Statistics and Data Analysis in Excel

Develop crucial skills for a career as a data analyst

If you’re considering a career as a data analyst, you need to know about many aspects of statistics including histograms, pareto charts, boxplots, Bayes’ theorem, and much more.

On this five-week course, you’ll delve into applied statistics and learn how to use the powerful tools built into Excel to explore the core principles of statistics.

Learning from the specialists at CloudSwyft, a leading Learning-as-a-Service platform provider, you’ll develop essential data science skills to improve your knowledge and confidence in the field.

Learn the basics of descriptive statistics

On the course, you’ll develop an understanding of the concepts of descriptive statistics and basic probability before learning how to apply them.

For this process, you’ll learn how to use the calculation and visualisation environment of Excel.

Unpack random variables, confidence intervals, and more

To develop your statistical skills further, you’ll learn about the fundamentals of random variables, sampling, confidence intervals, and hypothesis testing – all key concepts in data analysis.

Learn how to apply concepts such as hypothesis testing

As you increase your knowledge on statistical concepts, you’ll learn how to apply it all in Excel.

This practical exercise will help you see the power of Excel in action and will give you the ability to calculate and visualise these essential statistical concepts in flexible ways.

With this understanding, you’ll be able to quickly explore data and produce valuable insights from the data set.

By the end of the course, you’ll have developed critical data analyst skills and have the confidence and knowledge to use Excel to its full potential.

This course is designed for anyone interested in learning more about statistics, and how to apply these concepts in Excel.

It will be particularly useful for those wanting to pursue a data analyst career.

You will need Microsoft Excel installed on your computer.

• Descriptive Statistics
• About the Course
• Defining Data
• Histogram and Skewness
• Descriptive Statistics with Analysis ToolPak
• What is a Boxplot?
• Categorical Data, PivotTables, and PivotCharts
• Summarizing Heirarchal Data
• 80-20 Rule and Pareto Charts
• Week 1 Test
• Basic Probability
• Introduction to Probability
• Law of Complements
• Mutually Exclusive Events and Finding Prob (A or B)
• Conditional Probability
• Law of Total Probability and Bayes Rule
• Basic Probability Review
• Week 2 Test
• Random Variables
• Random Variable Definitions
• Mean, Variance, and Standard Deviation of a Random Variable
• Mean, Variance, and Standard Deviation for Sum of Random Variables
• Binomial Random Variable
• Poisson Random Variable
• Normal Random Variable
• Central Limit Theorem
• Z Scores
• Week 3 Test
• Sampling and Confidence Intervals
• Populations and Samples
• Point Estimation of a Population Mean and Proportion
• The Standard Normal
• Confidence Interval Estimation
• Sample Size Determination
• The Finite Correction Factor
• Additional Reading
• Week 4 Quiz
• Hypothesis Testing
• Defining Hypotheses
• Type I and Type II Error
• One Sample Z-Test
• One Sample T-Test
• Single Sample Test for Population Proportion
• Testing Equality of Variances
• Testing the Difference Between Two Population Means
• Chi-Squared Test for Independence
• Additional Reading
• Week 5 Quiz
• Wrapping Up the Course