Free Python Courses for Data Science Beginners

Explore Free Python Courses for Data Science Beginners
Free Python Courses for Data Science Beginners

In today’s world, information or data has become the world’s new money. Data analysis and interpretation remain key competency sets, especially for data scientists. For aspiring learners keen on starting this journey, Python emerges as the most preferred programming language due to its ease and use in a wide range of applications.

Learning Python is super supportive if you’re looking to switch to a data career. But there is a lot to learn, from the essentials of Python programming to data analysis, machine learning, and splitting coding interviews. So, how do you discover the best resources to learn them all?

Here are the courses that can assist you in learning how to use Python for data science or enhance your skills in Python programming. These free Python courses are suitable for both beginners and advanced levels of Python coding, especially for those who need a refresher in Python. The courses will help you gain knowledge about the basics of Python, Python data science libraries, Python number analysis, machine learning, and data structures and algorithms using Python.

Free Python Courses for Data Science

Here is the list of free Python courses for data science that can help you enhance your skills in the field.

Python for Beginners

The Python courses for beginners from Mosh will help you become familiar with the basics of Python programming.

In almost an hour, you can get up and running with the following basics:


Receiving input

Type conversions


Operator's and administrator's priority

If explanations

While and for loops

Lists and tuples

Intermediate Python Programming

Now that you know the basics, you can take this Intermediate Python Programming course. This course begins by discussing the different Python built-in data structures and then progresses to more advanced highlights of the language.

The themes secured in this course include:

Python’s built-in data structures



Lambda functions

Exceptions and errors


Working with JSON

Random number era



Multithreading and multiprocessing

Function arguments

Shallow vs. deep duplicate

Context managers

Data Analysis with Python

Once you have a great grasp of Python, you can continue to learn about the different Python data science libraries. The Data Analysis with Python certification from freeCodeCamp will help you learn all the essential Python data science libraries:





You will also get to construct a few data analysis ventures. You must complete this to get the Data Analysis with Python certification.

Machine Learning with Python and Scikit-Learn

You must now be comfortable programming with Python and working with Python data science libraries. And you can currently begin investigating machine learning.

Machine Learning with Python and Scikit-Learn will help you learn about the hypothesis (how machine learning algorithms work) and the execution of machine learning algorithms with scikit-learn. This course will also teach me how to approach and arrange machine learning, extend, construct, and send machine learning applications.

Here’s an outline of the points secured:

Linear regression and gradient descent

Logistic regression for classification

Decision trees and random forests

How to approach machine learning ventures

Gradient boosting machines with XGBoost

Machine learning venture from scratch

Deploying a machine learning extension with class

Information Structures and Calculations in Python

In the information science meet handle, you ought to begin with split coding interviews to continue to the other stages. To break them and to make your coding hone sessions more compelling, start with a solid establishment of information structures in algorithms.

Data Structures and Calculations in Python is a free course that focuses on Python and will help you learn the basic information structures and algorithms. Just take this information structure into account when calculating scores. The taking after Information Structures and Calculation Sports will offer assistance to you in learning the taking after points. 

Binary look, connected records, and complexity

Binary look trees, traversal, and recursion

Hash tables and Python dictionaries

Sorting calculations, separating and overcome

Recursion and energetic programming

Graph calculations

Python meet questions, tips, and advice

Role of Python in Data Science

Analyzing and visualizing data forms a vital part of Data Science today. Python's toolkits, which are used to analyze and visualize data, have addressed the common challenges currently facing data science.

Exploring Datasets

The process should be seen as something other than a formal step in the data analysis process. However, it is widely accepted that exploring datasets is a crucial stage in the process. Pandas of Python provides utilities for loading data and writing data to file formats, including Excel and SQL. One of its large areas of application is working with table data, which means data placed in tables or database spreadsheets. Besides data understanding and analysis, pandas also offer data discovery, data scrubbing, and data pre-processing.

Data Cleaning and Preprocessing

Data cleaning and preprocessing are imperative before data is analyzed, and this is why they hold great importance. In the following steps and Sub-steps, the Pandas tool of the Python language, as provided, stacks all data cleaning and preprocessing tools, including removal of duplicate data and missing data, as well as data transformation. It also has handy data munging methods such as sample, subset, melt, and join, which, in fact, allows a user to manipulate a data frame.

Data Wrangling and Manipulation

Data pre-processing is the process of cleaning and transforming raw data into a form that can be readily used in data analysis. NumPy offered the most fundamental functions for operating on arrays, including indexing, slicing, and reshaping of arrays using Python’s array library. Apart from arrays, NumPy also contains tools for mathematical manipulations of arrays like addition, subtraction, multiplication, and division. An introduction to Pandas in Python: Pandas include libraries for data analysis tools like selecting, filtering, or aggregating data.

Generating Statistical Reports

Making Statistical reports forms part of the overall process of data analysis. The statistical analysis tools for hypothesis testing, regression analysis, and cluster analysis were all available from Python’s SciPy library. Matplotlib is another widely used Python toolkit for data visualization, including line graphs, scatter graphs, bar graphs, and histograms. Matplotlib, for instance, is ideal for generating clear and well-organized graphics needed to display scientific manuscripts and documents.


1. Is Python free or paid?

Yes. Python is a free, open-source programming language that is available for everyone to use. It also has a huge and growing ecosystem with a variety of open-source packages and libraries. If you would like to download and install Python on your computer, you can do so for free at

2. Can I get a job with Python?

Yes, the popularity and demand for Python programming—and the demand for Python Developers—continue to grow at a consistent rate. One estimate has over 40,000 jobs currently available for professionals with Python programming skills and experience.

3. Can I complete Python in 3 months?

Basic Python learning can take around 2-6 months. As a complete beginner, you can start by learning the basics of programming concepts such as data types, Python Strings, Flow Control Statements, Variables, and File I/O. Once you get the hang of the basics, you can move on to building more complex programs and projects.

4. Can I learn Python on my own?

Yes, it's possible to learn Python on your own. There are many learning resources available on the web to help you learn Python for everything from web development to artificial intelligence.

5. Can I get a certificate from Python for free?

This free Python course with a certificate has no prerequisites, making it ideal for those with no prior programming experience. You'll need a computer with internet access, a text editor, and Python installed to follow along with the course. The course covers setting up Python in your system as well.

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