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Executive Practical Connection
Name
Institution
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Executive Practical Connection
Most organizations today understand the importance of hiring data scientists. On the
same note, higher learning institutions are started data science programs and have publications
that are meant to promote the necessity of undertaking the course. However, despite the
marketing of data analysis programs and attempt by many organizations to hire data analysts,
there is a confusion on the specific role of a data scientist in an organization (Wang et al., 2018).
The course provided a deep meaning and description of the roles of data scientists.
Through the course, I learned that the main role of a data scientist is to design, evaluate
and implement statistical methods and models in various challenges. In my workplace, data
analysis is used to design the statistical and econometric model for various issues such as
classifications, sampling, simulations, and projection. Organizations today face a challenge of
increasingly accumulating data due to increased use of electronic devices (Wang et al., 2018). As
much as data management is challenging due to the large volumes of data that organizations
have to deal with, it is important to note that data if proper management through big data
analytics leads to the making of fact-based decisions that are based on improved insights of the
organization operations.
In the organization I work, data from different sources are analyzed and used to create
patterns and trends that are used for marketing and engagement of customers. Customer
information regarding their preferences is used to design marketing campaigns. Similarly,
customer feedback, reviews and comments are used in the improvement of the quality of the
products (Wang et al., 2018). Even though the organization I work for is not in the healthcare
industry, through the course, I learnt the importance of big data analytics in the e-healthcare. The
matching of disease symptoms and drug prescriptions can be used to enable e-healthcare through
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big data analytics. The concept can be used in designing treatment plans for new diseases. Big
data analytics also increases the levels of prescription accuracy as the decisions made by
physicians will be supported by facts.
Regarding of Internet of Things, the devices that have been embed with artificial
intelligence work with data. The larger the volumes of data that a device can manipulate the
more the tasks the device can perform. For instance, robots interact with the data and make
meaning out of them. The other part of the course that I highly critical was the use of big data
analytics for business intelligence. Big data is used for business intelligence to help in making
accurate predictions about market, competitors, products and services (Wang et al., 2018). The
other concept that the course covered was big data mining which is the foundation of big data
analytics.
From the large volumes of data, organizations need to retrieve the information that
matters to gain insight into the operations of the organization. Through data mining,
organizations can retrieve relevant data from data sets without having to deal with the entire
database or pool of data. Importantly, through the course, I learned about various challenges of
big data. Markedly, the main challenge with big data is how to manipulate the large volumes of
data. For some organizations, storage of such data is a challenge as they require large-capacity
architecture. However, with the use of cloud storage, storage is no longer an issue. The last
aspect covered in the course is the bitcoin economics which is a blockchain cryptocurrency form
of digital transaction (Wang et al., 2018). The understanding of Bitcoin technology opened space
led to the understanding of other types of cryptocurrencies which were developed from the
bitcoin concept.
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Reference
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and
potential benefits for healthcare organizations. Technological Forecasting and Social
Change, 126, 3-13.