We take a look at the top free Python courses available for data scientists in 2021. 

Python for data science: Cognitiveclass.ai

The course by Cognitive Class is beginner friendly, and meant for anyone wanting to learn programming with Python for Data Science. It will enable a data scientist to know from zero to programming in Python in approximately 20 hours, enabling them to write their own Python scripts and performing basic hands-on data analysis on Cognitive Class’ Jupyter-based lab environment. 

The course is self-paced and can be audited as many times as one wishes. While a data scientist gets only one chance to pass the course, they get multiple attempts per question. When signing up for this free course, data scientists will get free access to Watson Studio. The course’s modules include basics of expressions, variables and string operations, data structures, programming fundamentals, and working with and saving data with pandas. 

The course is offered by IBM’s Data Scientists Joseph Santarcangelo. The prerequisite for taking this course is a basic knowledge of Math. Data Scientists can opt for an instructor-signed certificate for the course for Rs 7,382, from edX platform.

Python project for data science: Edx

This course is not for someone who intends to learn Python, but for someone who plans to demonstrate their functional Python skills for working with data. It is a part of the IBM Data Science Professional Certificate and the IBM Data Analytics Professional Certificate. It allows a data scientist to build a dashboard using Python and Jupyter notebook. 

The intermediate level course takes about six hours to complete and offers a shareable certificate upon completion. 

Again an IBM course, it is delivered by instructors Azim Hirjani, Cognitive Data Scientist, and Joseph Santarcangelo. Before taking this course, a data scientist must complete the Python for data science, AI and development course from IBM, or have equivalent proficiency in working with Python and data. 

Practical Python Programming: David Beazley 

Veteran Python coder David Beazley offers a course for corporate training and professional development. The course covers all fundamental aspects of Python programming with emphasis on script writing, data manipulation and programme organisation.

After the completion of this course, learners should be able to start writing Python programmes on their own or Python programs on their own or be able to understand and modify already written Python codes.

Beazley has been continuously developing the programme since 2007. The course requires approximately 25 to 35 hours of intense work, and includes the completion of approximately 130 hands-on coding exercises. It is licensed under a Creative Commons Attribution ShareAlike 4.0 International License. 

The prerequisites for this code includes a basic installation of Python 3.6 or newer versions, and having prior programming experience in either Python, or some other programming language. 

Foundations of Data Science: University of London 

Offered by the University of London, the Foundations of Data Science: K-Means Clustering in Python focuses on the basic mathematics, statistics and programming skills necessary for typical data analysis tasks. 

During the course of this about 29 hours programme, a data scientist will be asked to carry out a series of mathematical and programming exercises and a small data clustering project for a given dataset. After taking this beginners course, one will learn demonstrating the understanding of key constructs and features of Python, implementing Python principle steps of the K-means algorithm, and designing and executing a whole data clustering workflow to interpret the output. 

The course is delivered by– Matthew Yee-King, lecturer; Betty Fyn-Sydney, Lecturer in Mathematics; Jamie A Ward, Lecturer in Computer Science; and Larisa Soldatova, Reader in Data Science– at the Department of Computing in Goldsmiths, University of London.

For more information, click here. 

Statistics for Data Science with Python: Class Central 

Offered by IBM, this course is designed to introduce one to the basic principles of statistical methods and procedures used for data analysis. After completing this course, a data scientist will have practical knowledge in data gathering, summarising data using descriptive statistics, displaying and visualising data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to AVONA, regression and correlation analysis. 

The course takes about 12-hours to complete and does not require any computer science or statistics background. However, it is recommended that one should take the Python for Data Science course before opting for the Statistics for Data Science with Python course. 

It is offered by Murtaza Haider, Professor of Data Science and Real-Estate Management at Ryerson University, and Aije Egwaikhide, Senior Data Scientist. 

Automating real-world tasks with Python: Coursera 

Offered by Google, this is the final course of a six-part course offered by Google that teaches learners to analyse real-world IT problems and implement the appropriate strategies to solve those problems. On taking this course, data scientists will have to tackle real-world scenarios in Qwiklabs that will challenge them to use multiple skills at once. 

By taking this course, learners will be able to use Python external libraries to create and modify documents, images and messages; they will understand and use APIs to interact with web services, understand and use data sterilisation to send messages from running programmes; and build a solution using the skills they have learnt. 

The course teaches manipulating images, interacting with web services, and automatic output generation. The programme takes about 13 hours to complete. 

Advanced Portfolio Construction and Analysis with Python: Coursera 

This course by EDHEC Business School, the course focuses on hands-on implementation of Python programming language in investment management. The course syllabus includes Style and factors, robust estimates for the covariance matrix, robust estimates for expected returns, and portfolio optimisation in practice. 

At the end of this course, the learner will have a foundational understanding of modern computational methods in investment management. On completion, data scientists will be able to use the toolkit to perform their own analysis. 

The course is taught by Lionel Martellini, Director at EDHEC-Risk Institute, and Vijay Vaidyanathan, CEO at Optimal Asset Management Inc. 

Data Science Fundamentals with Python and SQL Specialisation: IBM 

This course can be taken by data scientists who want to learn about Python programming basics including data structures, logic, working with files, invoking APIs, and libraries including Pandas and Numpy. The course also teaches about statistical analysis techniques including descriptive statistics, data visualisation, probability distribution, hypothesis testing, and regression. 

Additionally, by taking this course, one will get the working knowledge of data structures tools including Jupyter Notebooks, R Studio, GitHub and Watson Studio. 

Offered by IBM, this course offers a flexible schedule, and a certificate upon completion, and approximately takes six-months to complete. The prerequisites for taking this course includes basic computer literacy. No prior knowledge of computer science or programming languages is required.