Data science and data analytics. Are they the same thing? Well, you may have seen these terms
used interchangeably, but if I'm mining data from a large dataset, am I performing data science or
am I performing data analytics? Or what if I'm trying to create a prediction of when my store
will sell out of our current inventory of cantaloupes? Well, is that analytics or is
that science? It's worthwhile to understand the difference to better comprehend what these two
fields can do, and also, if you're considering a career in either field. After all, these are two
different jobs. Somebody who works in the field of data science is known as a data scientist.
For data analytics, that role is called a data analyst. Now, this is kind of a trick question
because we can classify everything-- data mining, data forecasting and all the rest of
it --as simply data science. And that's because data science is the overarching umbrella term
that covers tasks related to finding patterns in large datasets, training machine learning models,
and deploying AI applications. Data analytics, it could be argued, is one task that resides under
the data science umbrella. It's a specialization of data science, and it focuses on querying,
interpreting and visualizing datasets. Data science is iterative, meaning data scientists form
hypotheses and experiments to see if a desired outcome can be achieved using available data.
And that is a process that is known as the data science lifecycle, which usually follows seven
phases. So first is to identify a problem or an opportunity. Then the next phase is data mining,
which is to extract data relevant to that problem or opportunity from large datasets. Now that data
will likely consist of a bunch of redundancies and errors, which is fixed in the next stage,
data cleaning. And then at that point, we move on to data exploration analysis to try to make sense
of that data. We'll then apply feature engineering using domain knowledge to extract details from the
data. And predictive modeling comes next to use the data to predict or forecast future outcomes
and behaviors. And then finally, we have data visualization that represents the data points with
graphical tools such as charts and animations. And so the lifecycle repeats. Now, the role of a
data scientist is an in-demand profession right now. If that's something you're interested in,
you'll want to develop deep skills in machine learning and AI. It's helpful to be able to
write code in languages such as Python--also in R-- it's another popular language for data science. And
you should have experience working with big data platforms. So perhaps Hadoop or Apache Spark. And
it's also very helpful to have database knowledge and SQL. So that's data science. But what about
its specialization, data analytics? Well, the job of a data analyst is to conceptualize a data set
as it currently exists. So we have some data here and we need to do something with it. And
we need to be able to make decisions based on this data. How do we conceptualize it? Well,
four ways. One is through predictive analytics, which helps to identify trends, correlations and
causation within datasets like forecasting when those cantaloupes would have all flown off the
shelves. Or, in health care, to forecast regions which will experience a rise in flu cases. There's
prescriptive analytics, and that predicts likely outcomes and makes decision recommendations like
predicting when a tire will wear out and need to be replaced. There's diagnostic analytics
that helps pinpoint the reason an event occurred. Manufacturers can analyze a failed component on an
assembly line and figure out the reason behind its failure. And then there is descriptive analytics
which evaluates the qualities and quantities of a data set. A content streaming provider might
use descriptive analytics to understand how many subscribers it's lost or how many it's gained over
a given period of time and what content is being watched. And while a data scientist is a clearly
defined and specialized role, virtually any stakeholder can be a data analyst. For example,
business analysts can use BI dashboards to conduct business analytics and visualize KPIs. But many
organizations do employ professional, dedicated data analysts, responsible for data wrangling
and interpreting findings like why a company's marketing campaign didn't meet expectations. If
you want to be a data analyst, it helps to have both analytical and programming skills. So this
includes familiarity with databases. Also, you'll need to know about statistical analysis. And also
data visualization is another important skill. So data analytics is often more focused on using
statistical tools and techniques to interpret existing data and offer actionable insights.
It's usually less concerned with creating new algorithms or models. Data science,
on the other hand, has a broader scope that can involve complex machine learning algorithms, often
created from scratch. Data science focuses on phases from data collection to predictive
modeling. Data analysis, on the other hand, is more about answering specific questions with
that data. And if you've done both your data science and data analytics right,
you'll always be able to keep cantaloupes and just about everything else in stock.