problems we phase in data science

The Eduladder is a community of students, teachers, and programmers just interested to make you pass any exams. So we solve previous year question papers for you.
See Our team
Wondering how we keep quality?
Got unsolved questions?

Ask Questions
Data-StructuresDS-10CS35-->View question

Problems faced in Data Science.

What kind of problems are faced in Data Science while working  with data?


Taged users:


Be first to dislike this question

Talk about thisDelete|Like|Dislike|


It is becoming increasingly apparent that data scientists need to demonstrate skills necessary to convert data-based scientific inference into accessible, actionable insights for business and upper level management. Today's data scientists need to both straddle the worlds of business boardrooms and IT as well as become a hybrid of them. 

But does their software support them in achieving these lofty goals? A data scientist worth his salt uses applications that help him surmount the three key challenges to his job. 

Multiple Data Sources 

The latent value of big data is best mined when data scientists can reach across the expanse of the data landscape and access data from multiple platforms and data sources. Deeper and often more meaningful insights can be gleaned, the more relevant data that the inquiries have at their disposal. With cloud-based, integrated data platforms like ClicData, virtual data warehouses can be 'built' that effectively connect data from numerous locations, arriving in a variety of formats, at different times, captured both in batch and in real-time. This more inclusive reach means more useful inferences and insights. 

Customers Insist On Interacting 

The new data scientist needs to go beyond delivery of historically-driven reports and provide actionable answers in environments that give the customer control. ClicData full-featured dashboards allow data scientists to prioritize the metrics and indicators that are relevant to the strategic goals and objectives of the business, and communicate them in the language of C-level stakeholders. As such the work they do has a direct and immediate impact on the business. 

Communicating with Real People 

These days, data scientists must do more than understand their data; they need to make their data understood by others. The results of their work are used to resolve business problems,create an efficient supply chain, automate of operations, nourish customer relationships, launch revenue lines, and establish strategic competitive advantages. 

Dashboard software like ClicData offers a wide range of visualization widgets to make the data meaningful and actionable, choosing the right tool to graphically display and convey the crucial and supportive insights that are needed. 

Communication becomes automated with the ability to distribute results, reports and performance indicators to chosen groups or users. Notifications and alerts are set up according to predetermined conditions. As the business model embraces collaboration, these tools are essential to business-wide communication. 


Be first to like this answer

Be first to dislike this answer
Talk about this|Once you have earned teacher badge you can edit this questionDelete|Like|Dislike|

Can you help us to add better answer here? Please see this

Not the answer you're looking for? Browse other questions from this Question paper or ask your own question.

Join eduladder!