Data science

Data science is the process of extracting useful information from data. As a discipline, it remains somewhat ill-defined, with nearly as many definitions as there are experts. Rather than add yet another definition, I will follow Drew Conway's description (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram). He describes data science as the culmination of three orthogonal sets of skills:

• Data scientists must have hacking skills. Data is stored and transmitted through computers. Computers, programming languages, and libraries are the hammers and chisels of data scientists; they must wield them with confidence and accuracy to sculpt the data as they please. This is where Scala comes in: it's a powerful tool to have in your programming toolkit.
• Data scientists must have a sound understanding of statistics and numerical algorithms. Good data scientists will understand how machine learning algorithms function and how to interpret results. They will not be fooled by misleading metrics, deceptive statistics, or misinterpreted causal links.
• A good data scientist must have a sound understanding of the problem domain. The data science process involves building and discovering knowledge about the problem domain in a scientifically rigorous manner. The data scientist must, therefore, ask the right questions, be aware of previous results, and understand how the data science effort fits in the wider business or research context.

Drew Conway summarizes this elegantly with a Venn diagram showing data science at the intersection of hacking skills, maths and statistics knowledge, and substantive expertise:

It is, of course, rare for people to be experts in more than one of these areas. Data scientists often work in cross-functional teams, with different members providing the expertise for different areas. To function effectively, every member of the team must nevertheless have a general working knowledge of all three areas.

To give a more concrete overview of the workflow in a data science project, let's imagine that we are trying to write an application that analyzes the public perception of a political campaign. This is what the data science pipeline might look like:

• Obtaining data: This might involve extracting information from text files, polling a sensor network or querying a web API. We could, for instance, query the Twitter API to obtain lists of tweets with the relevant hashtags.
• Data ingestion: Data often comes from many different sources and might be unstructured or semi-structured. Data ingestion involves moving data from the data source, processing it to extract structured information, and storing this information in a database. For tweets, for instance, we might extract the username, the names of other users mentioned in the tweet, the hashtags, text of the tweet, and whether the tweet contains certain keywords.
• Exploring data: We often have a clear idea of what information we want to extract from the data but very little idea how. For instance, let's imagine that we have ingested thousands of tweets containing hashtags relevant to our political campaign. There is no clear path to go from our database of tweets to the end goal: insight into the overall public perception of our campaign. Data exploration involves mapping out how we are going to get there. This step will often uncover new questions or sources of data, which requires going back to the first step of the pipeline. For our tweet database, we might, for instance, decide that we need to have a human manually label a thousand or more tweets as expressing "positive" or "negative" sentiments toward the political campaign. We could then use these tweets as a training set to construct a model.
• Feature building: A machine learning algorithm is only as good as the features that enter it. A significant fraction of a data scientist's time involves transforming and combining existing features to create new features more closely related to the problem that we are trying to solve. For instance, we might construct a new feature corresponding to the number of "positive" sounding words or pairs of words in a tweet.
• Model construction and training: Having built the features that enter the model, the data scientist can now train machine learning algorithms on their datasets. This will often involve trying different algorithms and optimizing model hyperparameters. We might, for instance, settle on using a random forest algorithm to decide whether a tweet is "positive" or "negative" about the campaign. Constructing the model involves choosing the right number of trees and how to calculate impurity measures. A sound understanding of statistics and the problem domain will help inform these decisions.
• Model extrapolation and prediction: The data scientists can now use their new model to try and infer information about previously unseen data points. They might pass a new tweet through their model to ascertain whether it speaks positively or negatively of the political campaign.
• Distillation of intelligence and insight from the model: The data scientists combine the outcome of the data analysis process with knowledge of the business domain to inform business decisions. They might discover that specific messages resonate better with the target audience, or with specific segments of the target audience, leading to more accurate targeting. A key part of informing stakeholders involves data visualization and presentation: data scientists create graphs, visualizations, and reports to help make the insights derived clear and compelling.

This is far from a linear pipeline. Often, insights gained at one stage will require the data scientists to backtrack to a previous stage of the pipeline. Indeed, the generation of business insights from raw data is normally an iterative process: the data scientists might do a rapid first pass to verify the premise of the problem and then gradually refine the approach by adding new data sources or new features or trying new machine learning algorithms.

In this book, you will learn how to deal with each step of the pipeline in Scala, leveraging existing libraries to build robust applications.