PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
April 2018, a PyData conference was held in London, with three days of super interesting sessions and hackathons. While I couldn’t attend in person, I very much enjoy reviewing the sessions at home as all are shared open access on YouTube channel PyDataTV!
In the following section, I will outline some of my favorites as I progress through the channel:
Winning with simple, even linear, models:
One talk that really resonated with me is Vincent Warmerdam‘s talk on “Winning with Simple, even Linear, Models“. Working at GoDataDriven, a data science consultancy firm in the Netherlands, Vincent is quite familiar with deploying deep learning models, but is also midly annoyed by all the hype surrounding deep learning and neural networks. Particularly when less complex models perform equally well or only slightly less. One of his quote’s nicely sums it up:
“Tensorflow is a cool tool, but it’s even cooler when you don’t need it!”
— Vincent Warmerdam, PyData 2018
In only 40 minutes, Vincent goes to show the finesse of much simpler (linear) models in all different kinds of production settings. Among others, Vincent shows:
how to solve the XOR problem with linear models
how to win at timeseries with radial basis features
how to use weighted regression to deal with historical overfitting
how deep learning models introduce a new theme of horror in production
how to create streaming models using passive aggressive updating
how to build a real-time video game ranking system using mere histograms
how to create a well performing recommender with two SQL tables
how to rock at data science and machine learning using Python, R, and even Stan
The 2018 annual Society for Industrial and Organizational Psychology (SIOP) conference featured its first-ever machine learning competition. Teams competed for several months in predicting the enployee turnover (or churn) in a large US company. A more complete introduction as presented at the conference can be found here. All submissions had to be open source and the winning submissions have been posted in this GitHub repository. The winning teams consist of analysts working at WalMart, DDI, and HumRRO. They mostly built ensemble models, in Python and/or R, combining algorithms such as (light) gradient boosted trees, neural networks, and random forest analysis.
Tilburg University has set up a masterclass Predictive HR Analytics. In 3 days, the Professional Learning program will teach you all you need to know to implement predictive analytics and take HR to the next level. More information can be found here.
What makes this program unique?
The masterclass Predictive HR Analytics goes beyond HR analytics and focuses on transformational people predictions. You learn how to embed predictive HR analytics into your HR Strategy and how to use your findings to convince others.
The masterclass is developed at the prestigious Human Resources department at Tilburg University, which has obtained international recognition with its high-quality academic research in the HRM field.
The mix of professors in conjunction with leading HR professionals leads to a strong academic program with a practical approach.
Your peer participants will make sure that the class opens up a high-quality network of HR specialists. The diversity of leading companies from different sectors in the classroom creates new insights for all the participants.
The program is like a 3-day pressure cooker. By combining online and offline components, we can create more in-depth discussions in the classroom.
You will experience a high impact on your daily practice, since the program is focused on direct implementation.
This course is ideal for anyone in HR seeking to become more adept in using quantitative data for decision making. Typical participants are (future) HR analysts, HR managers, HR business partners, HR consultants and (financial) business analysts with a strong link on people resources. Participants are from various sectors, such as financial services, healthcare institutions, government agencies and business services.
Things can get confusing quite quickly if you’re a layman. People boast about boosting while deep, brain-like networks are used to play child’s games. Data guru’s speak of mighty, though random woodlands and the media simultaneously praise and criticize IBM Watson. To create even more confusion, consultancy firms introduce a new type of analytics every year, each one more valuable than its predecessor. I am not even kidding, I counted seveneightnineten eleven types: descriptive, diagnostic, exploratory, inferential, strategic, causal, enterprise, advanced, predictive, prescriptive, adaptive, and cognitive analytics, roughly in that order of complexity.
The resulting confusion I experience firsthand in my work. In my workshops, people would ask questions like “How can I use data mining to make our dashboards to morepredictive?” or“How can I build neural networks to understand our customer needs?”. Similarly, I’ve heard managers ask for more “cognitive solutions” or “one of those fancy neural networks“. However, things can get pretty ugly, pretty soon, once unnecessary complexity is introduced without good reasons (e.g., superior performance, processing speed), appropriate foundations (e.g., accurate, valid, and sufficient data), or good research designs (e.g., control conditions, random assignment, out-of-sample validation).
It is high time to demystify the data domain. If people outside the direct domain know what’s what, they will better understand what can and can’t be done with data. Moreover, they will not be as easily fooled by the cognitive AI mumbojumbo of consultants. A recent blog made me very happy. David Robinson — data scientist at StackOverflow — proposes very simple definitions of three interrelated domains (data science, machine learning, and artificial intelligence) and highlights their differences. If you haven’t yet, do read it, but to summarize David’s take:
Data science produces insights
Machine learning produces predictions
Artificial intelligence produces actions
These definitions are overly simplistic, David acknowledges, and not without their flaws: “A fortune teller makes predictions, but we’d never say that they’re doing machine learning!”. However, I feel its a great first attempt at demystification. Particularly, the applied example with which David continues make matters more clear:
Suppose we were building a self-driving car, and were working on the specific problem of stopping at stop signs. We would need skills drawn from all three of these fields.
Machine learning: The car has to recognize a stop sign using its cameras. We construct a dataset of millions of photos of streetside objects, and train an algorithm to predict which have stop signs in them.
Artificial intelligence: Once our car can recognize stop signs, it needs to decide when to take the action of applying the brakes. It’s dangerous to apply them too early or too late, and we need it to handle varying road conditions (for example, to recognize on a slippery road that it’s not slowing down quickly enough), which is a problem of control theory.
Data science: In street tests, we find that the car’s performance isn’t good enough, with some false negatives in which it drives right by a stop sign. After analyzing the street test data, we gain the insight that the rate of false negatives depends on the time of day: it’s more likely to miss a stop sign before sunrise or after sunset. We realize that most of our training data included only objects in full daylight, so we construct a better dataset including nighttime images and go back to the machine learning step.
Around the same time I read David’s blog, I came across the picture below, and its brother:
This got me thinking about how I would explain the field to a layman. In Human Resource Management (my PhD domain), there is enormous confusion around what’s what. When HR professionals speak of analytics they can mean about anything from a group average or a bar chart up to a deep neural network. I hoped that a simple diagram could help solve some of the confusion in terminology. Here’s my attempt:
Note that this diagram reflects my personal, implicit definitions of the concepts. Hence, in many ways, it may be biased, incorrect, or plain stupid. Fortunately, the r/datascience and r/MachineLearning communities were very willing to help me improve it. I should also stress that David’s blog inspired the attempt in the first place. While the diagram still greatly oversimplifies matters (and is in conflict with the purist academic definitions), I hope its helps as a layman’s introduction to the field.
How to read it? From left to right, we start out with raw data. Often, we’d first transform this data into usable features/variables: discriminatory characteristics of the objects were trying to analyze. On the one hand, a researcher may engineer these features. For instance, by some (statistical) transformation such as taking the average X within groups or reducing the number of categories for Z. On the other hand, unsupervised machine learning techniques may be applied to (semi-)automatically engineer features by identifying relevant clusters or dimensions in the data.
Next, the features can be input into statistical analysis. Taking the upper path, both unsupervised and supervised machine learning techniques can be used to build models that can be interpreted to gain insights about phenomena. This process is what business people usually mean when they say “analytics“. Mostly, it involves descriptive, causal or inferential analyses in order to gain insights into some process or phenomenon. Taking the lower path, supervised learning may be applied build a predictive model and retrieve predictions for a dependent variable. These predictions may also be evaluated using further analysis to retrieve insights. For instance, to gain understanding about what’s driving the predictions or how the predictions may be leveraged in practice.
Finally, both predictions and insights may form the basis of actions, which can be taken by a human agent or by a computer agent. In the latter case, we would deal with AI by some definitions.
There is one more route in the diagram, going directly from the raw data to the predictions: deep learning. Here, a neural network may take in complex data (e.g., text, images, sound) and engineer relevant features autonomously to base predictions on.
Disclaimer: The diagram is a major oversimplification! Particularly the placement of and overlap between the domains in this diagram is a simplification and not very good by purist, academic standards. For instance, despite being a extremely important field of innovation, I excluded reinforcement learning as I was unable to place it without making the figure considerably more complex. Similarly, the others domains do not have as clear demarcations as this figure suggests and their placement is by my definition of them. Data science, in my opinion, reflects the diffusion of insights or knowledge from data, particularly the (human) decisions and actions made in that process. Much of data science relies on machine learning, which involves how algorithms learn a model of reality from data, observations, or experiences. This learning can occur in different forms (e.g., supervised, unsupervised, deep, and reinforcement learning) and, unlike David’s definition, thus not always output predictions (e.g., also dimensions, clusters). Finally, machine learning is a specific branch of artificial intelligence, a label that has had many definitions. In my eyes, it includes any (partially) automated process where seemingly intelligent actions are automatically executed based on decision rules. An action can be as simple as a single if-then statement or as complex as a smart fridge ordering new milk. Whether AI is or should be considered a part of data science is food for a different discussion. For much more straightforward definitions of the fields, please consult this slide shared by u/mmcmtl:
If you have any thoughts on how the above diagram and/or blog could or should be improved, feel free to comment below, reach out, or share your own attempts!