Data Science with Python Training Programmes

Who should attend

Analysts, Data Scientists, and software developers who want to get a practical introduction to Data Science and Machine Learning with Python.

Prerequisites

Delegates should ideally have some prior experience programming and / or of using statistical analysis techniques. Any experience with Python would be beneficial but we will give you "just enough" Python pointers to be able to create real-world workable solutions to data analysis, Machine Learning and visualisation challenges.

Live, instructor-led online and on-site training

We appreciate that you need flexibility to fit in with new working situations - whether you're an individual, part of a distributed team, or simply have projects and deadlines to meet.

Our remote training can take place online in a virtual classroom, with content split into modules to accommodate your scheduling challenges and meet your learning goals. Get in touch today to find out how we can help design a cost-effective, flexible training solution.

This example syllabus takes topics from our Data Analysis and Machine Learning courses and would typically comprise 9 - 10 days of training for experienced analysts. For newer learners with less exposure to data science or programming, we can modify the duration to allow for a more comfortable pace, to include self-paced learning sessions and realistic mini projects.

For more advanced teams, or for projects with very specific technology or outcome requirements, we are happy to build a highly tailored syllabus accordingly.

Introduction to Data Science with Python

• A gentle introduction to Python
• Working with data
• Key concepts
• Data Analysis
• Data Visualisation
• Machine Learning
• Deep Learning
• Quick tour of popular Data Science Tools and Platforms

Data Analysis with Python

Numpy:

• Working with NumPy arrays
• Essential operations with NumPy arrays
• Stats and linear algebra with NumPy

pandas:

• Working with table-like data in pandas
• Essential operations with Series and DataFrame object
• Loading data from file into DataFrame objects
• Summary statistics over DataFrame objects
• Data aggregation queries (groupby() method)
• Exploratory analysis of new datasets
• Data visualisation over DataFrames
• Join/merge operations with DataFrames
• Working with text data in DataFrames
• Working with Time Series data

Working with Databases:

• Working with relational databases in Python
• SQL and pandas
• Big Data solutions - Polars, DuckDB, PySpark

Data Visualisation in Depth

• Basic theories and principles of data visualisation
• Best practices in visual storytelling

Getting Started with Python's Core Plotting Libraries

• Introduction to Matplotlib
• Basic plotting functions
• Customizing plots using the axes API

Exploring plotting with pandas

• Quick visualisation directly from DataFrames

Overview of Seaborn

• Statistical data visualisation with Seaborn
• Creating complex data visualisations with less code

Hands-On Practice with Core Libraries

• Interactive exercises using the Gapminder dataset, creating a variety of charts and incorporating best visualisation practices

Advanced Visualisation with Plotly and GeoPandas

• Advanced dynamic visualisations with Plotly
• Interactive plots and dashboards
• Introduction to GeoPandas
• Handling geospatial data
• Creating maps and spatial plots

Building Interactive Web Applications with Streamlit

• Overview of Streamlit
• Deploying visualisations with Streamlit
• Building an interactive web app
• Allowing users to explore data through interactive widgets

Capstone Project

• Participants apply what they've learned in a comprehensive project using all tools from the course to create a data-driven story
• Peer reviews and group discussions on the projects

Machine Learning with Python

AI: What is it good for?

• Case studies
• When doesn't it work?
• Distilling the hype

How do machines learn?

• Supervised learning
• “Classic” ML vs Deep Learning
• Feature engineering
• Training
• Evaluation
• Describing the data vs predicting from it
• Data domains
• Tabular (unstructured) data, text, images, video, audio, multi-domain
• Unsupervised learning

Classic ML

• Supervised Learning Problems
• Classifier methods for predicting a label: Logistic regression, kNN, decision trees
• Regression methods for predicting a quantity: Linear regression, random forests
• State of the art ensemble methods: xgboost, catboost
• Evaluating and iterating on models
• Train/validation/test split, data leakage
• Underfitting/overfitting
• Feature selection, parameter tuning
• Model explainability, Shapley values
• Unsupervised Learning Problems
• Clustering: grouping similar items with k-Means
• Dimensionality Reduction with Principal Component Analysis

Fundamentals of Deep Learning with PyTorch

• Introduction to deep learning
• From the perceptron to the deep network
• How neural networks are trained
• When to use deep learning over classical ML
• Pretrained models, fine-tuning
• Building & training simple neural networks
• Overview of frameworks
• Layers, activation functions, loss functions
• Gradient descent: learning rate, batch size, epochs
• Overfitting & regularisation: dropout, L2
• Parameter tuning, auto-ML
• Training a neural network from scratch to recognise handwriting

MLOps

• Working with ML in production - from research to deployment
• Tooling
• Python Ecosystem for Data Science and Machine Learning
• Moving from notebooks to IDEs for local development
• Virtualenv, poetry, uv
• VSCode + Github Copilot, Cursor
• Cursor
• Experiment tracking with MLFlow
• Model deployment
• Docker containers
• Model endpoints: FastAPI, cloud (AWS Sagemaker, GCP, Azure)
• Monitoring and alerting
• Model performance
• Data drift

Advanced ML methods

• Deep learning for images
• CNNs
• Vision Transformers (ViT)
• Generative modelling for creating new data
• Autoencoders
• GANs
• Style transfer
• Sequence/time series modelling
• Recurrent neural networks
• Transformers
• Text
• Transformers
• Fine tuning a BERT model
• Embeddings
• Reinforcement learning