Working alone as a data scientist is not enough to boost one’s career because organisations are always looking to find something extra from their employees. In short, an organisation might expect data scientists to deliver different functions. So the question is how can a data scientist learn to increase their knowledge while still working full-time?

In this article, we will discuss a few pointers through which data scientists can increase their knowledge while performing their full-time duty and can be ready to perform different tasks. 

1. Research Papers: A data scientist should keep an eye on the research papers that are being released by experts in a week or a month. It is important to give a thorough read of a research paper since these new papers are the original source for the invention of any new technology. For example, a working data scientist must read the new research paper by Gary Marcus.
2. Blogs: Artificial Intelligence (AI) blogs are another great source of reading from which a data scientist can keep oneself update about latest developments being done in the data science space by technology giants. Some of the blogs one can look at are Google AI blogs and Microsoft AI blogs.
3. Fictional Books: It is true that there is no end to reading since knowledge is vast, and one cannot acquire everything. Still, a data scientist can try to read up a number of nonfictional AI books such as Together: AI and Human. On The Same Side, Rise of the Robots: Technology and the Threat of a Jobless Future, A Compassionate Guide For Social Robots and The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. One can also check a list of 55 carefully curated AI books.
4. Teaching: The idea of penning down this article is not just about finding ways to learn new things in data science but also remember the old lessons. A data scientist can begin teaching classes and provide with basics of data science to students. In this process, the clarify doubts and look at a number of new projects.data scientist can brush up the old learnings.
5. Community Learning: This could be another approach a data scientist can take in order to stay in the loop of current advancements. It is advisable to be a part of communities like Kaggle and GitHub where one can indulge in several discussions, 
6. Hackathons: It is not enough to be a part of a community and just discuss certain new papers or projects. The more vital aspect to be looked at is to stay in the game and for that, one needs to take part in several hackathons. One of the best places to take part is MachineHack, which allows a user to practice and then take part in hackathons against hundreds of Data Scientist.
7. EdTech Platforms: MOOCs or Massive Open Online Courses are other sources of learning more about data science. There are a number of courses curated by several online academies such as Udemy, Coursera and edX where one can find the ideal topic to increase one’s data science knowledge base.
8. Weekend Courses: A full-time data scientist can always go for a part-time machine learning courses which are usually held on weekends and are curated for working professionals. The courses are slow-paced with ample time provided to cope up with the subject, along with a number of projects at disposal.
9. Conferences: A data scientist can also take part in a number of data science conferences, summits and fairs such as MLDS and Cypher. These are a great source of meeting experts from the field. It also allows having an open discussion, which could lead to new learnings.

One can further read our articles on training programs and learning data science in six months to have a better idea of how to begin their journey of new learning.