How to Identify Real-Time Data Science Problems?



Original Source Here

How to identify real-time data science problems?

Data is everywhere and that is indeed true. Let me tell you, no matter what industry or what business sector you belong to, you are dealing with data. Right from your smartphone to your smartwatch in your hand, you are constantly in touch with data and information. This information is then processed, used, analyzed to provide recommendations, predictions, and a lot of other things to improve user experience.

These data outcomes that are generated are not restricted to only improving user experiences, but it helps in making better decisions, generates hidden insights, adds value to the business and its stakeholders, and more. There are multiple ways through which the value of any organization can be increased. One such way is through solving data problems. Before that, important is to identify these problems and understand their impact of the same.

Image created by Akshay Toshniwal using Canva

Today, we use digital devices, that interact with us in different manners and processes the data in real-time. This is no magic. The magic is in identifying certain use cases and applying data science techniques to them. The use cases can range from simply analyzing and/or visualizing the data to making a certain level of recommendations for the end-users, it all depends on what is the problem and what outcome is to be generated out of it.

Few ways to identify real-world business problems that can be solved using data science methods and techniques

  1. Social Media: You use social media, right? Use your own social media data to understand what are your activities, how do you use them, what is the frequency of your usage, and a lot more.
  2. Streaming Platforms: Streaming platforms like YouTube, Netflix, Amazon Prime and a lot of others provide multimedia data in the form of videos, textual data in the form of user ratings and reviews, user comments, and more. These datasets can be used to identify how the platforms are performing and comparative analysis can be done as well.
  3. eCommerce platforms: eCommerce data is one of the most detailed datasets you can work on. It consists of a lot of information. Product information, user reviews, product images, category listing, product recommendation information, offers and discounts, and more. This data can be used for visualization, classification, prediction, and many other data outcomes.
  4. Day-to-day activities: There are many things that you might come across in your entire day. It can be when you watch a TV show or when you are writing a blog or when you are reading something. Ideas pop up to the mind to build something. Everything that you do is related to data today. You can keep a track of this little information, curate it, and generate a different kind of useful data outcomes.
  5. Business and financial data: Today, cryptocurrency is creating a lot of buzz in the financial market. The rise of investors for such cryptos is increasing. It will be a good idea to understand the crypto market by applying data science skills to track and monitor the data and accordingly further processing if needed. Besides that crypto, you can always use financial data to fetch company details and understand its performance.

To summarize in a nutshell, as data is available in abundance, it is important to identify different pain points in different areas and solve them for making things better.

AI/ML

Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot

%d bloggers like this: