TagMath; Statistics; Predictive Analytics; Machine Learning; Neural Networks; Professional Development; Learning Engineering

The Math You Need for Future Success


Have you seen recent reports talking about knowledge and skills required for jobs and careers in the “smart future”?

The World Economic Forum 2023 Future of Jobs Report indicates that big data, cloud computing and AI are on target for future enterprise adoption. More than 75% of companies in the WEF Report look to adopt these technologies in the next five years. Digital platforms and apps are the technologies most likely to be adopted by the organizations surveyed, with 86% of companies expecting to incorporate them into their operations. The second-ranked category encompasses education and workforce technologies, with 81% of companies looking to adopt these technologies by 2027.

Math and analytical skills are going to be essential for future success, especially if one has dreams about being a research analyst, a scientific researcher, a designer or a product developer. This is especially true if one is working in the education and workforce technologies sector, where 81% of learning and development institutions and enterprises expect to adopt smart digital tools.

How does one prepare for opportunities that may call for more math or statistics than one may have needed “before Big Data”? 

One of the first things to do is to understand expectations for professionals working in jobs depending on machine learning and neural networks, as those are some of the most common gateways into the current “smart job” market

Here are examples of some of the math, programming, and analytical methods that you will want to know – or at least know about – if you want to get involved in these lines of work. For examples, techniques for being successful with Machine Learning include:

  • Linear Algebra
    • Vectors and matrices: Representing data and performing transformations are fundamental.
    • Eigenvalues and eigenvectors: Key in techniques like Principal Component Analysis (PCA) for dimensionality reduction.
  • Calculus:
    • Derivatives: Understanding gradients which lie at the heart of optimization algorithms used to train models (e.g., gradient descent).
    • Partial derivatives: Essential for multivariable optimization, especially in neural networks.
  • Statistics and Probability:
    • Probability distributions: Modeling the likelihood of events and understanding how data is generated.
    • Descriptive statistics: Summarizing and understanding data characteristics.
    • Hypothesis testing: Evaluating the significance of results.
    • Bayesian methods: Probabilistic reasoning for updating beliefs as more data becomes available.

Python is the dominant programming language you will need to know, thanks to its extensive libraries and ease of use. Python libraries make it easy to easily extend programming power and flexibility. Libraries include:

  • NumPy: Powerful numerical computing foundations.
  • Pandas: Data manipulation and analysis.
  • Scikit-learn: Classical machine learning algorithms.
  • TensorFlow or PyTorch: Deep learning frameworks.
  • Matplotlib/Seaborn: Data visualization.

Essential math and programming skills needed for Neural Networks development include:

  • The Math:
    • Linear algebra and calculus (especially chain rule for backpropagation), and optimization techniques are core to understanding how neural networks learn.
  • The Programming:
    • Proficiency with deep learning libraries (TensorFlow, PyTorch) to build and manipulate neural network architectures.
    • Working with large datasets and understanding how to optimize training efficiency.

For those who found themselves hyperventilating at the mere prospect of diving as deeply into the math pool as might be inferred from this list, keep in mind that deep mathematical expertise isn’t mandatory to apply machine learning successfully. Libraries abstract much complexity. However, a solid grasp of mathematics improves intuition and model development effectiveness.

Even though many learning and development professionals have not typically seen themselves as heavy users of mathematics, statistics or research methodologies in their work, the recognition that generative AI is now a part of Learning and Development whether we like it or not has been a wake-up call.  Anyone currently working in settings where “data-informed decision making” is becoming more prevalent owes themselves AT LEAST a quick refresher on the methods driving today’s biggest decisions. Anyone who wants to claim that they “know AI” needs to understand that it’s going to take more than prompt engineering to open that door.