Data Science: Definition, Types, and Lifecycle
Key Points
- Data science is defined as extracting knowledge and insights from noisy data and converting those insights into actionable business decisions.
- It sits at the intersection of computer science, mathematics, and business expertise, requiring collaboration across all three domains for true data‑science initiatives.
- Different analytics methods serve varying business questions: descriptive (what is happening), diagnostic (why it happened), predictive (what will happen), and prescriptive (what should be done).
- The data‑science lifecycle begins with solid business understanding to ensure the right problem is tackled, highlighting the critical role of domain knowledge.
- After defining the problem, the process moves through data mining, data cleaning, and subsequent analytical steps to generate insights and recommendations.
Full Transcript
# Data Science: Definition, Types, and Lifecycle **Source:** [https://www.youtube.com/watch?v=RBSUwFGa6Fk](https://www.youtube.com/watch?v=RBSUwFGa6Fk) **Duration:** 00:07:51 ## Summary - Data science is defined as extracting knowledge and insights from noisy data and converting those insights into actionable business decisions. - It sits at the intersection of computer science, mathematics, and business expertise, requiring collaboration across all three domains for true data‑science initiatives. - Different analytics methods serve varying business questions: descriptive (what is happening), diagnostic (why it happened), predictive (what will happen), and prescriptive (what should be done). - The data‑science lifecycle begins with solid business understanding to ensure the right problem is tackled, highlighting the critical role of domain knowledge. - After defining the problem, the process moves through data mining, data cleaning, and subsequent analytical steps to generate insights and recommendations. ## Sections - [00:00:00](https://www.youtube.com/watch?v=RBSUwFGa6Fk&t=0s) **Defining Data Science and Its Scope** - The speaker defines data science, outlines its three‑discipline foundation of computer science, mathematics, and business, and introduces the hierarchy of analytics methods, beginning with descriptive analytics. ## Full Transcript
let's talk about data science and some
of the other related terms you may have
heard such as predictive analytics
machine learning
advanced analytics and others
so let's start with the textbook
definition of data science
so data science is the field of study
that that involves extracting
knowledge
and
insights
from
noisy
data
and then turning those insights
into
actions
that our business or organization can
take
okay so let's dig into it a little bit
more and discuss what are the different
areas that are covered by data science
so really data science is the
intersection between three different
disciplines
we start with
computer science
then
we also cover
the area of mathematics
and then what i think is the most
important
is business
expertise
so the intersection of these three
disciplines is data science
and true data science initiatives
involve collaboration across all these
three different areas
okay so now let's touch on the different
types of data science that you can do
now what we need to understand here is
that we have different data science
methods
for different questions that we might
ask in an organization
and these questions can vary by
complexity and the value that we get out
of them so let's chart them here
by complexity
and
value
okay so the first one that we have here
is
descriptive
analytics
so this is really about what is
happening in my business right and it
involves having accurate data collection
to make sure that we know what's
happening so a a good question we could
ask here is
well did sales go up or down
the next level is
diagnostic
analytics
and this is more about why did something
happen so why did sales go up or down
and it involves
drilling down to the root cause of our
problem
now the next one that we have is
predictive
analytics
so this is about what is likely to
happen next
right so what will our sales performance
be next quarter
and it involves using historical
patterns in our uh in our data to
predict outcomes in the future
and then finally
we have
prescriptive
analytics
so this is about what do i need to do
next what is the recommended best action
for a particular outcome so question we
could ask here is what do i need to do
to improve sales by 10
right
okay
so now we can talk about how data
science is done and who actually does it
so let's look at the data science life
cycle and the first thing that we always
must start with is
business
understanding
so this is really critical to make sure
that we're asking the right question
before we go down a lengthy data science
initiative
and this is where you can see the having
the business expertise and the domain
expertise
can be incredibly critical to make sure
that we're asking the right questions
okay so once we've defined that
we can move on to
data mining
so this is this is the process of
actually going out into our data
landscape and procuring the data that we
need for our analysis
so once we've done that
we can move on to
data
cleaning
so
the the reality of the marketplace is
that
once we when we find data it's probably
not in the best format that we need it
in and it probably has uh some some
issues with it right it might have rows
that have missing values it might have
duplicates in it so there's some
preparation and cleaning that we have to
do before it's ready for our analysis
so once we've done that cleansing
we can move on to
exploration
okay so this is the part of the process
that allows us to use different
analytical tools that can start helping
us answer some of
the the types of questions that i
mentioned here earlier
and if we actually want to get into some
of these higher value questions like
predictive and prescriptive
then we must start using advanced
analytical tools such as
machine learning tools
that leverage massive amounts of
computing power and massive amounts of
high quality data
to make predictions and prescribe
actions for the future
now once we've done our exploration and
perhaps our advanced
analytics
what do we do next well we need to
visualize
our insights and outcomes of our
analysis
okay
now i want to quickly touch on who does
what
in this life cycle
so in an organization you may have roles
like
a business analyst
you might have
data engineers
and then you might have
data scientists
so
business analysts
are obviously involved in formulating
the questions they have the domain
expertise they can help with the
business understanding but they're also
involved with
visualizing our insights in a way that's
useful for the business right
and then we have folks like data
engineering folks so these are the
people that can help us find the data
clean the data
and then also help with some of the
exploration
we move on to our data scientists so
these are the people that will really
help us with the exploration they'll
help us with the advanced machine
learning techniques
and they'll also assist in the
visualization
so you can see there's there's some
overlap between the roles and that's why
it's critical
to have collaboration
across
these roles
and what you also start seeing nowadays
in the marketplace is that sometimes
business analysts have to do some
machine learning they have to help out
with exploration
data scientists sometimes need to go and
find the data on their own so there's a
lot of overlap and
these different roles must collaborate
with each other
okay so i hope you can see now how the
data science life cycle can help us take
noisy data turn it into knowledge and
insights and then turn it into
meaningful action for our business
thank you
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