So now that we've talked a bit about machine learning, it's time to tackle a closely related topic and 1
also a big part of this course - 2
data science. Data science is another one of those terms that gets thrown around a lot, 3
but what does it actually mean? 4
At its heart, data science is about turning data into value. 5
But let's take a look at how it came to be. In the 1980s, 6
IBM came out with the first relational database storing things like customer details or company payroll 7
data. 8
And soon after, we started thinking about what useful things we can mine from the data and the term 9
data mining was coined in a paper called "From Data Mining to Knowledge Discovery in Databases". Published 10
in 1996, 11
it defines data mining as the application of specific algorithms for extracting patterns from data. Let's 12
think about that - extracting patterns from data. 13
We're going to see a lot more of that in the course. 14
Up until now a lot of those patterns were being extracted with plain old statistics, but we're now in 15
the 1990s when computer science was progressing at crazy speeds. 16
So a lot of people were looking into levelling up data mining with computer science and they used the 17
term data science to refer to this new field. As the dot-com era exploded, 18
there were more and more data being generated. And these days every click, every scroll, every time you 19
open an app, 20
huge amounts of data are being generated and collected. 21
Why? Business intelligence. 22
If you were a retailer, wouldn't it be cool if you could predict when somebody was pregnant and then 23
market to them 24
the things that you sell to new parents? Well, this is exactly what Target did. 25
They collected data on people's purchases and then based on what they bought, they could use data science 26
to predict whether someone was pregnant. Or maybe you've seen the movie Moneyball - the story of how a 27
poor baseball team used data science to pick undervalued players, which changed their fate from a losing 28
team to win 20 consecutive games, the longest in American League's history. 29
How did they do it? 30
Data science. Data science is a broad term. So broad that the Journal of Data Science defines it as almost 31
everything that has something to do with data. 32
So most people think of data science as taking huge amounts of data or tapping into big data and performing 33
machine learning or using A.I. to get important insights. 34
But data science is actually much more than that. 35
Just as humans need food and water before they can start thinking about shelter, or what is the meaning 36
of life - in the data science hierarchy of needs, a company first needs to be able to collect and store 37
their data before they can even start thinking about getting meaning out of it. 38
And in a large company that's usually done by data engineers. 39
The role of the data scientist is to then take this data, clean it, explore it, visualize it, and then apply 40
intelligent algorithms to start answering questions with the data. In the upcoming modules we'll be embarking 41
on projects that traverse all the layers that are the responsibility of the data scientist and the machine 42
learning expert. In every module we'll try to solve a real world problem by first understanding how to 43
clean, segment and visualize raw data, 44
we can then learn to apply shallow and deep learning algorithms to extract meaning from the data. 45
Algorithms like linear regression, Bayes classification and deep learning with neural networks. 46
Also, we're going to learn how to use tools like TensorFlow, Python, Keras, pandas and NumPy to solve 47
our data problems - problems like movie revenue prediction, spam classification and image recognition. 48
There's a lot packed into this course. 49
So what are you waiting for? 50
Let's get started.