Statistically Speaking  Understanding the fundamentals
Cost: $180 per participant
Learning Outcomes:
At the end of this workshop, participants should be able to:

Recognise the importance of datadriven decision making

Understand and interpret different data visualisations

Have a basic understanding of inferential statistics methods (i.e. hypothesis testing, correlation and regression)

Interpret simple outputs of the above inferential statistics methods

Have an awareness of the caveats and nuances in the interpretation of statistical results
Outline of Programme:

Descriptive Statistics

Common Data Visualisations

Population vs Sample

Confidence Intervals

Correlation and Linear Regression
Making Sense of Data
Cost: $380 per participant
Learning Outcomes:
At the end of this workshop, participants should be able to:

Recognise that there are different types of data related questions and data analytics

Apply exploratory and descriptive analysis to glean insights from the data

Understand the key principles of effective data visualisation

Create short presentations that effectively present the data story
Outline of Programme:

Knowledge Discovery process

Using basic Data Analysis methods using Excel (e.g. Pivot Table/Chart, Data Analysis Toolpak) to distil key insights from a dataset

Conceptualising effective Data Visualisations

Communicating insights through a data story
Data Storytelling
Cost: $180 per participant
Learning Outcomes:
At the end of this workshop, participants should be able to:

Recognise the various types of story structures

Understand the structure/framework of a good data storyboard

Have an awareness of the key principles behind developing a compelling data story

Build a simple storyboard using a case study
Outline of Programme:

Principles of effective Data Visualisations

Elements of a data story

Story structures (e.g. Freytag’s pyramid, Toulmin’s model)

Key principles of a compelling data story
Significant or not
Cost: $380 per participant
Learning Outcomes:
At the end of this workshop, participants should be able to:

Transform a relevant query and formulate it into a hypothesis

Use a gathered data sample to carry out the process of hypothesis testing on means in Excel

Interpret the output and make a conclusion about the means

Quantify the error of the conclusion and be cognizant of the consideration of practical significance

Appreciate the concepts of hypothesis testing and be able to engage in discussions on this topic
Outline of Programme:

Formulation of hypothesis

Concepts of hypothesis testing

Testing of mean using Excel

Statistical vs practical significance

Analysis of variance (ANOVA)
Data Preparation and Manipulation
Cost: $380 per participant
Learning Outcomes:
At the end of this workshop, participants should be able to:

Understand what is meant by ‘clean’ data and its importance

Inspect and clean datasets for analysis purposes

Recognise the importance of documentation in the data preparation phase

Be able to manipulate data for analytical purposes
Outline of Programme:

Importance of ‘clean’ data

Metadata catalogue

Data inspection and cleaning

Data manipulation and transformation
A tale of three regressions
Cost: $380 per participant
Learning Outcomes:
At the end of this workshop, participants should be able to:

Tell when to use single linear regression (SLR), multiple linear regression (MLR) and logistic regression

Generate the respective models using Excel

Interpret the coefficients and the various statistics

Assess the fit of a model and compare across models

Use logistic regression to perform classification

Understand and appreciate the concept of a model
Outline of Programme:

Concepts of modelling

Simple linear regression

Multiple linear regression

Logistic regression
Fundamentals of Machine Learning
Cost: $380 per participant
Learning Outcomes:
At the end of this workshop, participants should be able to:

Understand the big ideas of machine learning (e.g. supervised vs unsupervised machine learning)

Recognise the difference between bias and variance

Evaluate difference models in terms of metrics

Have an awareness of different machine learning techniques and interpret its output
Outline of Programme:

Supervised vs Unsupervised machine learning

Bias and Variance

Model evaluation metrics (e.g. accuracy, sensitivity, specificity)

Knearest neighbour

Kmeans clustering

Decision Tree