Learn to carve valuable information from your masses of data!
Our Data Science with Python training course is aimed at analysts and software developers who need to create analysis and data visualisation solutions using the key functions and libraries available in and around Python.
You will benefit from extensive hands-on labs, delivered by an expert Data Science practitioner who can guide you from the basics of data wrangling with Python to using sophisticated libraries to visualise and make predictions based on your data.
This course forms an important part of our Data Science training programme - talk to us about our graduate training and cross-training for building robust in-house capabilities.
You may have come here looking for a more general intro to Python Programming training course. We've seen a rise in demand for Data Science training though, so we've had a bit of a re-jig as a result. Whatever your requirement, please do get in touch if you have any questions, we would love to help!
We would be happy to discuss custom / on-site Python Data Science training for any size of team. We can take into account your existing technical skills, project requirements and timeframes, and specific topics of interest to tailor the most relevant and focussed course for you.
This can be particularly useful if you need to learn just the new features and Python programming Best Practices, or need to include extra topics to help with pre-requisite skills. If you would like to dicuss your custom training requirements, please get in touch.
Introduction to Python Data Science
Why is Python good for Data Analysis?
Quick refresher on Python commands & executing code
Storing & Accessing Data
Manipulating Data in Lists
Python Data Structures
Key / Value Pairs
Overview of Python Data Science Libraries
A closer look at Numpy
Arrays & Matrices
N-dimensional Array Objects (ndarray)
SciPy in detail
Ordinary Differential Equation solvers
Data Manipulation using Pandas
Data Analysis using Pandas
Predictive Modelling & Machine Learning (ML) in Python
Evaluating available Algorithms
Importing your Dataset
Building & Validating your Model
Training the Model
Visualising the results – Univariate and Multivariate Plots