what is data science explained in five minutes hi this is 365's new series of videos in which
we will try to explain a given topic very quickly in fact we'll use a timer to keep our speed up and
try to fit the video in five minutes so which should be the first topic that we want to cover
given what we're called 365 data science we'll start with what is data science hope you're ready
let's start the clock a few years ago a shrewd journalist came up with a headline
that data scientist is the sexiest job of the 21st century according to glassdoor.com
data scientist continues to be among the most desired career paths today ironically the term
data science which was first introduced in the 1980s remains confusing for many people even now
the large number of individuals particularly the ones outside the field struggle to understand how
data scientists help companies around the world and why their services are in such high demand
there are a few key reasons for this first of all data science is an interdisciplinary field
its true foundations are in statistics mathematics computer science and business so it is difficult
to draw a clear separation line indicating what lies in the realm of data science and what doesn't
on a few occasions people have claimed that data science and statistics are the same thing
however we'd argue that data science is broader as it grows out of statistics to include topics that
are apt to deal with digital and big data another frequent misconception is that data science and ai
are interchangeable terms ai designates a model a machine if you will which tries to mimic human
behavior and produce human-like decisions it sometimes leverages data science methods such as
deep learning but is not confined solely to them for example symbolic reasoning is a type of ai
that does not necessarily have to be characterized as data science machine and deep learning are
important domains within data science and so is statistics so data science is in fact a very broad
discipline which continues to expand with new data related needs let's briefly pause the chronometer
before we continue in the second part of the video we will talk about the key aspects of the data
scientist profession but before we do that please like this video and subscribe to our channel
so that youtube's ml algorithms won't keep you away from our videos having said that
let's continue with the video and consider a few important aspects of data scientists work
type of data used one activities performed on the job two time allocation three key skills four
frequently used data science methods what type of data do data scientists use in their analysis
the answer is both structured and unstructured data structured data comes in the form of excel
spreadsheets and csv files to put it briefly it can be organized in rows and columns examples of
such data are client tables and spreadsheets with transaction information unstructured data on the
other hand is everything else images video and audio files all types of other data we
can have reportedly unstructured data represents more than eighty percent of all enterprise data
so every data scientist worth or salt should be able to take advantage of it which are the types
of activities a data scientist carries out in their job it depends mostly on company size in
smaller firms a data scientist might be the only data person and therefore will have an end-to-end
ownership of data analysis processes in larger enterprises there will be a higher degree of
specialization as the company is able to afford more resources the main activities that a data
scientist can perform but not necessarily does in a business environment are data collection and
storage data pre-processing also referred to as data cleaning data organization data visualization
and the creation of kpi dashboards experimentation and a b testing statistical inference
building ml models evaluating ml models deploying ml models and monitoring how ml models perform
considering these activities which are the duties that take the most time ask anyone
in the industry and you will hear the same answer they'll tell you they spend 80 of the
time in an effort to make a hypothesis find the necessary data and clean it only 20 of the useful
hours are dedicated to performing an analysis and interpreting the findings and what about the key
abilities of data scientists needs of course the profession is multifaceted but here's our top five
very solid knowledge of statistics and mathematics computer science skills business understanding
analytical abilities and communication skills some of the most frequently used data science
techniques are statistical inference linear regression logistic regression machine learning
techniques such as decision trees support vector machines clustering dimensionality reduction
deep learning methods supervised unsupervised and reinforcement learning however we should remember
that regardless of the method a data scientist end goal would be to make a meaningful contribution
to the business to create value for the company we can distinguish among two main ways do that
first help a company make better decisions when it comes to their customers and employees netflix and
youtube are able to recommend you the content you want to see next your telecom operator knows when
you are more likely to switch to a competitor and banks use data science methods for fraud detection
the other major way in which data science can add value to a business is through the optimization
of existing operations for example a logistics company might benefit from an improved delivery
schedule while a manufacturing firm could use predictive analytics to foresee maintenance needs
without risking production stoppages and that's it data science explained in almost five minutes
we tried to win the race against the clock but more importantly we hope you learned
something new let us know how we did in the comments and what you'd like to see next