Details
With the ease of gathering and storing data, successful businesses are learning the benefits of using data to drive the decision-making process. Learn how you can make better data-driven decisions.
In this course you will learn:
- Why it is important to base your decisions on data
- How Systems Thinking can be used to better understand your processes
- The fundamentals of data analysis
- A little bit of the statistical theory behind data analysis
- How to use Excel to put this into practice.
Delivery Methods
| Delivery Method | Duration | ||
|---|---|---|---|
| 2 days | Get a Quote | ||
| 2 days | Get a Quote |
Discounts Available
Save up to 10% by booking and paying 10 business days before the course.
Brochure:
Audience
- Business managers and leaders at all levels
- Project managers
- Those interested in learning data analysis
Pre-Requisites
Critical Thinking
Course Outline / Curriculum
Why Data-driven Decision Making?
- Bias and noise
- Data is everywhere
- People are predictable (to an extent)
Introduction to Systems Thinking
Harnessing data effectively is challenging. Fortunately, there’s an answer. We call this System Thinking for data – and it represents an end-to-end, enterprise-wide vision for how data, applications, processes, and people can all work in concert to drive innovation and change. System Thinking is so essential for data leaders today. It's is critical for data scientists to have a good appreciation of systems thinking and also to practice it.
Peter Senge sums up lucidly what systems thinking is: “Systems thinking is a discipline for seeing the ‘structures’ that underlie complex situations, and for discerning high from low leverage change. That is, by seeing wholes we learn how to foster health. To do so, systems thinking offers a language that begins by restructuring how we think.”
There are many stakeholders, such as LOB business analysts, data scientists, data engineers, data stewards, data governance councils, InfoSec analysts, administrators, and more. System Thinking is crucial to enabling all these people to work together effectively.
Introduction Data Analysis
- Choosing data
- Preparing and cleaning data
- Data models
- Storytelling with data
Some Statistics You should Know
- Descriptive Statistics
- Summary stats (means, std dev, etc)
- Observations, populations, and samples
- Inferential Statistics
- Probability distribution functions
- Parameters
- Skewness and Kurtosis
- Describe a Probability Distribution’s Shape
- Confidence
Practical Using Excel
- Using statistical functions
- Preparing data in Excel
- Performing confidence tests
- Linear regression and other modeling techniques
- Data presentation
Schedule Dates and Booking
There are currently no scheduled dates.