The fields of machine learning, data science and AI are always evolving and these skills have an increasing impact

The subjects of data science courses for women and coding in Python. It does not require any previous knowledge or experience as it starts right from the basics. However, unlike some other very entry-level courses, it does progress to some actual practical instruction in Python and, particularly usefully, its Sci-Kit learn framework, a very popular tool for academic and enterprise-level data exploration and mining.

This means that practically anyone can upgrade their employability and career prospects by learning the fundamental theory and practical skills needed for data science. And luckily, there’s a ton of resources about data science courses for women to do that.

Some of these might require payment at the end of the course if they want official certification or accreditation of completing the course, but the learning material is freely available for everyone who wants to level up their data knowledge and skills.

1. Data Science Crash Course, John Hopkins University (Coursera):

Designed to give a “fluff-free” overview of what data science courses for women, how it works, and what it can be used to do. This course offers an introduction to the technical side of data science but is particularly aimed at understanding the “big picture” for those who need to manage data scientists or data science work.

It’s a relatively short course consisting of just one module that can be completed in under a week and serves as a great introduction for those who want to learn the terminology and understand how to build a data science strategy, without necessarily needing detailed instructions on using the technical tools involved.

2. Introduction to Data Science (Revised) – Alison:

A completely free course that breaks down the core topics of the data science courses for women it processes an introduction to machine learning into three modules, each designed to take around three hours to complete, and concluding with an assessment. Once you’ve worked through that, you can choose from several other similarly bite-sized tutorials covering data programming languages, visualization tools, and techniques such as building clustering and regression models.

3. Data Science and Machine Learning Essentials – Microsoft (EdX):

This course, aimed at those wanting to improve their career prospects with a mix of practical and theoretical knowledge, walks you through core concepts and terminology, statistical techniques such as regression, clustering, and classification, and the practical steps needed to build and evaluate models.

As it is a Microsoft course, its cloud-based components focus on the company’s Azure framework, but the concepts that are taught are equally applicable in organizations that are tied to competing cloud frameworks such as AWS. It assumes a basic understanding of R or Python, the two most frequently used programming languages in data science, so it may be useful to look at one of the courses covering those that are mentioned below, first.

4. Learn Data Science – Dataquest:

Although primarily a paid-for platform offering proprietary content, Dataquest offers a number of free introductory modules to anyone who signs up, covering essential topics such as working with data, visualizing data, data mining and constructing algorithms in Python and R. If you want the full, ad-free experience and certification there are monthly subscription options, but there’s more than enough information to get started free of charge.

5. Data Science – Harvard:

All of the class materials and lectures for Harvard’s data science course are made freely available online, so they can be studied at your own pace. You may not end up with a degree from one of the world’s most prestigious universities, but the course is detailed and technical enough to make an expert of you by the end. The course is part of a data science degree and constructed for students who have prior knowledge of, or are also studying, core fields such as programming, maths, and statistics. However, there are enough free resources out there on those subjects to make this a viable option for those outside of academia, if you are dedicated enough.

6. Introduction to Data Science in Python – University of Michigan (Coursera) :

Those wanting to get their hands dirty with some actual coding will soon find out that Python is one of the most commonly used programming languages in the field, and for good reason. It’s relatively simple to learn the basics and can be combined with a number of free, open-source libraries to perform hugely powerful data science operations.

This course serves as a first step along the road, introducing Python functions that are used to prepare and manipulate big datasets as well as the proven techniques for extracting insights from data. It is intended to be completed by spending between three and six hours per week studying or working on exercises, over four weeks.

7. Learn Data Science with R – Ram Reddy (Coursera):

This course led by an established expert in R and data analytics is the first in an in-depth, ten-part tutorial on expert R programming, but also stands on its own as an introduction to the language and a primer on the basics as they relate to data science.

Like Python, R is a totally free and open-source language and environment that has become an accepted standard among data scientists due to its power and flexibility. This course consists of 10 lectures delivered across eight hours of video, and is completely free to follow.