We can host this training at your preferred location. Contact us!

This two day course is designed for those who analyse data or who are creating machine learning models, but who wish to firm their understanding in core concepts as well as expanding into types of data distributions, inferential statistics (hypothesis tests), statistical significance, and a deeper understanding of how linear regression works. It is expected that you will have experience with a programming language used for data analysis such as Python or R – if this is not currently the case we suggest completing one of our Python or R for Data Handling courses.

As well as providing a business context to using core concepts such as averages, spread, and interpreting analyst visualisations, you will take this knowledge further and learn how distributions, sampling, and hypothesis testing can be used to analyse data in an organisation and in automatically highlighting significant results or anomalies.

If you are on a learning journey with Machine Learning and AI this course will give you a strong starting point in the statistical methods that underpin a large number of algorithms without overloading you with too many mathematical formulae or notations that are otherwise commonly used to communicate advanced mathematics. Your focus will be on business problems and applying tools such as Python or R that you will need as part of this journey.

Throughout the course you will engage with practical labs, activities, and discussions with one of our technical specialists. All modules involve the use of Python or R to practice the techniques taught – setting you up to succeed in analysing, interpreting, and getting value from your data.

Target Audience

Anyone wishing to expand their understanding of Maths and Statistics related to Data Science. This course will provide all the required pre-requisite statistical knowledge needed for our more in depth programmes.

Minimum of GCSE Maths or equivalent
Experience with Python or R for Data Handling

Central Tendency, Variation, and Outliers - Using an appropriate software tool, calculate:

Mean, Mode, Median, Mid-range
Population and Sample Standard Deviation & Variance
Inter-Quartile Range
Apply methods for automating identification of outliers
Discuss appropriate handling of outliers
Practical Lab Activities with Python

Visualisations and Skew - Using an appropriate software tool, create:

Histograms
Scatter Plots

Use these to:

Identify skew and the effect this may have on modelling
Identify the location of the averages
Compare two samples (e.g. taken at different times or from different locations)
Determine the appropriate shape of a model and whether there are opportunities to linearise
Practical Lab Activities with Python

Introduction to Probability

Interpret P() notation and calculate simple and conditional probabilities
Use Venn diagrams with set notation to calculate probabilities
Use Tree diagrams and simple combinatorics to calculate probabilities
Practical Lab Activities with Python

Introduction to Distributions

Recognise what a probability or data distribution is
Identify when a distribution is considered to be Binomial, Poisson, or Normal
Identify when a distribution can be treated as Normal and what this means for analytical methods
Practical Lab Activities with Python Sampling
Critique different sampling techniques
Explain the impact a sampling or data gathering method may have on analytical model results
Recognise methods for estimating summary statistics for a population from a sample
Practical Lab Activities with Python

Introduction to Hypothesis Testing

Recognise the steps required for a Hypothesis test from the set- up, assumptions, testing, and interpretation of p-values
Identify a variety of tests and when they are used
Evaluate the output of tests from an appropriate software tool
Practical Lab Activities with Python

Linear Regression

Recognise when a linear regression is an appropriate method to use
Interpreting y = mx + c
Evaluate linear models
Practical Lab Activities with Python

Upcoming Trainings

Join our public courses in our South Africa facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

Classroom / Virtual Classroom

02 August 2024