Prof Milan Vojnovic COL 5.05

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Operations Research & Analytics, MSc in Quantitative Methods for Risk Management, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

The MSc in Data Science students are given priority for enrollment in this course.

Basic knowledge of Python or some other programming knowledge is desirable.

The course covers basic principles of systems for distributed processing of big data including distributed file systems; distributed computation models such as Mapreduce, resilient distributed datasets, and distributed dataflow graph computations; structured querying over large datasets; graph data processing systems; stream data processing systems; scalable machine learning algorithms for classification, regression, collaborative filtering, topic modelling and other tasks. The course enables students to learn about the principles and gain hands-on experience in working with the state of the art computing technologies such as Apache Spark, a general engine for large-scale data processing, and Apache TensorFlow, a popular software library for (distributed) learning of deep neural networks. Through weekly exercises and course project work, student can gain experience in performing data analytics tasks on their laptops and cloud computing platforms.

For more information, please see the course handout: http://lse-st446.github.io

This course will be delivered through a combination of classes and lectures totalling a minimum of 35 hours across Lent Term. This year, some or all of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos. This course includes a reading week in Week 6 of Lent Term.

Students will be expected to produce 10 problem sets in the LT.

Eight of the weekly problem sets will represent formative coursework. The other two will represent summative assessment.

Karau, H., Konwinski, A., Wendell, P. and Zaharia, M., Learning Spark: Lightining-fast Data Analysis, O’Reilly, 2015

Karau, H. and Warren, R., High Performance Spark: Best Practices for Scaling & Optimizing Apache Spark, O’Reilly, 2017

Drabas, T. and Lee D., Learning PySpark, Packt, 2016

White, T., Hadoop: The Definitive Guide, O’Reilly, 4th Edition, 2015

Apache Spark Documentation https://spark.apache.org/docs/latest

Apache TensorFlow Documentation https://www.tensorflow.org/get_started

Project (80%) in the LT.
Continuous assessment (10%) in the LT Week 4.
Continuous assessment (10%) in the LT Week 7.

The main assessment will consist of an individual project to develop a package for fitting statistical models of the student's own choice to big data sets.

In addition, among the 10 weekly problem sets, there will be two (in weeks 4 and 7) which will contribute to summative assessment (10% each).