Why Python?

Before we move on, it is important to formalize many of the requisite coding skills in Python.

In Python, we have variables that are placeholders for objects. We will focus on just a few types of basic objects at first, as shown in the following table:

We will also have to understand some basic logistical operators. For these operators, keep the Boolean datatype in mind. Every operator will evaluate to either True or False. Let's take a look at the following operators:

When coding in Python, I will use a pound sign (#) to create a "comment," which will not be processed as code, but is merely there to communicate with the reader. Anything to the right of a # sign is a comment on the code being executed.

In Python, we use spaces/tabs to denote operations that belong to other lines of code.

Note that the following list variable, my_list, can hold multiple types of objects. This one has an int, a float, a boolean, and string inputs (in that order):

my_list = [1, 5.7, True, "apples"] 
 
len(my_list) == 4  # 4 objects in the list 
 
my_list[0] == 1    # the first object 
 
 
my_list[1] == 5.7    # the second object 

In the preceding code, I used the len command to get the length of the list (which was 4). Also, note the zero-indexing of Python. Most computer languages start counting at zero instead of one. So if I want the first element, I call index 0, and if I want the 95th element, I call index 94.

Here is some more Python code. In this example, I will be parsing some tweets about stock prices (one of the important case studies in this book will be trying to predict market movements based on popular sentiment regarding stocks on social media):

tweet = "RT @j_o_n_dnger: $TWTR now top holding for Andor, unseating $AAPL" 
 
words_in_tweet = tweet.split(' ') # list of words in tweet 
 
for word in words_in_tweet:             # for each word in list 
  if "$" in word:                       # if word has a "cashtag"  
  print("THIS TWEET IS ABOUT", word)  # alert the user 

I will point out a few things about this code snippet line by line, as follows:

The output of this code will be as follows:

THIS TWEET IS ABOUT $TWTR
THIS TWEET IS ABOUT $AAPL

We get this output as these are the only words in the tweet that use the cashtag. Whenever I use Python in this book, I will ensure that I am as explicit as possible about what I am doing in each line of code.

As I mentioned earlier, domain knowledge focuses mainly on having knowledge of the particular topic you are working on. For example, if you are a financial analyst working on stock market data, you have a lot of domain knowledge. If you are a journalist looking at worldwide adoption rates, you might benefit from consulting an expert in the field. This book will attempt to show examples from several problem domains, including medicine, marketing, finance, and even UFO sightings!

Does this mean that if you're not a doctor, you can't work with medical data? Of course not! Great data scientists can apply their skills to any area, even if they aren't fluent in it. Data scientists can adapt to the field and contribute meaningfully when their analysis is complete.

A big part of domain knowledge is presentation. Depending on your audience, it can matter greatly on how you present your findings. Your results are only as good as your vehicle of communication. You can predict the movement of the market with 99.99% accuracy, but if your program is impossible to execute, your results will go unused. Likewise, if your vehicle is inappropriate for the field, your results will go equally unused.