The COVID-19 pandemic specifically has opened the doors to work more smartly by adopting the latest and advanced technology.

1. Essential capabilities of data scientist to have in 2022
The COVID-19 pandemic specifically has opened the doors to work more smartly by
adopting the latest and advanced technology. Even in the current year 2022, the
unusual circumstances are still continuing. But the best thing is data science is
continuing to grow more in every in industrial sector.
In this article let’s understand the top data scientist skills which include both technical
and non-technical skillsets that are required for enhancing the professional prospects
and to become a successful in the year 2022.
Technical skills required for being a data scientist
The most crucial data scientist skills are computing, statistical analysis, mining, and
processing large data sets. This also consists of very valuable data extraction. There are
many data science professionals who have Masters or a Ph.D. in engineering, computer
science, and statistics. Having a strong educational background offers them a strong
foundation to be aspiring data scientists. It will help them to acquire good knowledge of
various programming languages for data science and to be a success in the field such as
statistics and mathematics.
Many institutions offer specialized best data scientist certification programs in
addition to the existing educational requirements to pursue a data science career.
Where every individual can focus on the field of study they are interested in, that too in
a short time. Apart from these, few other most important technical skills are needed to
become a better data science professional include:
• Programming Languages
One should have good knowledge of various data science programming languages,
which include C/C++, SQL, Python, Java, and Perl. Among all these Python is the most
preferred coding language needed in the job roles of data science. The languages
support the data scientists to well-organize the unstructured data sets. This will also
help in transforming the data into proper insights. In the present scenario, many
aspirants are mostly considering Python and R languages as they are very easy to use.
Following are the most popular programming languages that will perfectly fit with the
skillsets, which is required for every data scientist:
➢ Python
➢ R
➢ Julia
➢ TensorFlow
➢ Java
➢ Scala

2. ➢ SQL
• Data Storytelling and Data Visualization
Data storytelling combined with data storytelling is among the top capabilities of data
scientist that are required to be mastered by every data science professional. This is the
most crucial part of the data science process where the data scientists will be apart
when compared to their data engineering colleagues. As they take up the unique roles
that include interacting with project stakeholders for giving the results of a data science
project, having compelling data visualization is the perfect process to deliver the best
results which come from a machine learning algorithm. It is also a main requirement of
the final data storytelling. Always keep looking for the latest data visualization
techniques by using the Python libraries and R packages to get an effective outcome.
• SAS and Other Analytical Tools
SAS is a software suite with built-in statistical functions and Graphical User Interface
(GUI). But it is quite an expensive enterprise software when compared to Python and R,
which are available for free to use. Having meaningful insights on the various other
analytical tools will be helpful for the data scientist professionals for getting the
relevant data from an organized data set and also to give useful frameworks. Hadoop,
SQL, Spark, Hoop, Hive, and Pig are the most preferred data analytical tool which most
of the data scientists consider for use.
Non-Technical skills
Not just technical skills like knowledge of data science programming languages. It is
very important to have enough non-technical skills too. These capabilities of data
scientist are not that are visible like the educational qualifications, certification courses,
and so on. Few must have non-technical skills are:
• Communication Skills
If one wants to accomplish better results in their role apart from just data extraction,
understanding the information, and analyze data. They should hone the communication
skills so that they well-communicate and share the knowledge among their team
members who might not have the same skillset which you have.
• Business Acumen
As a data scientist, you are job role also demands you to seek accurate solutions to
meet the business requirements. Possessing strong business acumen will also channel
the technical skills. An aspiring data scientist must be able to discern the issues as well
as the potential challenges that are required to be solved for the growth of the company.
This will also ensure to explore more business opportunities.