Overview
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 R or Python – if this is not currently the case we suggest completing one of our R or Pythonfor 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 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 R to practice the techniques taught – setting you up to succeed in analysing, interpreting, and getting value from your data.
Prerequisites
- Minimum of GCSE Maths or equivalent
- Experience with Python or R for Data Handling
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.
Outline
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 R
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 twsamples (e.g. taken at different times or from different locations)
- Determine the appropriate shape of a model and whether there are opportunities t linearise
- Practical Lab Activities with R
Introduction to Probability
- Interpret P() notation and calculate simple and conditional probabilities
- Use Venn diagrams with set notation tcalculate probabilities
- Use Tree diagrams and simple combinatorics tcalculate probabilities
- Practical Lab Activities with R
Introduction to Distributions
- Recognise what a probability or data distribution is
- Identify when a distribution is considered tbe Binomial, Poisson, or Normal
- Identify when a distribution can be treated as Normal and what this means for analytical methods
- Practical Lab Activities with R 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 R
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 R
Linear Regression
- Recognise when a linear regression is an appropriate method tuse
- Interpreting y = mx + c
- Evaluate linear models
- Practical Lab Activities with R
Frequently asked questions
How can I create an account on myQA.com?
There are a number of ways to create an account. If you are a self-funder, simply select the "Create account" option on the login page.
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Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.
How do QA’s virtual classroom courses work?
Our virtual classroom courses allow you to access award-winning classroom training, without leaving your home or office. Our learning professionals are specially trained on how to interact with remote attendees and our remote labs ensure all participants can take part in hands-on exercises wherever they are.
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How do QA’s online courses work?
QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.
All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.
When will I receive my joining instructions?
Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.
When will I receive my certificate?
Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.