Data scientists need to be productive and efficient, which is why they need a programming language that can do the job. There are hundreds of programming languages, but some are better suited for data science projects than others.

Data fabric might sound like the latest buzzword in the enterprise technology world, but in our opinion, it deserves all the attention it has been getting and more. The sheer complexity of an organisation's data is on a steady increase. Your company's success depends on how well you manage, access, and analyse this data.

According to the Harvard Business Review, the Data Scientist Profession is the Hottest Career of the 21st Century. The job also offers numerous perks and competitive salaries with the rising demand. That is why data science has emerged as a lucrative career option in the industry. However, it is no child’s play to crack a Data Science interview.

The data science field has a very steep learning curve. Unless you know precisely where to look, it can be tough to break into the industry as a fresher. A data scientist needs real-life experience with various tools and technology stacks, not just project-specific know-how.

As a part of this year’s Teacher’s Day celebrations, Newton School has launched a heart-warming campaign titled #CareerKaCompass. Tell us who your #CareerKaCompass is!

Webflow is a powerful platform for bloggers of all kinds, from in-house marketing teams to independent creators. Here's how to start blogging with Webflow.

Employers usually divide the questions to be asked to backend developers into three categories. These include aptitude, technical domain expertise, and work experience or cultural fitness.

Whether you have just started to code, are advancing in the IT sector, or are an industry expert, reading books can be a game-changer. As a programmer, it is essential to form a habit of learning consistently. What better way than reading books to learn new things and upskill on a daily basis?