Bank PhD Graduate intakes get a solid foundation in C++ and Python Data Analytics

Now in our fifth year, we were initially asked to assist in this programme to teach their Tier 1 PhD graduate intakes in key technologies of C++ and Python to enable them to extract, transform and analyse crucial data to help achieve business-driven objectives by presenting key insights and developments and to assist Business Insights, Portfolio Analytics, Quantitative Analytics, Quantitative Research and Applied Sciences teams with models, forecasts, visualisations and understanding to help make decisions on potential changes to key products and initiatives.

This 2 week intensive training program has helped all the new intakes, over several years, gain a solid foundation on which to build their path ahead and covers the following topics:

C++ Introduction and C++ Advanced Programming including:

Principles of Object Oriented Programming - High Performance, Responsive and Robust C++ Application Development - Imperative Programming - Use Functions and Flow of Control - Memory Management - Data Structures and Classes - Implement Inheritance and Polymorphism – Templates - Operators and Streams – Metaprogramming - Idioms and Design Patterns - Smart pointers - Policy-Based Design

Python Introduction:

Python techniques and features - Integers, floating-points and strings - Aggregated types to manage large data sets - Flow Control for code layout and clarity - IF statements - Parameters and variables - Functional programming - Modularise code – Exceptions - Text and binary files – Agile and TDD - Regular expressions – OOP with Classes – Dynamic typing and re-usable code

Python for Data Analysis:

How to use Python and Jupyter Notebooks with libraries such as Altair, Pandas, Matplotlib, Numpy, Plotly, Seaborn and Scikit-Learn to Wrangle and load data from different sources, like Excel files and SQL databases – Analyse and extract statistical information – Run queries - Perform data aggregations - Time series  - Financial Series - Plot data effectively

Python for Machine Learning:

Data Science Vs Data Mining Vs Machine Learning - Problems and Applications - Supervised Learning - Unsupervised Learning - Machine Learning Algorithms - Deep Learning - Neural Networks

Tailored Pathways:

Our programmes can be tailored incorporating various technologies and session durations as may be required. Below is an example of a Python training pathway we developed for a major London based investment bank with each module designed as single day events as freeing up their resources for more than a day at time was not possible for them. 

We're always happy to discuss client needs in detail and help design training plans and pathways to suit.