Data Science, the Good, the Bad, and the… Future

What is data science?

How often do you think you’re touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I’ll explain what data science means to me.

“Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information.”

That’s a mouthful. If we unpack that, all data science really means is to answer questions by using math and science to go through data that’s too much for our brains to process.

Data Science covers…

• Machine learning
• Data visualization
• Predictive analysis
• Voice assistants

… and all the buzzwords we hear today, like artificial intelligence, deep learning, etc.

To finish my thought on data science being used to find this article, I’ll ask you to think of the steps you used to get here. For the sake of this explanation, let’s assume that most of you were online looking at pictures of kittens and puppies when you suddenly came across a fancy word related to data science and wanted to know what it was all about. You turned to Google hoping to find the meaning of it all, and you typed “What is *fill in your data science related buzzword*.”

You would have noticed that Google was kind enough to offer suggestions to refine your search terms – that’s predictive text generation. Once the search results came up, you would have noticed a box on the right that summarizes your search results – that’s Google’s knowledge graph. Using insights from SEO (Search Engine Optimization) I’m able to make sure my article reaches you easily, which is a good data science use case in and of itself. All of these are tiny ways that data science is involved in the things we do every day.

To be clear, going forward I’m going to use data science as an umbrella term that covers artificial intelligence, deep learning and anything else you might hear that’s relevant to data and science.

Positives: astrophysics, biology, and sports

Data science made a huge positive impact on the way technology influences our lives. Some of these impacts have been nice and some have been otherwise. *looks at Facebook* But, technology can’t inherently be good or bad, technology is… technology. It’s the way we use it that has good or bad outcomes.

We recently had a breakthrough in astrophysics with the first ever picture of a black hole. This helps physicists confirm more than a century of purely theoretical work around black holes and the theory of relativity.

To capture this image, scientists used a telescope as big as the earth (Event Horizon Telescope or EHT) by combining data from an array of eight ground-based radio telescopes and making sense of it all to construct an image. Analyzing data and then visualizing that data – sounds like some data science right here.

A cool side note on this point: a standard Python library of functions for EHT Imaging was developed by Andrew Chael from Harvard to simulate and manipulate VLBI (Very-long-baseline interferometry) data helping the process of creating the black hole image.

Olivier Elemento at Cornell uses Big Data Analytics to help identity mutations in genomes that result in tumor cells spreading so that they can be killed earlier – this is a huge positive impact data science has on human life. You can read more about his incredible research here.

Python is used by researchers in his lab while testing statistical and machine learning models. Keras, NumPy, Scipy, and Scikit-learn are some top notch Python libraries for this.

If you’re a fan of the English Premier League, you’ll appreciate the example of Leicester City winning the title in the 2015-2016 season.

At the start of the season, bookmakers had the likelihood Leicester City winning the EPL at 10 times less than the odds of finding the Loch Ness monster. For a more detailed attempt at describing the significance of this story, read this.

Everyone wanted to know how Leicester was able to do this, and it turns out that data science played a big part! Thanks to their investment into analytics and technology, the club was able to measure players’ fitness levels and body condition while they were training to help prevent injuries, all while assessing best tactics to use in a game based on the players’ energy levels.

All training sessions had plans backed by real data about the players, and as a result Leicester City suffered the least amount of player injuries of all clubs that season.

Many top teams use data analytics to help with player performance, scouting talent, and understanding how to plan for certain opponents.

Here’s an example of Python being used to help with some football analysis. I certainly wish Chelsea F.C. would use some of these techniques to improve their woeful form and make my life as a fan better. You don’t need analytics to see that Kante is in the wrong position, and Jorginho shouldn’t be in that team and… Okay I’m digressing – back to the topic now!

Now that we’ve covered some of the amazing things data science has uncovered, I’m going to touch on some of the negatives as well – it’s important to critically think about technology and how it impacts us.

The amount that technology impacts our lives will undeniably increase with time, and we shouldn’t limit our understanding without being aware of the positive and negative implications it can have.

Some of the concerns I have around this ecosystem are data privacy (I’m sure we all have many examples that come to mind), biases in predictions and classifications, and the impact of personalization and advertising on society.

Negatives: gender bias and more

This paper published in NIPS talks about how to counter gender biases in word embeddings used frequently in data science.

For those who aren’t familiar with the term, word embeddings are a clever way of representing words so that neural networks and other computer algorithms can process them.

The data used to create Word2Vec (a model for word embeddings created by Google) has resulted in gender biases that show close relations between “men” and words like “computer scientist”, “architect”, “captain”, etc. while showing “women” to be closely related to “homemaker”, “nanny”, “nurse”, etc.

Here’s the Python code used by the researchers who published this paper. Python’s ease of use makes it a good choice for quickly going from idea to implementation.

It isn’t always easy to preempt biases like these from influencing our models. We may not even be aware that such biases exist in the data we collect.

It is imperative that an equal focus is placed on curating, verifying, cleaning, and to some extent de-biasing data.

I will concede that it isn’t always feasible to make all our datasets fair and unbiased. Lucky for us, there is some good research published that can help us understand our neural networks and other algorithms to the extent that we can uncover these latent biases.

When it comes to data science, always remember –

“Garbage in, garbage out.”

The data we train our algorithms with influences the results they produce. The results they produce are often seen by us and can have a lasting influence.

We must be aware of the impact social media and content suggestions have on us. Today, we’re entering a loop where we consume content that reinforces our ideas and puts people in information silos.

Research projects that fight disinformation and help people break out of the cycle of reinforcement are critical to our future. If you were trying to come up with a solution to this fake news problem, what would we need to do?

We would first need to come up with an accurate estimate of what constitutes “fake” news. This means comparing an article with reputable news sources, tracing the origins of a story, and verifying that the article’s publisher is a credible source.

You’d need to build models that tag information that hasn’t been corroborated by other sources. To do this accurately, one would need a ton of not “fake” news to train the model on. Once the model knows how to identify if something is true (to a tolerable degree of confidence), then the model can begin to flag news that’s “fake.”

Crowd sourced truth is also a great way to tackle this problem, letting the wisdom of the crowd determine that “truth” is.

Blockchain technology fits in well here by allowing data to flow from people all over the world and arrive at consensus on some shared truth.

Python is the fabric that allows all these technologies and concepts to come together and build creative solutions.

from sklearn import datasets, linear_model
import matplotlib.pyplot as plt

iris = datasets.load_iris()

# Data and features are both numpy arrays
data = iris.data
features = iris.feature_names

# Create the regression model
regression = linear_model.LinearRegression()

# Reshape the Numpy arrays so that they are columnar
x_data = data[:, 2].reshape(-1, 1)
y_data = data[:, 3].reshape(-1, 1)

# Train the regression model to fit the data from iris (comparing the petal width)
regression.fit(x_data, y_data)


# Display chart
plt.plot(x_data, regression.predict(x_data), color='black', linewidth=3)
plt.scatter(x_data, y_data)
plt.show()