Essential Excel For Business Statistics and Data Analytics

Name: Essential Excel For Business Statistics and Data Analytics
Rating: 4.3 (191 reviews)

Applied Statistical Modelling For Business Cases With Excel

Created byMinerva Singh

Last updated 11/2025

English

What you'll learn

Introduction to Excel and navigating around the interface
Reading in external data (including data from webpages) into Excel
Derive insights from the data via grouping and pivoting
Describe your data (descriptive statistics, including identifying the best statistic)
Data visualisation through charts
Inferential statistics in Excel (including hypothesis testing)
Regression modelling in Excel

Course content

6 sections • 56 lectures • 3h 39m total length

Whats This Course About?1:54
Master essential Excel for business statistics and data analytics by reading, cleaning, and preprocessing data; visualize insights with pivoting, hypothesis testing, and regression analysis using MS Excel.
Data and Code0:03
Introduction to Different Data Types3:37
Say Hello To the Excel Interface6:18
Learn to navigate the Excel interface on Mac or Windows, using the title bar, ribbon, formula bar, and active cell, with templates, saving formats, and essential formulas.
Excel Templates4:36
Rock The Ribbon1:51

Logical Operations1:09
Basic Sort and Filter3:10
Learn how to filter data quickly in Excel using sort and filter, copy filtered data to another workbook, and expand selections to preserve row integrity during sort.
Filtering-23:44
Check For Conditions-If Only2:47
Apply AND/OR Conditions4:40
Basics Of Pivot Tables3:31
More Pivot Tables6:07
Order Of Progression Computations5:33
Learn how brackets determine the order of operations in Excel to avoid miscalculating tax and totals, illustrated by bracketed versus unbracketed formulas.
More BODMAS2:25
Remove Dollar Sign1:48
Learn to remove dollar signs from Excel data to enable basic computations, using format cells to set currency symbol to none, or find and replace across a column.

Descriptive Statistics in Excel5:08
Measuring Central Tendency7:41
Understand central tendency by comparing mean, median, and mode, and learn how outliers and skew influence which measure to use in Excel.
Summarisation By a Categorical Variable3:09
Subgroup Statistics2:33
Data Visualisations9:33
Line Chart1:50
Use line charts to visualize time series data, such as stock prices by date, displaying open and high values, with options to customize colors and legend.
Scatter Plot5:45
Plot a scatterplot to compare height and weight as continuous numerical variables, revealing their relationship. Adjust axes and add a trend line, and discuss linear regression later.
Histograms6:41
Bar Charts-Basics6:05
More Bar Charts4:17
Explains how to create and customize bar plots and stacked bar plots to compare math and reading scores across parental education categories.

What Is Hypothesis Testing?5:42
Comparing Two Groups3:08
Carry Out a T-Test (Check For Variances First)4:25
Why Do We Need ANOVAs?2:20
Carry Out One Way ANOVA In Excel2:45
Interpretation of One Way ANOVA2:37
Run a Two Way ANOVA In Excel4:00
Interpretation of Two Way ANOVA4:07
Understanding Interactions (In Case of of 2 Way ANOVAs)4:07
Confidence Interval-Theory6:06
Explore the theory of confidence intervals, including 95% intervals, margin of error, and the switch from z to t distribution for small samples, to quantify uncertainty in sample means.

Relationships Between Categorical Variables: Chi-Square Tests2:39
Use chi-square tests to assess whether two categorical variables are associated, by comparing observed and expected counts, evaluating p-values and the null hypothesis of independence.
Compute the Chi-Square Statistic9:19
Carry Out the Chi-Square Test5:06
Compute the chi-square statistic across all cases by summing squared values. With p > 0.05, accept the null hypothesis that gender and past time are independent.
Establishing a Relationship Between Quantitative Variables6:43
More Correlations3:06
Important Point to Note1:26
What is Ordinary Least Squares (OLS) Regression?6:52
Explore how ordinary least squares regression links height as the response to weight as the predictor. Interpret r-squared, adjusted r-squared, coefficients, and p-values to assess variation explained and model significance.
Categorical Predictors-Dummy Variables2:23
Create Dummy Variables3:46
Create dummy variables for marital status by coding married and divorced as 1/0 with single as the baseline. Use these dummy variables in regression analysis to interpret results.
Regression With Dummy Variables3:21
Interpret the Results of the Regression With Dummy Variables6:36
Coefficient of Determination2:15
Databases0:08
Learn how to choose the right database by comparing SQL and NoSQL, and apply a quick rule for SQL data to support business statistics and analytics.

Requirements

Access to Microsoft Excel
Interest in data processing and analysis
Developing data visualisations in Excel
Statistical modelling in Excel

Description

If You Are…..

A business intelligence (BI) practitioner
Data analyst
Interested in gaining insights from data (especially financial, geographic, demographic and socio-economic data)

Excel Is Your Friend for Common Business Data Analysis and Statistics Tasks

I’m Minerva Singh, and I’m an expert data scientist. I’ve graduated from 2 of the best universities in the world; MPhil from Oxford University (Geography and Environment), and a PhD holder in Computational Ecology from Cambridge University. I have several years of experience in data analytics and data visualization.

My course aims to help you start with no prior/limited exposure to data analysis and become proficient in undertaking common business statistical analysis with Microsoft Excel, including reading data from different sources and building data visualisations. You don't need any prior exposure to data analytics and visualization to get started with MS Excel. So if you have struggled with Excel, worry no more. After finishing my course you will be able to:

Read in and clean messy data in MS Excel
Basic webscraping with Excel
Carry out common business data analytic tasks including filtering and pivoting
Carry out pre-processing and data summarization to glean insights from data
Develop powerful visualisations with MS Excel
Learn important statistical techniques, including hypothesis testing and inferential statistics
Carry out common statistical analysis using MS Excel-including correlations and regression analysis
Include qualitative attributes in your regression models and interpret the results

If you take this course and it ever feels like a disappointment, feel free to ask for a refund within 30 days of your purchase, and you would get it at once. Become an expert in Data Analytics and Visualization with a new and powerful tool by taking up this course today!

Who this course is for:

Data analysts
Business and management students
Students and professionals seeking to use Excel for data tasks
Those looking to use Excel for basic data visualisations
Those looking to use Excel for basic statistical modelling

Essential Excel For Business Statistics and Data Analytics

What you'll learn

Explore related topics

Course content

Welcome To The Course?6 lectures • 18min

Read Data Into Excel7 lectures • 21min

Basic Data Processing With Excel10 lectures • 35min

Describe Your Data10 lectures • 53min

Inferential Statistics10 lectures • 39min

Statistical Relationship Between Variables13 lectures • 54min

Requirements

Description

Who this course is for: