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Essential Excel For Business Statistics and Data Analytics (Udemy.com)

Applied Statistical Modelling For Business Cases With Excel

Created by: Minerva Singh

Last updated November 2025

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What you will 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

How it compares

Against the other 355 rated Udemy courses in Excel on CourseDuck:

  • Rating4.2 out of 5higher than 8% of them
  • Reviews202#305 of 356 by number of reviews
  • Length3.6 hoursshorter than 69% of them
  • Last updatedNovember 2025more recently than 69% of them

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Course 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:


  1. Read in and clean messy data in MS Excel

  2. Basic webscraping with Excel

  3. Carry out common business data analytic tasks including filtering and pivoting

  4. Carry out pre-processing and data summarization to glean insights from data

  5. Develop powerful visualisations with MS Excel

  6. Learn important statistical techniques, including hypothesis testing and inferential statistics

  7. Carry out common statistical analysis using MS Excel-including correlations and regression analysis

  8. 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!

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Instructor Details

Minerva Singh

I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.

I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly

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Reviews

4.2

202 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Koen van Langen on 2/27/2026

It could have been a bit more efficient (for a lot of text was drawn instead of typt)

By Eric Cassity on 12/1/2025

Even with the zip file, it was unclear if and when the person taking the training was supposed to join in or which file to select. At times the data seemed confusing or was created at random which made it difficult to relate to a real-world situation.

By Brian Boruff on 11/10/2025

The instructor should've provided data sets in the .zip file that aligned with the data she was illustrating in the modules. She repeatedly lost her train of thought or displayed a lack of knowledge on how to quickly and efficiently complete the tasks she was trying to show. For example, when showing how to add a string of numbers, she typed each individual cell out (C2+C3+C4+C5+C6, etc, etc), when it's much easier to use the sum function =SUM(C2:C6). Moreover, she repeatedly used Mac options, when a large majority of people still use PCs and the operations to accomplish things are considerably different. For example, The steps for importing data from an XML are considerably different between Mac and PC. On PC, you have to pre-load the data into the transform screen, then drill down into the table to get it to show the data. You can't just open the xml as it is, or else you just get a single line of information, rather than the complete table with the texts. It's really important to be able to teach those differences, especially for people who don't know how to use Excel on PC. Luckily, I do, but I can imagine there are a ton of people who are lost in the sauce with your guidance here. The second quarter was just as convoluted. Once again, example data provided in .zip did not match the examples stepped through via the instruction. The instructor continuously lost her train of thought or showed examples that she had to go back and fix because they didn't produce what she said they would. Differences between Mac OS and PC also showing throughout the courses, causing me to have to Google a majority of the topics being discussed. If I have to search outside of the training to get an understanding of the training/processes being discussed, is the training really successful as a training tool? I would say no. Chart examples specifically could've have used the "financial sample" workbook that was provided in the data packet, for consistency and to answer questions that could arise from the visualization of data sets. For example, showing how to get a line chart that showed "units sold" per country on each date, would've been extremely helpful, as opposed to the examples shown by the instructor.

By Md Kobir on 2/7/2025

This course will play a crucial role in helping me achieve my goals and elevate my skills to the next level.

By Samson Michael on 2/7/2025

The instructor explains the course material in depth, making the learning experience both captivating and enjoyable.

By Muhammed on 2/7/2025

This course is incredibly valuable and strongly recommended for anyone looking to master business statistics and data analysis.

By Nguyễn S. Hùng on 2/7/2025

This course offers valuable real-world applications in business statistics and data analytics, making it highly practical and impactful.

By Md Riham Khan on 2/7/2025

This excellent Excel course dives into essential statistical concepts, providing hands-on techniques for data analysis and interpretation.

By Anonymized User on 11/20/2024

Thought i wouldn't keep up with the curse because of the crammed topics but tutor makes sure you understand the topics and also apply them.

By Anonymized User on 11/3/2022

The course has immense potential for practical application in the field of business statistics and data analytics.

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Overall Score : 84 / 100