https://datascienceinstitutework.blogspot.com/2023/02/skills-of-data-science.html

3. Data science is the study of data to extract
meaningful insights for business,institute and organisation. It is
a multidisciplinary approach that combines principles and
practices from the fields of mathematics, statistics, artificial
intelligence, and computer engineering to analyse large
amounts of data.

5. Programming
Skills:
Proficiency in one or
more programming
languages such as
Python, R, or SQL is
essential for data
science. These
languages are
commonly used for
data manipulation,
analysis, and
modelling.

6. Statistics and
Mathematics:
A solid foundation in
statistics and
mathematics is crucial
for data science.
Knowledge of
statistical methods,
probability theory,
linear algebra, and
calculus is necessary
for performing data
analysis and modelling.

7. Data
Wrangling:
Data wrangling is
the process of
cleaning,
transforming, and
preparing raw data
for analysis. It
involves skills in
data cleaning, data
preprocessing, and
data integration.

8. Machine
Learning:
Machine learning is
an important aspect
of data science, which
involves building
models that can learn
from data to make
predictions and
classifications.
Knowledge of machine
learning algorithms
and their applications
is essential.

9. Data
Visualization:
Data visualisation is
the process of
presenting data in a
visual form to
communicate insights
effectively. Skills in
data visualisation
tools such as
Tableau, matplotlib,
or ggplot2 are
necessary for
creating effective

10. Communication
Skills:
Data scientists need to
be able to communicate
their findings and
insights effectively to
non-technical
stakeholders. They need
to have strong
communication skills,
both written and verbal,
to explain complex data
science concepts to a
wider audience.

11. Skill of Data Science