MLI Online Library Resource

Prior to the start of the MLI students are given access to our online resource in preparation for the certificate.

MLI Online Primer Resource:

  • Python Primer for Data Science. Presented by Nikolaos Aletras, Lecturer at The University of Sheffield
  • Python for Data Science and Artificial Intelligence Workshop. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
  • Maths Primer Refresher Material: For each topic (Linear Algebra, Optimization, Probability & Statistics), there will be a specific quiz to test initial background knowledge. We recommend that you first attempt answering the quiz exercises without looking at any material.

Additional MLI Learning Resource:

  • Big Data, High-Frequency Data, and Machine Learning with kdb+/q Workshop. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas.

Python Primers: 

Python for Data Science and Artificial Intelligence

Date: Tuesday 17th September 2019

Live and Online: 09.00 – 17.00


Advanced Python Techniques 

Date: Tuesday 24th September 2019

Live and Online: 09.00 – 17.00


Python for Data Science and Artificial Intelligence

Tuesday 17th September 2019: 09.00 – 17.00

Overview

Python is the de factolingua franca of data science, machine learning, and artificial intelligence. Familiarity with Python is a must for modern data scientists.

The MLI Python Primers are designed to take you from the very foundations to state-of-the-art use of modern Python libraries.

You will learn the fundamentals of the Python programming language, play with Jupyter notebooks, proceed to advanced Python language features, learn to use distributed task queues (Celery), learn to work with data using NumPy, SciPy, Matplotlib, and Pandas, examine state-of-the-art machine learning libraries (Scikit-Learn, Keras, TensorFlow, and Theano), and complete a realistic, real-life data science lab.


Syllabus:

  • The fundamentals of the Python programming language and Jupyter notebooks
    • Jupyter notebooks
    • The Python syntax
    • Data types, duck typing
    • Data structures: lists, sets, and dictionaries
    • Data types
  • Advanced Python features; distributed tasks queues with Celery
    • List comprehensions
    • Lambdas
    • Objects
    • The Global Interpreter Lock (GIL)
    • Multithreading and multiprocessing
    • Distributed task queues with Celery
  • Python libraries for working with data: NumPy, SciPy, Matplotlib, and Pandas
    • Multidimensional arrays in NumPy
    • Linear algebra and optimisation with SciPy
    • Data visualisation in Matplotlib
    • Time series data
    • Dealing with Pandas DataFrames
  • Machine Learning with Scikit-Learn; Deep Learning with Keras, TensorFlow, and Theano
    • Overview of machine learning
    • Introduction to Scikit-Learn
    • Keras and TensorFlow
    • Introduction to Theano

Advanced Python Techniques

Tuesday 24th September 2019: 09.00 – 17.00

Advanced Python Features and Putting them to use in Practice.

  • Algorithmics and graph theory
  • Prime numbers
  • Cryptography
  • Blockchain

Distributed Computing with Python


Final Examination: 

DATE: Tuesday 21st April 2020

Candidates will sit a formal 3-hour examination on a laptop. The exam is held in London for UK students and using our global network of examination centres for overseas students.

Final Project:

DATE: Friday 22nd May 2020

At the end of the programme, candidates apply the theoretical and practical skills acquired to a real world application within the financial services industry.

The assessment will take into account the quality and the originality of the work as well as the clarity of its presentation.