For centuries, statistics has been deemed the “language of data”. However, with the rise of computers and programming languages in recent decades, machine learning (ML) has emerged as a more dynamic, predictive, and technologically advanced “dialect” to understand everything your data has to say.
While these two disciplines might at first glance look like distant cousins, the fact is that they’re more closely interrelated than many think. For that reason, this article outlines and describes what statistics experts, professionals, and enthusiasts need to know to self-adapt to its modern, yet related evolution: machine learning.
Shared Foundations
Both statistics and ML focus on describing, recognizing patterns within, and drawing insights from data at their cores. While statistics typically concerns itself with describing and inferring traits datasets classically referred to as data groups, ML goes several steps beyond this by developing predominantly predictive models — but also advanced descriptive — models capable of learning and adjusting themselves based on data without explicitly programming the way they work or apply rules to perform predictions or other inference tasks.
The good news: as a stats-savvy individual, your statistics knowledge can serve as a great launching pad for jumping into ML in a relatively smooth manner. Essential statistical concepts such as hypothesis testing, probability distributions, time series analysis, and regression analysis are basic foundations of most ML algorithms. Think of your statistical background as a toolbox filled with different traditional tools that you are ready to technically enhance with more advanced instruments.
Four Key Points to a Smooth Transition
1. From a Descriptive to a (Mostly) Predictive Mindset: In statistics, your mission typically consists of explaining what has happened by analyzing past, existing data. Most ML models flip the narrative by predicting what might happen or will happen when exposed to new, unseen data, based on what they learned from labeled, historical data with known output or conclusions. A clear example is regression, which is equally present in both disciplines and often mixed up: while statistical regression can help you understand the relationship between variables (e.g. a known house attribute like size with its also known price), an ML-based regression model can use that understanding to forecast future trends very accurately, e.g. predicting the unknown price of a new house listing based on known features like size and others.
2. Embrace Data Complexity: Statistical methods usually work best with clean, well-structured data and make strong assumptions about data distributions, for instance, normality. ML algorithms used to build different types of models for regression, classification, etc., are more flexible, capable of handling messy, complex datasets and extracting insights through techniques like ensemble models and neural networks able to learn very intricate, non-linear relationships hidden in the data and otherwise invisible to the human eye.
3. Navigate Uncertainty and Probability Confidently: your statistical knowledge for understanding and modeling uncertainty, particularly that of a probabilistic nature, is crucial in plenty of ML techniques. Concepts like confidence intervals, p-values, and probability distributions won’t be left behind at all when diving into ML: many of them are reformulated and beautifully integrated into more sophisticated predictive frameworks. For example, a complex deep neural network capable of recognizing and classifying objects in images has at its very core a layer (called softmax activation layer) that calculated the probability of an object in the image belonging to a set of possible categories, thereby producing its output prediction as the category with highest probability.
4. Plunge into Programming: Pick a programming language and jump in. For example, the Python ecosystem provides powerful data science libraries like NumPy, pandas, scikit-learn, and TensorFlow that streamline ML model development workflows. Regarding the technical nuances of having all the necessary tools in your computer to start implementing ML models, you don’t need to fully immerse into integrated development environments (IDE) or platforms for full-stack software development: sticking to portable and handy tools in the cloud like Jupyter and Google Colab notebooks is in most cases enough to become familiar with building, evaluating, and trying out ML models.
Practical Take-Home Strategies and Wrap Up
To become ML-savvy, start by leveraging what you already know in statistical terms. It really does become easier to shift towards more complex ML methods like gradient boosting when you already have a solid understanding of basic techniques like linear regression.
Make room for studying programming languages such as Python, even if you have zero knowledge about programming: perhaps this is the perfect time to get started! Far from becoming an obstacle, programming will facilitate the transition toward the practical application of ML techniques with powerful libraries like scikit-learn and TensorFlow.
The knowledge gap can be filled with online courses, especially those that focus on the mathematical ML foundations. Comprehensive learning pathways are found in platforms like Udemy and Coursera.
Remember: the goal of ML in many practical scenarios is to extend statistical analysis processes, not to replace them. Becoming ML-savvy does not imply leaving your statistical roots. In fact, your background in statistics will be a strength, not a weakness, in the world of ML. An extra knowledge that many ML professionals might be short of or even lack.
