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 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, but then we also cover
the area of mathematics, and then what I think is the most important is. Business
expertize. 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 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
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 a 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 lifecycle. 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 that having the business expertize and
the domain expertize 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 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 reality of the marketplace is that we when we find data, it's probably not in the best format
that we need it in. And it probably has some some issues with it. Right. It might have rows
that have missing values. It might have duplicates in it. So there are 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 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 expertize. 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. Next, 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 lifecycle can help us take noisy data,
turn it into knowledge and insights, and then turn it into meaningful action for our business.
Thank you. If you have questions, please drop us a line below.
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