Data… a collection of symbols representing
the quality or quantity of a physical phenomenon.
Humans have likely been using data for as
long as we’ve been counting on our fingers.
We have evidence of humans carving notches
into wood, bone, and stone to count days,
lunar cycles, and animals for at least the
past forty-thousand years.
A few millennia ago, the Sumerians, Egyptians,
and Chinese were recording written counts
of items, animals, people, and astronomical
They recorded these data using clay tablets,
papyrus, and parchment, using early writing
systems like cuneiform, hieroglyphics, and
A few centuries ago, data were collected by
governments for census and taxation, or by
businesses for accounting, inventory, and
Data at this point in history were recorded
largely using quill pens in paper ledgers.
In the 1800s, mechanical computers radically
sped up data processing and ushered in a new
area of data analysis.
For example, the 1880 US census took over
7 years to process and analyze without a computer.
However, the 1890 US census, took only 18
months thanks to Herman Hollerith’s punch-card-based
In the 1900s, electrical computers dramatically
increased both data storage and processing
By the mid-1900s, digital computers allowed
us to store and analyze data as bits of information
encoded as ones and zeros.
In the 1980s, the emergence of relational
databases allowed us to efficiently store
and process transactional data.
We also saw the emergence of programming languages
like structured query language which allow
us to rapidly query and analyze data.
In the 1990s, data warehouses, data marts,
and data cubes were used to store and analyze
ever-larger growing sets of data.
We also saw the emergence of data mining to
allow us to discover patterns of interest
in large data sets.
In the 2000s, Big Data platforms emerged to
handle vary large data sets by spreading data
and processing across several computers in
We also saw the rise of machine learning — training
computer algorithms on large sets of data
to classify new data and make predictions.
In the 2010s, cloud-scale distributed-computing
platforms emerged to handle storing and processing
of data across thousands of computers in a
This decade also ushered in the era of deep
learning — training deep neural networks
on very large data sets to classify and predict
much more complex patterns of data.
As we move into 2020s, the explosion of data
from the internet of things is leading to
a need for new methods to store and process
In addition, the demand for modern data analysis
has made data science one of the most in-demand
professions of the 21st century.
In the next decade and beyond, the field of
data science will continue to grow and will
likely evolve into data-driven artificial
A new era of data that will almost certainly
change our world
in more ways than we could possibly imagine.