You’re struggling to keep up with the latest algorithms and technology. What are your options?

1 Assess your needs

Prior to exploring the vast and varied pool of algorithms and technology, it is important to have a clear idea of what you want to achieve, what problems you want to solve, and what skills you need to develop. To do this, ask yourself questions such as: what are the current and future trends in your industry or domain? What are the key performance indicators and metrics that you wish to optimize or enhance? What data sources and types do you have access to or need to acquire? What existing or potential use cases and applications of algorithms and technology can be applied in your business context? Lastly, what gaps or weaknesses exist in your current knowledge or practice of algorithms and technology? Answering these questions will help you narrow down your focus and prioritize your learning goals.

2 Choose your sources

Once you have a clear picture of your needs, you can look for the best sources of information and education on algorithms and technology. Depending on your preferences, budget, and availability, you can choose from a variety of options, such as online courses and platforms like Coursera, Udemy, edX, and Khan Academy; books and blogs like Introduction to Algorithms by Cormen et al., Grokking Algorithms by Aditya Bhargava, and Towards Data Science blog; podcasts and videos like Data Skeptic podcast, Linear Digressions podcast, and Two Minute Papers YouTube channel; or communities and events like Stack Overflow, Reddit, Meetup, and Kaggle. You can learn from experts or authors’ insights, examples, tips, stories, opinions, advice, questions, feedbacks, experiences or network with other learners.

3 Apply your learning

Finally, to test your understanding and demonstrate your value, you need to apply your learning to real-world problems and projects. This could be done by implementing algorithms and technology in existing or new workflows, systems, or products. Various tools and frameworks, such as Python, R, TensorFlow, PyTorch, and scikit-learn can be used to code and deploy your solutions. Moreover, you can evaluate algorithms and technology in terms of their performance, accuracy, efficiency, and scalability. Cross-validation, confusion matrix, ROC curve, and A/B testing are some of the methods and metrics that can be used to measure and compare your results. Additionally, you can communicate algorithms and technology to stakeholders through various formats and media such as reports, presentations, dashboards or infographics to explain and visualize findings and recommendations. Keeping up with the latest algorithms and technology is challenging but possible; by assessing needs, choosing sources and applying learning you can enhance knowledge, skills and value as an executive in the data-driven world.