As we know,
there is an increasing demand for data scientists and data science
applications. Still, many difficulties and issues are faced by scientists.
These overlap with the data science field. As a result, many questions were
raised about the research challenges faced in data science.
In this article,
we will see some of the research areas on which data scientists should focus to
improve their research efficiency. If you want professional mentoring to become
a data scientist, you should enrol in data science courses in Delhi.
1. Deep Learning
We respect and
praise the success of deep learning, but deep inside, we don’t have a logical
or explanatory understanding of why it is so much efficient. There are many
properties of deep learning which we don’t analyze. Deep learning models
produce specific outcomes, and we have no clue how to clarify them. We have no
idea whether on giving new inputs, deep learning can perform the given task or
not. It is an example where experimenting is way too ahead of any theoretical
understanding. To understand better, you can take some data
science courses in Delhi.
2. Synchronized Video
Analytics
As the internet
is easily accessible even in undeveloped nations, videos have become one of the
most reliable sources to share information. Telecom systems, news channels, and
CCTvs are boosting the role of videos to share information.
Now, even
real-time videos can be shared with almost negligible latency and accessed from
anywhere across the globe through the cloud.
3. Protection
Nowadays, as we
have sufficient information, we can design better models. Sharing information
is one of the best approaches to get more information, e.g. many organizations
distribute their datasets to more than one party rather than giving it to one.
Because of specific
guidelines or privacy terms, protecting the confidentiality of the datasets of each
party has become a necessity. Therefore, data scientists are investigating some
adaptable ways using statistical analysis for parties to share models and information
to ensure the security of each dataset.
Government and
private industries are finding different methods and ideas like differential
privacy, zero-knowledge proofs, and homomorphic encryption to find a solution
to his problem
4. Carefree Reasoning
Artificial intelligence is an important area that is useful in analyzing
different patterns and relationships in massive datasets. AI has given us many research
zones, and these fields require advanced technologies that can handle casual
inquiries. Data scientists have started exploring various inferences, not only
to overcome strong assumptions but because many actual observations are
interacting with each other.
Financial analysts are using casual reasoning to invent new
strategies with the help of economists and AI that could make induction
estimation more useful.
