data science weekly november

This week has been quite exciting for data science. From discovering new potentials for the aviation industry to exploring augmented analytics, the spectrum of data science news has been quite wide. We have curated the top trends and developments of the field for data science enthusiasts and professionals like you. Keep reading!

Only Data Can Save Airlines Operations Now 

One of the biggest challenges Airlines are facing today is cutthroat competition and low profit margins. Many of the national airlines including Kingfisher,  Modiluft, Eastwest and more recently Jet Airways have closed shop due to the lack of operational success. The most effective way of solving this growing challenge is data analysis. The Aviation industry needs to embrace the data revolution to tackle problems with bandwidth and connectivity. Companies like SpaceX and more are helping airlines to boost connectivity. Apart from aircraft maintenance and hospitality services, the main cost incurring component of aviation companies is fuel. Data-driven air traffic management systems like TASAR (Traffic-Aware Strategic Aircrew Requests) can save fuel consumption by optimising flight routes. Based on freely available Automated Dependent Surveillance-Broadcast (ADS-B) data, these route and traffic management systems have the potential to transform the aviation industry and increase revenues.

data science in aviation industry

Why should you learn R for Data Science?

You know that data is ruling the world today and has created lucrative career opportunities for professionals in recent years but not many are sure which is the most useful language for data science. R has emerged as the most flexible and adaptable programming language and environment. It has the most intensive environment to examine, model and visualise data. Owing to its flexible structure, it is widely used across industries and since it’s an open-source tool, it can be used and maintained by an active community. Developers prefer R for its robust packages and aesthetic visualisations. Community support also accounts for its vast popularity. Updates and troubleshoots can be aided by community members. Since it is both a language and environment for statistical computing and graphics, R is quite multifunctional.   

Automated tools broaden the future scope of Data Science

Reports suggest that technologies like augmented analytics will increase the reach and efficiency of data science within enterprises. Contrary to popular belief, data scientists jobs will not be threatened by this automation, in fact, it will boost productivity and make the job easier. “I actually do not believe at all that they’re capable of replacing our jobs.” – Usama Fayyad, co-founder of the Initiative for Analytics and Data Science Standards. Gartner has predicted that there will be a 45% reduction in manual tasks in the fields of data management in the next three years as AI tools will be better equipped to categorise, prep, integrate and manage datasets. Automation will strategise more scopes for data science. 72% of organisations believe that they need to keep up with this trend of automation to avoid losing out. 

Why should you learn Python for Data Science? 

If you are confused about which programming language to pick up for data science, settle for Python. It’s been a favourite among data scientists for a long time now and not without reason. Even now, Python is the most widely used programming language in the field of data science. Owing to its ease of learning, readability and usability, Python is as popular with professionals as it is with beginners. It can also interface with high-performance algorithms written in C or Fortran. Commonly used in data mining, web development, scientific computing, and more Python is an easily adaptable language for data modelling and visualisation. Learn more about Python and how you can upskill to become a data scientist.

How to Optimize your LinkedIn profile as a Data scientist? 

Creating a proper professional profile and networking in your domain is as important as picking up the right skills as a data scientist. Linkedin is currently the biggest professional network that data scientists can leverage to avail the best career opportunities, learn about the latest trends of the field and more. Having the right LinkedIn profile can go lengths in securing your career success. From basic information including a profile photo and professional summary to highlighting your pet projects and endorsements, this article shares some guidelines on how to create a stellar LinkedIn profile for data scientists. 

Watch this space for more such updates every week.

0

LEAVE A REPLY

Please enter your comment!
Please enter your name here