Welcome to the first module! In this module we'll learn about how to use data science to solve a practical 1
problem and we will also introduce our first machine learning algorithm - namely linear regression. Regression 2
is the workhorse of data science, business analytics and research. 3
You'll find people using regression whenever someone is looking to model a phenomenon or make a prediction 4
or discover the hidden relationships among things. 5
Also, I want to show you how to solve problems using Data Science. 6
So what kind of problem are you going to look at? 7
Well, imagine that you're a film producer. You provide money to make movies and when the movies make money, 8
you make money. 9
And when the movies lose money, you lose money. 10
But today you discover a problem. 11
You found out that you just put a whole bunch of money into a movie called "Zombies Rise Again 12
Part 5". 13
And if that doesn't sound like a terrible title for a film, then I don't know what does. Worse yet, you've 14
staked your reputation on this film, 15
you've mortgaged your house, you've sold your car, 16
you've sold your grandmother's silverware to fund this film. 17
So, is your money gone? 18
Will you go bankrupt? 19
Well, to answer these questions you would have to be able to predict the future. 20
And this is where data science will come to the rescue. 21
Let's have a think about how a data scientist would approach this problem. Well first, a data scientist 22
would carefully formulate the question that they're looking to answer. Why? 23
Well, a clear and well formulated question will determine the research and it will also affect the kind 24
of data that you will go out and gather. In fact, 25
step 2 is gathering the data that will help us answer the question. But real world data is also messy. 26
So we have to clean the data. 27
We have to look out for missing data or incomplete data. 28
We have to look out for errors and even bound formatting. 29
In fact, we have to explore the data that we've gathered. 30
And often this means visualizing the data so that we can better understand what it is that we're working 31
with. A graph or a chart is much more helpful than a table of numbers. 32
And after we've done all that, 33
the next step is actually training our algorithm using our computer to identify patterns in the data. 34
In our case, that algorithm will be our linear regression. 35
And finally, we have to evaluate the results. 36
How did our algorithm do? 37
Did it answer our question? How accurate was our algorithm 38
in answering our question? The process that I've outlined here is the process data scientists use to 39
solve problems. 40
This is their workflow for understanding and making sense of the world. 41
So let's tackle that first step. 42
Let's formulate our question. 43
So here's our first shot at this. 44
How much money will our movie make? Now, 45
this is actually not a very good question. 46
The problem is is that this question is too vague. 47
For starters, what do we actually mean with the word money? 48
Can we be more specific? 49
How about this? 50
How about "How much revenue will our movie make?"? Now, 51
this is already a much better question, because revenue is something that means something very specific 52
in the business world. 53
It's something that accountants actually measure and track and that means that we can actually find 54
data on movie revenue. 55
But, what does movie revenue actually depend on? 56
Let's have a think about this. 57
Maybe it depends on the actors. Do movies starring Scarlett Johansson do particularly well? Or maybe it depends 58
on the script? Or maybe what actually matters is the film's overall budget? 59
Because after all, you have to pay good actors and you have to pay for special effects and marketing 60
costs a lot of money too. 61
So maybe the overall budget would be the best thing to look at. 62
So if that's the case then we have a hypothesis - the movie budget could indeed be the thing to investigate. 63
After all, there are many famous examples of big budget films that did exceptionally well at the box 64
office. 65
Movies like Avatar, Titanic and The Avengers cost an absolute fortune, but they were enormously successful. 66
But can the success of these movies tell us something about the success of our movie? 67
If we knew how much movies like Avatar or The Avengers spent and how much they made, can we predict how 68
much we'll make 69
based on how much we're spending? So let's go back to our original question and let's make it a little 70
bit more specific. 71
"Can we use movie budgets to predict movie revenue?". Now, 72
that's already a much better question. 73
Why? 74
Well, it's testable, and by testable, I mean that we can actually check if there is a relationship between 75
movie budgets and movie revenue. 76
This is something that we can measure using data and linear regression. 77
And as data scientists we can actually look at this question and identify exactly what it is that we're 78
trying to predict, namely the movie revenue. A data scientist would call this the dependent variable and 79
in machine learning this would be called the target. 80
Also, we can identify what it is that we're using to make the prediction, namely the movie budgets. A data 81
scientist would call the budgets the independent variable and in machine learning this would be called 82
a feature. 83
So now that we've formulated our question we can move on to Step Two. 84
We can go out there and gather some data. And we'll do just that in the next lesson. 85
I'll see you there.