Ways To Learn Data Science While Working From Home

work from home

With COVID-19 adversely spreading, the only option to control the pandemic is going into a zone of self-quarantine. Keep that in mind, most of the organisations have ensured that the employees work from home to safeguard themselves and others around from getting infected with Coronavirus. Although these are tough times, work from home does provide an opportunity to learn data science since a lot of time is saved. But, when quarantined at home, how can one learn data science?

In this article, we will try to explore a few points that might help one learn data science while being stuck at home.

Online Courses

With social distancing a major guideline to follow at these times of peril, the idea of joining a full-time course goes right out of the window. The best option one can opt for is to register with a traditional online course in data science. It is true that an individual must have a lot of work even though at home, online courses are feasible options since a lot of time gets saved, which usually goes on waste due to unwanted hurdles such as getting stuck in a traffic jam.

Online courses come with a variety of schedules, and one can attend them as per their convenience. The courses are designed with quality content and are available in different formats and levels, which one can choose as per their educational background and skill sets they possess. But it does have some drawbacks in a few courses where contents are limited and can be accessed only if one completes the current level. Also, in case a learner is facing a problem, immediate suggestion or help is not present quickly. One can click on the link to know about some free online courses.

Massive Open Online Courses (MOOCs)

When we talk about MOOCs, there is a need to lay down clear differentiation between MOOCs and online courses or confusion often mounts up. MOOCs focus more on context rather than content, and it’s dynamic building up of context around content makes it unique. MOOCs are designed on the principle of micro-learning with no learning required to go beyond 10 to 12 minutes unless the topic really needs a detailed understanding. 

MOOCs are the answer to the drawbacks of traditional online courses. MOOCs give a learner the liberty to choose an online course in data science and learn at the pace they want to. All the materials can be accessed at any time irrespective of the current level a learner is in. The courses can be accessed from anywhere at any time, making them extremely remote-friendly in nature. Google has its own MOOCs, such as “Learn with Google AI” along with other crash courses in machine learning that are available to the public for free. Microsoft too has its “AI Learning Track” that provides free training courses to everyone. The courses come with a number of exercises, visualisations and instructional videos. To know more about MOOCs courses, feel free to read our article by clicking on the link, and to select the best courses, click here.

Books

It is essential to understand that data science is a vast field and is not just about computing. It covers several subjects such as mathematics, statistics, probability, programming and more. Due to this reason, books are the best companion that can provide a holistic view of data science. Be it a compilation of various topics or a detailed explanation of one topic, books are available, and some of them are available for free. Moving on, books have been curated by different authors for different kinds of readers, so books are a good-to-go option for beginners as well as professionals. For beginners who are wondering which book, to begin with, here’s a link that might be of help and professionals can increase their knowledge with these books.

Webinar

Webinar serves as an opportunity to be a part of events to educate or even educate about languages, tools and other topics related to AI, ML and data science. Although physical appearances are not associated with a webinar, interacting opportunities are present in ample options such as chat, poll, survey, test, call to action and Twitter. One can easily access these webinars on their desktop, tablet or smartphones, thanks to audio and video feed. For those who are in search of data science webinars that are about to take place in a matter of a few days, here’s a link.

Online Community

Online communities can help a person learn a lot about the field of data science. One can begin by posting their queries on platforms like Quora or Reddit, which are often answered by experts from the field. Twitter and Linkedin play an important role as it helps in connecting with mentors and experts from the field who can give an insight on a wide variety of topics.

Moving on, Kaggle and MachineHack are a must for anyone who is interested in the field of data science. From sharing projects or dataset to the public for open criticism and feedback to training models and picking up programming languages, these two platforms house more than millions of developers who are ever-ready to guide someone in need. Last but not least, online hackathons are another place, which gives a learning experience by collaborating with designers, developers and subject matter experts to build a prototype.

More Great AIM Stories

More Stories

OUR UPCOMING EVENTS

8th April | In-person Conference | Hotel Radisson Blue, Bangalore

Organized by Analytics India Magazine

View Event >>

30th Apr | Virtual conference

Organized by Analytics India Magazine

View Event >>

MORE FROM AIM
A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks. 

Yugesh Verma
A guide to explainable named entity recognition

Named entity recognition (NER) is difficult to understand how the process of NER worked in the background or how the process is behaving with the data, it needs more explainability. we can make it more explainable.

Yugesh Verma
10 real-life applications of Genetic Optimization

Genetic algorithms have a variety of applications, and one of the basic applications of genetic algorithms can be the optimization of problems and solutions. We use optimization for finding the best solution to any problem. Optimization using genetic algorithms can be considered genetic optimization

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM