Business Data Analytics (IIBA®-CBDA Exam preparation)

Let's learn what is involved in business data analytics and why business data analytics is important.

We will define business data analytics from a multitude of viewpoints.

We will learn the scientific method and how it is applied to the business world.

We will look at 4 types of analytics methods.

We will start with the most foundational concept in analytics - probability, what it is and how to interpret it.

We will use the knowledge from the previous lesson and start building probability trees.

An interesting case study of how the context of analysis may influence an individual's interpretation of data.

A set of simple true/false questions to test your knowledge.

The tasks that constitute Identifying research questions knowledge domain.

Let's look and how identifying a business problem initiates the quest for research questions.

We'll explore business model canvas and how it may help with understanding the context of analysis,.

We'll learn how to identify stakeholders of an analytics initiative.

We'll look at formal ways to analyse stakeholder landscape.

We'll see what is involved in current state analysis.

An overview of process modelling and analysis to help define scope for analytics and land on a research question.

Root cause analysis - a formal technique to better understand the underlying causes for business problems.

Exploratory data analysis is one of the main tools in your toolbox as an analytics specialist. In the first part, we will explore the method behind it and will learn how to calculate the basic descriptive parameters.

In part two we will explore more advanced descriptive parameters, such as measurement of correlation and linear regression.

In part three we will focus on numerous business visualization models.

In part four we will apply all the methods together to explore a sample data set and come up with initial research hypothesis.

A case study that proves importance of visualization.

We'll explore the steps to define the future state.

Some considerations when defining research questions.

We will explore one of the most powerful techniques in analytics - hypothesis testing. In particular, we will answer the questions: What makes a good hypothesis? How to interpret the testing results?

We will learn what to consider when planning an analytics approach for a selected research question.

A set of simple true/false questions to test your knowledge.

The tasks that constitute Sourcing data knowledge domain.

The steps involved in planning for data collection.

A quick case study on how combining technical knowledge and knowledge of the business allows to plan collection of data.

The steps involved in determining which data sets would suffice for analysis.

The techniques to model data in a way that makes it easier to  manage.

The technique to map data from multiple data sources.

The technique to create and maintain a single source of truth for knowledge about data in the organization.

The steps involved to collect data for analysis.

We'll look at a structured process to move data from the source to a target location.

A technique to model how data flows within the business.

We'll explore the definition of what makes data good enough for analysis and how to validate it.

A set of simple true/false questions to test your knowledge.

The tasks that constitute Analyzing data knowledge domain.

The importance of planning!

We'll look at linear regression again and discuss some considerations when using it.

We'll discuss methods to analyse seasonality effects.

Classification is one of the most common tasks in data analytics. We will review some popular methods to classify the data and use it for predictions.

The steps involved in preparing the data for analysis.

EDA can be used not only for initial investigation but also as the first step of in-depth analysis.

The method for performing your analysis.

After analysis is over, the results need to be reviewed to decide: have we achieved what we expected to achieve?

Business simulation is a way to generate data when you have none - based on business rules and you knowledge of the business.

Optimisation is the technique to maximize or minimize a certain value (e.g. profit or cost) being constrained by business limitations.

In this case study we will see how a particular dynamic systems modelling tool can be used to perform the tasks of simulation and optimisation.

A set of simple true/false questions to test your knowledge.

Which tasks constitute interpreting and reporting of results.

We'll explore how to validate your understanding of who the stakeholders are.

Steps to plan communication with the stakeholders.

How to determine communication needs of different stakeholder groups.

We will look at the method to derive insights and communicate findings.

Data storytelling is a powerful technique to make your reports better.

In this case study we will have a look at amazing example of data storytelling to get inspired.

A set of simple true/false questions to test your knowledge.

Which tasks constitute influencing business decision-making.

Let's see how you can put your BA hat on and recommend actions based on insights from data.

What is an implementation plan and why do you need to bother?

Which role organisation change management plays in the process?

Let's have a quick look at how you could map stakeholders, identify their needs, plan implementation and org change.

A set of simple true/false questions to test your knowledge.

Which tasks constitute guiding strategy for analytics.

Let's review organizational elements of the strategy, such as future state org charts and team composition.

Let's review how talent strategy contributes to building the practice.

A set of simple true/false questions to test your knowledge.

Wow, you have survived through all the lectures and quizzes! Well done!

This test is close to the real one but is a bit shorter with longer time frame so you are less stressed for time. To pass this test you need to score 70% in under 2.5 hours. This is a guess though, as IIBA(r) does not reveal the pass score for their real exams.

But wait! Before you hit that start button, I strongly recommend you take a pause and read the Guide to Business Data Analytics. It has a lot of nuances and details which are important to pass the test. This course should have given you enough to see the logic behind the guide and understand the connections between the domains, techniques, and real life. It is however unavoidable to read it if you aim to pass the real exam.

Good luck!

Note, although these questions are based on the Guide to Business Data Analytics they are not taken directly from the exam questions bank. Those are kept secret :)