The plan is to use one year of manufacturing data and work towards a more sustainable process. After the introduction on February 26th , we will meet 4 evenings to work on the data. Finalists will present their results on #DIS2016 on March 23rd.
IBM Bluemix is an open-standards, cloud platform for building, running, and managing applications. With Bluemix, developers can focus on building excellent user experiences with flexible compute options, choice of DevOps tooling, and a powerful set of IBM and third-party APIs and services.
In the past few years, Python has emerged as a solid platform for data science. Couple a mature, clean and expressive language with powerful, fully-featured libraries for data wrangling and machine learning, and you’re set up for maximum productivity. Easily ingest your data from practically anywhere using one of Python’s thousands of free libraries. Effortlessly turn hundreds of convoluted lines of obscure model code into just a few lines of near-English prose. Add a few annotations and get maximum performance without drowning in pools of unnecessary boilerplate code. Present your results in beautiful living notebooks that seamlessly mix text, code and graphs. Whether you do all your modeling in R, you’ve written nothing but Matlab since university, or you swear by C# or (gasp!) Java, discovering Python will be a wonderful experience.
In detail, we plan to cover the following points:
Quick history of Python and typical use cases
Key advantages and disadvantages of Python for data science
Ways to run python and write code
Quick tour of language
Showcase of useful language packages for data science: NumPy, Matplotlib, SciPy, Pandas, Scikit-learn, PySpark, PyHive. Accessing RDBMSs
The course will be taught by Patrick Varilly of Data Minded. Patrick fell in love with Python four years ago as a theoretical chemistry post-doc at Cambridge and has never looked back. He has contributed to SciPy and used Python in wide-ranging settings, from scientific libraries to model proof-of-concepts to data backend pipelines.