Welcome to the Data Science School in Nyeri, Kenya. In this school we will introduce the basic concepts of machine learning and data science. In particular we will look at tools and techniques that describe how to model. An integrated part of that is how we approach data with the computer. We are choosing to do that with the tool you see in front of you: the Jupyter Notebook.

The notebook provides us with a way of interacting with the data that allows us to give the computer instructions and explore the nature of a data set. It is *different* to normal coding, but it is related. In this course you will, through intensive practical sessions and labs, develop your understanding of the interaction between data and computers.

The first thing we are going to do is ask you to forget a bit about what you think about normal programming, or 'classical software engineering'. Classical software engineering demands a large amount of design and testing. In data analysis, testing remains very important, but the design is often evolving. The design evolves through a process known as *exploratory data analysis*. You will learn some of the techniques of exploratory data analysis in this course.

A particular difference between classical software engineering and data analysis is the way in which programs are run. Classically we spend a deal of time working with a text editor, writing code. Compilations are done on a regular basis and aspects of the code are tested (perhaps with unit tests).

Data analysis is more like coding in a debugger. In a debugger (particularly a visual debugger) you interact with the data stored in the memory of the computer to try and understand what is happening in the computer, you need to understand exactly what your bug is: you often have a fixed idea of what the program is trying to do, you are just struggling to find out why it isn't doing it.

Naturally, debugging is an important part of data analysis also, but in some sense it can be seen as its entire premise. You load in a data set into a computer that you don't understand, your entire objective is to understand the data. This is best done by interrogating the data to visualise it or summarize it, just like in a power visual debugger. However, for data science the requirements for visualization and summarization are far greater than in a regular program. When the data is well understood, the actual number of lines of your program may well be very few (particularly if you disregard commands that load in the data and commands which plot your results). If a powerful data science library is available, you may be able to summarize your code with just two or three lines, but the amount of intellectual energy that is expended on writing those three lines is far greater than in standard code.

In the first lecture we will think a little about 'how we got here' in terms of computer science. In the lecture itself, this will be done by taking a subjective perspective, that of my own 'data autobiography'.

We will be assuming that you have good background in maths. In particular we will be making use of linear algebra (matrix operations including inverse, inner products, determinant etc), probability (sum rule of probability, product rule of probability), and the calculus of differentiation (and integration!). A new concept for the course is multivariate differentiation and integration. This combines linear algebra and differential calculus. These techniques are vital in understanding probability distributions over high dimensional distributions.

In this course we will be using Python for our programming language. A prerequisite of attending this course is that you have learnt at least one programming language in the past. It is not our objective to teach you python. At Level 4 and Masters we expect our students to be able pick up a language as they go. If you have not experienced python before it may be worth your while spending some time understanding the language. There are resources available for you to do this here that are based on the standard console. An introduction to the Jupyter notebook (formerly known as the IPython notebook) is available here.

We are working in the Jupyter notebook (formerly known as the IPython notebook). It provides an environment for interacting with data in a natural way which is reproducible. We will be learning how to make use of the notebook throughout the course. The notebook allows us to combine code with descriptions, interactive visualizations, plots etc. In fact it allows us to do many of the things we need for data science. Notebooks can also be easily shared through the internet for ease of communication of ideas. The box this text is written in is a *markdown* box. Below we have a *code* box.

In [3]:

```
print "This is the Jupyter notebook"
print "It provides a platform for:"
words = ['Open', 'Data', 'Science']
from random import shuffle
for i in range(3):
shuffle(words)
print ' '.join(words)
```

Have a play with the code in the above box. Think about the following questions: what is the difference between `CTRL-enter`

and `SHIFT-enter`

in running the code? What does the command `shuffle`

do? Can you find out by typing `shuffle?`

in a code box?
Once you've had a play with the code we can load in some data using the `pandas`

library for data analysis.

There is a crisis in the movie industry, deaths are occurring on a massive scale. In every feature film the body count is tolling up. But what is the cause of all these deaths? Let's try and investigate.

For our first example of data science, we take inspiration from work by researchers at NJIT. They researchers were comparing the qualities of Python with R (my brief thoughts on the subject are available in a Google+ post here: https://plus.google.com/116220678599902155344/posts/5iKyqcrNN68). They put together a data base of results from the the "Internet Movie Database" and the Movie Body Count website which will allow us to do some preliminary investigation.

We will make use of data that has already been 'scraped' from the Movie Body Count website. Code and the data is available at a github repository. Git is a version control system and github is a website that hosts code that can be accessed through git. By sharing the code publicly through github, the authors are licensing the code publicly and allowing you to access and edit it. As well as accessing the code via github you can also download the zip file. But let's do that in python

In [5]:

```
import pods
import pandas as pd
data = pods.datasets.movie_body_count()
film_deaths = data['Y']
```

Once the data is downloaded we can unzip it into the same directory where we are running the lab class.

Once it is loaded in the data can be summarized using the `describe`

method in pandas.

