Topics:
Data Science
Machine Learning
Deep Learning
Big Data
Analytics
The Hitchhiker's Guide says the meaning of life is 42. Considering that the field of Data Science is going through a period of exponential growth it too could soon find that the meaning of an artificial life is also 42. But if you are not involved on a day-to-day basis, the expansion can seem bewildering. The story of how disparate disciplines have combined to produce Data Science is fascinating.
In this talk, we will walk through a journey of scientific discovery. Following how, from humble beginnings, a multitude of sciences (and a surprising number of hacks) converged into the incredible advancements that you see in the media today. With these building blocks, we will be able to succinctly describe what these disciplines are and how they relate. The result will be the decomposition of a "rockstar" data science application; you will see that it is not so complicated after all. But the interesting result is that this generates a philosophical and political minefield; we can decompose the application and clearly see how it is built, but it also mimics or surpasses human capabilities. Are these human qualities? Is a more efficient or productive algorithm better than a human? Can we call them "intelligent"?
Attendees will gain a fundamental understanding of the field of data science. You will leave understanding exactly the difference between machine learning and deep learning and how they are different. You will be able to describe how data mining can help your business run analytics tasks to improve efficiencies. You will be able to explain to your children why big data techniques were invented to solve a specific problem. This will suit anyone interested in the history of data science and also serve as a broad introduction to the rest of the day's in-depth talks.
So, is the meaning of life 42? Possibly. But maybe all we need is a science algorithm to ask a
better question.
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Topics:
Data Science
Machine Learning
Deep Learning
Big Data
Analytics
In this one-day beginners-level course, you will be introduced to a range of fundamental data science concepts. You will discover how to interrogate data, choose a which machine learning methods suit your problems and how to achieve results quickly. It will provide an overview of many tools and techniques. The course is focused towards developers through programming-led examples but is industry oriented. The goal is to provide you with enough knowledge to "know what you don't know" and enable you to discuss fundamental data science topics with confidence.
Who will benefit
This course is aimed towards developers, in which we will discuss a little mathematics, but focus on developing real-life algorithms in Python. One-to-one help will be provided for developers new to Python and all algorithms, frameworks and libraries used will be demonstrated by the instructor.
This is an introductory course, which is suitable for most users with limited development experience. Some experience of Python is helpful. No data science experience is expected.
What you will achieve
The day will comprise of a series of sub-hour theoretical sessions separated by practical exercises. It will cover a range of topics, but it is expected that you will be able to:
- Discuss the differences between types of learning
- Describe problems in a way which can be solved with Data Science
- Understand the difference between regression and classification
- Solve problems using regression algorithms
- Solve problems using classification algorithms
- Learn how to avoid overfitting and appreciate generalisation
- Develop features within data
- Describe how and where to obtain data
Topics covered in this training
- How data science fits within a business context
- Data science processes and language
- Information and uncertainty
- Types of learning
- Segmentation
- Modelling
- Overfitting and generalisation
- Holdout and validation techniques
- Optimisation and simple data processing
- Linear regression
- Classification and clustering
- Feature engineering
- An in-depth practical example demonstrating the day’s concepts if time allows
