The field of data science is having an little identity crisis. The fundamental questions of what data science is, and who a data scientist is, remain largely undecided. Regardless of where the answer will fall, there are a number of tools and techniques that every data scientist should have in their toolbelt. Although the software languages, frameworks, and algorithms will come in and out of fashion, the fundamentals behind the trade of data science, which we talk about in this session, have existed for centuries and will continue to be used for ages to come.

What will the audience learn from this talk?

The audience will learn an overview and history of the math, philosophy, software engineering, and algorithms that are inseparable from the field of Data Science. We will cover techniques like optimisation theory like principle component analysis, at the level of analysing where and why we use certain techniques, but not how they are implemented or how to use them in a data science pipeline.

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### Further reading about Data Science

☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ Top 10 Applications of Data Science 2019

☞ A “Data Science for Good“ Machine Learning Project Walk-Through in Python

☞ Best Python IDEs for Data Science

☞ Top 6 Benefits of Learning Data Science with Python

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