Python for Data Science and Artificial Intelligence


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

Your course is 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 about algorithmic complexity and how to address it with parallel and distributed computing, learn to work with data using NumPy, SciPy, Matplotlib, and Pandas, examine state-of-the-art machine learning libraries such as Scikit-Learn and Keras, and complete a realistic, real-life data science lab.

Ed Silantyev demonstrating the use of Python for Data Science and Artificial Intelligence


  • Dr. Paul Bilokon


This training takes place at London’s premier fintech hub:

Level39, One Canada Square
Canary Wharf
London E14 5AB

The nearest tube station is Canary Wharf (Jubilee line). The nearest DLR station is Canary Wharf DLR.

The delegates’ names will be registered with the ground floor Reception. If you need assistance, please contact us on +44 (0)20 796 57587.


Join our course in person and benefit from live, in-the-room interaction with our Python experts:


WBS Training Ltd (World Business Strategies) organises workshops and conferences for the capital markets and treasury divisions of investment companies worldwide, with all our efforts centered solely on the education of our clients. WBS Training does not operate to present dozens of events every year. Instead we select only the most innovative, pertinent and dynamic subjects, thus bridging the gap between the latest theoretical developments through to proven practical trading floor requirements. Therefore, we aim to ensure that such requirements can be effectively implemented in the real financial world.

The Machine Learning Institute Certificate is a comprehensive six-month part-time course, with weekly live lectures in London or globally online. The MLI is comprised of 2 levels, 6 modules, 24 lecture weeks, lab assignments, a practical final project and a final sit down examination using our global network of examination centres. This course has been designed to empower individuals who work in or are seeking a career in machine learning in finance. Throughout our unique MLI programme, candidates work with hands-on assignments designed to illustrate the algorithms studied and to experience first-hand the practical challenges involved in the design and successful implementation of machine learning models. The MLI is a career-enhancing professional qualification, that can be taken worldwide.


TimeDay 1
08:30 – 09:00Registration and welcome, a tour of Level39
09:00 – 10:00Lecture 1: The fundamentals of the Python programming language and Jupyter notebooks
10:00 – 10:30Tutorial 1
10:30 – 11:00Coffee break
11:00 – 12:00Lecture 2: Advanced Python features; algorithmic complexity; distributed computing; sieve of Eratosthenes; applications to cryptocurrencies and Blockchain
12:00 – 12:30Tutorial 2
12:30 – 13:30Lunch
13:30 – 14:30Lecture 3: Python libraries for working with data: NumPy, SciPy, Matplotlib, and Pandas
14:30 – 15:00Tutorial 3
15:00 – 15:30Coffee break
15:30 – 16:30Lecture 4: Machine Learning with Scikit-Learn; Deep Learning with Keras
16:30 – 17:00Tutorial 4
17:00 – 18:00Lab


  • 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
  • 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
    • Introduction to Keras

What to bring

Please refer to


Your course is designed to be self-contained. However, if you would like to read up on its contents before, during, or after the course, we recommend the following books:

  • David Beazley, Brian K. Jones. Python Cookbook, third edition. O’Reilly, 2013.
  • Yves Hilpisch. Python for Finance: Mastering Data-Driven Finance, second edition. O’Reilly, 2018.
  • Wes McKinney. Python for Data Analysis, second edition. O’Reilly, 2017.
  • Aurelien Geron. Hands-On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly, 2017.
  • Francois Chollet. Deep Learning with Python. Manning Publications, 2018.