Working with Your Own Data in Python

A language model can describe your data, but it cannot tell you whether the pattern you are observing is genuine or simply the result of ordinary variation. That is a different question, and it requires a different set of tools.

This course is designed for people who understand both the capabilities and the limitations of generative AI, and who now need to work directly with their own data rather than merely describing it to an assistant.

Starting from first principles, you will learn enough Python to load, sort, filter and analyse datasets, create charts, write your own functions, and run simulations. Rather than relying solely on formulas, you will use the computer to model scenarios thousands of times and observe what chance alone can produce.

By the end of the course, you will be able to determine whether an outcome in your own data is genuinely surprising, and you will understand which questions can be answered by an AI assistant and which require your own analysis. The material is based, with permission, on the University of California, Berkeley Data 8 course and has been delivered repeatedly to adult professionals.

By the end of the training, participants will be able to:

• Write and run Python from scratch, with no prior programming experience

• Load a dataset and select, filter, sort and summarise it

• Produce histograms and charts that show what the data does rather than what you hoped

• Write your own functions and apply them across a whole dataset

• Simulate a situation many times to see what chance alone would produce

• Test whether a model of how something works is consistent with the data you observed

• Reason about samples: what a sample can and cannot tell you about the whole

• Recognise which questions a language model can answer and which require working with the data directly
  

Contents:  ten units, each combining a short presentation of the concepts with hands-on lab work in a hosted Python notebook:

• Getting started: notebooks, numbers, names, and reading an error message

• Tables: selecting, dropping, sorting and filtering a real dataset (Exercise)

• Text and arrays, and loading your own data from a file (Exercise)

• Table manipulation and visualisation: charts and histograms (Exercise)

• Writing your own functions, and applying them across a whole table (Exercise)

• Working through a real dataset end to end (Exercise)

• Simulation: randomness, loops, and running a situation ten thousand times (Exercise)

• Chance: probability worked out by simulation rather than by formula (Exercise)

• Drawing random samples, and testing whether a model of how something works fits the data (Exercise)

• Sampling variability and the normal distribution: how much a result moves by chance alone. Wrapup of the course

The course combines short presentations introducing each concept with hands-on lab sessions in a hosted Python notebook environment. The day is structured around ten learning units, with individual support provided throughout the practical exercises. Participants will also receive additional exercises that can be completed independently after the course to reinforce and extend their learning.

This course is intended for professionals who work with data within their organisation and who already understand the capabilities and limitations of generative AI tools. Typical participants include analysts, controllers, risk and compliance professionals, banking and fund operations specialists, as well as HR and marketing analysts.

It is also particularly relevant for participants of the Mastering Corporate AI Implementation programme who have identified the data analyst role as a capability their organisation needs to develop.

No prior programming experience is required.

At the end of the training course, participants will be able to download a certificate of attendance issued by the House of Training from the learner portal.