Various programmable ORM tools help users, you can learn in a data scientist online course.

These tools are useful for Python developers to keep using Python code while querying. Therefore, instead of using SQL for tasks such as deleting, updating, reading, and creating, Python developers can use ORM.

These are some Python ORM tools

  • Django

It is an effective tool for developing web applications quickly.

  • web2py

It is a free-to-use Python framework. It develops quick, secure, scalable, and data-driven applications.

  • SQL Object

This ORM tool offers an object interface for the database.

  • SQL Alchemy

It offers continuous patterns for better and more effective database access.

Why learn Python?

1. Python is easy to adapt:-

Everyone, from beginners to senior-level professionals, can learn Python through a data science online course with certificate. It is so simple because of its syntax, which is built around the English language. Python's syntax is simple to learn, write, and interpret. It is a user-friendly language as it removes any unwanted code and syntax.

Python is also very efficient because it takes only 1 line of code to produce an outcome that will otherwise take 4–5 lines of code in other languages.

2. Python is versatile:-

Python's flexibility can be seen in its applications in various areas. Data science, gaming, AI, web development, etc., use Python to code efficiently. Industries ranging from technical to creative utilize Python as it is easily adaptable.

3. Python can boost your career in data science:-

As Python is a language applied to various industries, learning it gives a competitive edge over others. Many specialists see a boost in their career and income with Python as a skill.

Python developers are in demand mainly in the fields of data science as it proves highly advantageous. Top organizations, from Microsoft to GitHub, require data scientists with Python skills.


Python skills are of much importance in industries. Gaining the necessary skills and knowledge in Python will help level up your career. Individuals can also showcase their Python skills in interviews to impress recruiters.

A precise starting point for individuals with no prior knowledge would be to get data science online training. Implementation of Python with data science projects can solidify your skills. You will also understand the concepts in detail by applying your knowledge to practical scenarios.

Many aspirants start with a data scientist online course. You can learn, practice and even prepare for interviews through online courses. Additionally, you get the benefit of experts for your assistance at all times.

Join a data science online course with certificate to start learning Python skills in data science. An online data science certification is valuable for students to display their talents. You can study the fundamentals of Python from the Data Science and AI Master Program and practice in real-world projects with expert mentors.

This program also provides IBM and Microsoft certificates students can attain by completing the courses. These certifications enhance a resume and help the student stand out in interviews.

Frequently asked questions

1. Which certificate is best for data science?

Ans. An IBM data science professional certificate is the most preferred among aspirants. As IBM is a reputed institution, gaining an online data science certification from this organization shows the talent and capabilities of an individual. A data science course duration is mostly short so that you can get certified quickly.

2. Is IBM certification free?

Ans. There are some free as well as paid certifications IBM provides. Even though both are great options, getting a paid IBM certificate is more valuable. As free courses offer fewer features than paid versions, it is often advised to go with paid certifications to get the whole study.

3. Can data science be learned online?

Ans. A data science online course with certificate is worth the effort. You can start learning data science and understand the concepts like a live class and participate in active discussions with teachers and peers. Many students opt for data science online training as it saves them time and money to study from home. A data scientist course fee is also reasonable.