Core Statistics for Data Literacy Training in Greece

Statistics involves collecting, analysing, interpreting, presenting, and organising data and provides methods for making inferences and conclusions about a population based on a sample of data.

Understanding the fundamentals of statistics can help make sense of large datasets by summarising and drawing meaningful insights from them.

So, whether you are calculating averages, measuring variability, or testing hypotheses, statistics can play a crucial role in the business.

This introductory course targets you, the data user, who wants to learn the basics of statistical analysis and will help you become more confident in using standard statistics in your day-to-day activities. It will also serve as an introduction to more advanced statistical tools available in products like Excel.

You will build and develop your statistical knowledge throughout the course using sample data inside an Excel environment.

We will help you apply the topics covered in the course to your everyday activities through hands-on learning and structured workshops.

• Understand how to write formulas using standard functions
• Experience in creating or using charts and graphs
• Ideally, you should have attended Microsoft Excel Essentials (QAEXINTC)

At the end of the course, you will be able to:

• Understand the need for statistical analysis
• Build common statistic functions, such as Min, Max, Average
• Create dispersion statistics such as Median, Mode, Standard Deviation
• Interact with the Data Analysis ToolPak in Excel
• Develop statistical visuals, such as trendlines, histograms, and scatter plots

Module 1: What is statistical analysis?

• Introduction to stats
• Identify the Excel formulas and techniques.

Module 2: Build the common statistics

• Count, Min, Max, Average

Module 3: Central Tendency and Dispersion

• Median
• Mode (sngl and mult)
• Standard deviation (sample and population)
• Quartiles (upper, lower, and IQR)

Module 4: The Data Analysis ToolPak

• Descriptive statistics
• Histogram data
• Rank and Percentile
• Correlation

Module 5: Visualise the results

• Line charts – the trend
• Histogram charts
• Box and whisker – dispersion
• Scatter charts - correlation

Looking to advance your statistical skills?

If you are interested in furthering your knowledge, consider our advanced courses: Statistics for Data Analysis in R and Statistics for Data Analysis in Python. These courses delve deeper into statistical methods and are designed for those who wish to apply these techniques using R or Python.