In [2]:

```
film_deaths.describe()
film_deaths
```

Out[2]:

In ipython and the jupyter notebook it is possible to see a list of all possible functions and attributes by typing the name of the object followed by .

For functions we can also see the documentation about the function by following the name with a question mark. This will open a box with documentation at the bottom which can be closed with the x button.

In [4]:

```
film_deaths.describe?
```

The film deaths data is stored in an object known as a 'data frame'. Data frames come from the statistical family of programming languages based on `S`

, the most widely used of which is `R`

). The data frame gives us a convenient object for manipulating data. The describe method summarizes which columns there are in the data frame and gives us counts, means, standard deviations and percentiles for the values in those columns. To access a column directly we can write

In [5]:

```
film_deaths['Year']
#print film_deaths['Body_Count']
```

Out[5]:

This shows the number of deaths per film across the years. We can plot the data as follows.

In [6]:

```
# this ensures the plot appears in the web browser
%matplotlib inline
import pylab as plt # this imports the plotting library in python
plt.plot(film_deaths['Year'], film_deaths['Body_Count'], 'rx')
```

Out[6]:

You may be curious what the arguments we give to plt.plot are for, now is the perfect time to look at the documentation

In [7]:

```
plt.plot?
```

We immediately note that some films have a lot of deaths, which prevent us seeing the detail of the main body of films. First lets identify the films with the most deaths.

In [8]:

```
film_deaths[film_deaths['Body_Count']>200]
```

Out[8]:

Here we are using the command `film_deaths['Kill_Count']>200`

to index the films in the pandas data frame which have over 200 deaths. To sort them in order we can also use the `sort`

command. The result of this command on its own is a data series of `True`

and `False`

values. However, when it is passed to the `film_deaths`

data frame it returns a new data frame which contains only those values for which the data series is `True`

. We can also sort the result. To sort the result by the values in the `Kill_Count`

column in *descending* order we use the following command.

In [9]:

```
film_deaths[film_deaths['Body_Count']>200].sort(columns='Body_Count', ascending=False)
```

Out[9]:

We now see that the 'Lord of the Rings' is a large outlier with a very large number of kills. We can try and determine how much of an outlier by histograming the data.

In [10]:

```
film_deaths['Body_Count'].hist(bins=20) # histogram the data with 20 bins.
plt.title('Histogram of Film Kill Count')
```

Out[10]:

We could try and remove these outliers, but another approach would be plot the logarithm of the counts against the year.

In [11]:

```
plt.plot(film_deaths['Year'], film_deaths['Body_Count'], 'rx')
ax = plt.gca() # obtain a handle to the current axis
ax.set_yscale('log') # use a logarithmic death scale
# give the plot some titles and labels
plt.title('Film Deaths against Year')
plt.ylabel('deaths')
plt.xlabel('year')
```

Out[11]:

Note a few things. We are interacting with our data. In particular, we are replotting the data according to what we have learned so far. We are using the progamming language as a *scripting* language to give the computer one command or another, and then the next command we enter is dependent on the result of the previous. This is a very different paradigm to classical software engineering. In classical software engineering we normally write many lines of code (entire object classes or functions) before compiling the code and running it. Our approach is more similar to the approach we take whilst debugging. Historically, researchers interacted with data using a *console*. A command line window which allowed command entry. The notebook format we are using is slightly different. Each of the code entry boxes acts like a separate console window. We can move up and down the notebook and run each part in a different order. The *state* of the program is always as we left it after running the previous part.

We are now going to do some simple review of probabilities and use this review to explore some aspects of our data.

A probability distribution expresses uncertainty about the outcome of an event. We often encode this uncertainty in a variable. So if we are considering the outcome of an event, $Y$, to be a coin toss, then we might consider $Y=1$ to be heads and $Y=0$ to be tails. We represent the probability of a given outcome with the notation: $$ P(Y=1) = 0.5 $$ The first rule of probability is that the probability must normalize. The sum of the probability of all events must equal 1. So if the probability of heads ($Y=1$) is 0.5, then the probability of tails (the only other possible outcome) is given by $$ P(Y=0) = 1-P(Y=1) = 0.5 $$

Probabilities are often defined as the limit of the ratio between the number of positive outcomes (e.g. *heads*) given the number of trials. If the number of positive outcomes for event $y$ is denoted by $n$ and the number of trials is denoted by $N$ then this gives the ratio
$$
P(Y=y) = \lim_{N\rightarrow \infty}\frac{n_y}{N}.
$$
In practice we never get to observe an event infinite times, so rather than considering this we often use the following estimate
$$
P(Y=y) \approx \frac{n_y}{N}.
$$
Let's use this rule to compute the approximate probability that a film from the movie body count website has over 40 deaths.

