Data Science with Python Training Programmes

This comprehensive training programme is designed for organisations seeking to build teams that can harness the power of Python for data-driven insights and solutions. It is particularly beneficial for:

• Data Analysts: Professionals aiming to enhance their data manipulation, exploration, and visualisation skills using Python's extensive libraries to uncover meaningful patterns and trends.

• Scientists and Researchers: Individuals across various scientific disciplines who require a versatile programming language for data analysis, statistical computing, and applying fundamental machine learning techniques to their research data.

• Business Intelligence Professionals: BI specialists looking to expand their analytical capabilities with Python to generate more sophisticated reports, dashboards, and predictive insights.

• Aspiring Data Scientists: Those seeking a comprehensive introduction to data science principles and Python's key libraries (like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn) as a foundation for a data science career.

• Software Developers: Developers looking to expand their skill set into the data science domain and integrate Python-based data analysis and machine learning capabilities into their projects.

• Financial Analysts: Professionals in the finance sector who need to perform quantitative analysis, build financial models, and leverage Python's data science tools for informed decision-making.

• Marketing Analysts: Individuals who want to analyse marketing data, understand customer behaviour, and potentially build basic predictive models for campaign optimization using Python.

• Anyone working with data: Professionals in various roles who need to extract, process, analyse, and visualise data effectively using Python.

In the case of Graduate Schemes, we would expect an audience to have attained degree-level quialifications in STEM or Computer Science; however, if the programme is designbed to cross-skill a non-technical audience we would be able to change the angle of attack to account for those with less exposure to IT / scientific disciplines.

Our custom Python data science training programmes are available for in-house face-to-face delivery at your location of choice, or online via MS Teams (or your own preferred platform).

Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.

This example syllabus takes topics from a range of 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

• https://www.python.org/ - the home of Python

• https://pandas.pydata.org/ - get pandas

• https://numpy.org/ - get Numpy

• https://anaconda.org/anaconda/conda - distribution of many popular Python libraries and tools