In [12]:

```
deaths = (film_deaths.Body_Count>40).sum() # number of positive outcomes (in sum True counts as 1, False counts as 0)
total_films = film_deaths.Body_Count.count()
prob_death = float(deaths)/float(total_films)
print "Probability of deaths being greather than 40 is:", prob_death
```

When predicting whether a coin turns up head or tails, we might think that this event is *independent* of the year or time of day. If we include an observation such as time, then in a probability this is known as *condtioning*. We use this notation, $P(Y=y|T=t)$, to condition the outcome on a second variable (in this case time). Or, often, for a shorthand we use $P(y|t)$ to represent this distribution (the $Y=$ and $T=$ being implicit). Because we don't believe a coin toss depends on time then we might write that
$$
P(y|t) = p(y).
$$
However, we might believe that the number of deaths is dependent on the year. For this we can try estimating $P(Y>40 | T=2000)$ and compare the result, for example to $P(Y>40|2002)$ using our empirical estimate of the probability.

In [13]:

```
for year in [2000, 2002]:
deaths = (film_deaths.Body_Count[film_deaths.Year==year]>40).sum()
total_films = (film_deaths.Year==year).sum()
prob_death = float(deaths)/float(total_films)
print "Probability of deaths being greather than 40 in year", year, "is:", prob_death
```

We've now introduced conditioning and independence to the notion of probability and computed some conditional probabilities on a practical example The scatter plot of deaths vs year that we created above can be seen as a *joint* probability distribution. We represent a joint probability using the notation $P(Y=y, T=t)$ or $P(y, t)$ for short. Computing a joint probability is equivalent to answering the simultaneous questions, what's the probability that the number of deaths was over 40 and the year was 2002? Or any other question that may occur to us. Again we can easily use pandas to ask such questions.

In [14]:

```
year = 2000
deaths = (film_deaths.Body_Count[film_deaths.Year==year]>40).sum()
total_films = film_deaths.Body_Count.count() # this is total number of films
prob_death = float(deaths)/float(total_films)
print "Probability of deaths being greather than 40 and year being", year, "is:", prob_death
```

This number is the joint probability, $P(Y, T)$ which is much *smaller* than the conditional probability. The number can never be bigger than the conditional probabililty because it is computed using the *product rule*.
$$
p(Y=y, T=t) = p(Y=y|T=t)p(T=t)
$$
and $$p(T=t)$$ is a probability distribution, which is equal or less than 1, ensuring the joint distribution is typically smaller than the conditional distribution.

The product rule is a *fundamental* rule of probability, and you must remember it! It gives the relationship between the two questions: 1) What's the probability that a film was made in 2002 and has over 40 deaths? and 2) What's the probability that a film has over 40 deaths given that it was made in 2002?

In our shorter notation we can write the product rule as $$ p(y, t) = p(y|t)p(t) $$ We can see the relation working in practice for our data above by computing the different values for $t=2000$.

In [15]:

```
p_t = float((film_deaths.Year==2002).sum())/float(film_deaths.Body_Count.count())
p_y_given_t = float((film_deaths.Body_Count[film_deaths.Year==2002]>40).sum())/float((film_deaths.Year==2002).sum())
p_y_and_t = float((film_deaths.Body_Count[film_deaths.Year==2002]>40).sum())/float(film_deaths.Body_Count.count())
print "P(t) is", p_t
print "P(y|t) is", p_y_given_t
print "P(y,t) is", p_y_and_t
```

The other *fundamental rule* of probability is the *sum rule* this tells us how to get a *marginal* distribution from the joint distribution. Simply put it says that we need to sum across the value we'd like to remove.
$$
P(Y=y) = \sum_{t} P(Y=y, T=t)
$$
Or in our shortened notation
$$
P(y) = \sum_{t} P(y, t)
$$

Bayes rule is a very simple rule, it's hardly worth the name of a rule at all. It follows directly from the product rule of probability. Because $P(y, t) = P(y|t)P(t)$ and by symmetry $P(y,t)=P(t,y)=P(t|y)P(y)$ then by equating these two equations and dividing through by $P(y)$ we have
$$
P(t|y) = \frac{P(y|t)P(t)}{P(y)}
$$
which is known as Bayes' rule (or Bayes's rule, it depends how you choose to pronounce it). It's not difficult to derive, and its importance is more to do with the semantic operation that it enables. Each of these probability distributions represents the answer to a question we have about the world. Bayes rule (via the product rule) tells us how to *invert* the probability.

If you want to explore more of the things you can do with movies and python you might be interested in the `imdbpy`

python library.

You can try installing it using `easy_install`

as follows.

In [ ]:

```
!easy_install -U IMDbPY
```

If this doesn't work on your machine, try following instructions on (http://imdbpy.sourceforge.net/)

Once you've installed `imdbpy`

you can test it works with the following script, which should list movies with the word 'python' in their title. To run the code in the following box, simply click the box and press `SHIFT-enter`

or `CTRL-enter`

. Then you can try running the code below.

In [ ]:

```
from imdb import IMDb
ia = IMDb()
for movie in ia.search_movie('python'):
print movie
```

In [ ]:

```
from IPython.display import YouTubeVideo
YouTubeVideo('GX8VLYUYScM')
```

In [ ]:

```
```