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Statistics 101: Guiding Data Science & Analysis using Excel
Rating: 4.6 out of 5(58 ratings)
233 students

Statistics 101: Guiding Data Science & Analysis using Excel

Statistics, Data Analytics, Data Science, Methods, Basics of Machine Learning, Linear Algebra
Created byLearn with Amine
Last updated 1/2026
English
English [Auto],

What you'll learn

  • Fundamentals of Statistics and Quantitative Methods
  • Statistics concepts to help with Interviews after graduation
  • Practical tools for Statistics and Visualization
  • Case studies

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Course content

2 sections • 10 lectures • 1h 31m total length

  • Measures of Central Tendency, Measures of Dispersion, Visualization27:36

    Explore measures of central tendency and dispersion, visualize data with pie charts, bar charts, and scatter plots, and analyze income five years after graduation by major.

  • How to create Pivot Tables9:52

    Create a pivot table to compute the average income five years after graduation by school type and major, with totals and a relative frequency distribution of income ranges.

  • How to Add Analysis Toolpak2:40

    Learn how to install the analysis toolpak in Excel on Windows, enabling data analysis tools for regression, anova, histogram, and t tests.

  • Binomial and Normal Distributions in Excel17:08
  • Differences between Binomial and Bernoulli Distribution6:31

    Differentiate Bernoulli and binomial distributions by noting Bernoulli has one trial with outcomes 0 or 1 and p, while binomial has n trials with outcomes 0–n and p.

Requirements

  • No prior data science experience required.

Description

Interested in the pathway to Machine Learning? Path to Machine Learning begins with Statistics. This course is for such students! This course is for Beginners and uses Excel for Data Analysis ( Data Analysis Toolpak and Excel Functions)

The course is designed to provide the fundamentals of machine learning and deep learning. It is targeted toward newbies, scholars, students preparing for interviews, or anyone seeking to hone the data science skills necessary. In this course, we will cover the basics of machine learning, and deep learning and cover a few case studies.


This short course provides a broad introduction to machine learning, and deep learning. We will present a suite of tools for exploratory data analysis and machine learning modeling. We will get started with python and machine learning and provide case studies using keras and sklearn.

Contents:

- Measures of Central Tendency, Measures of Dispersion, Visualization
- How to create Pivot Tables

- How to enable Data Analysis Toolpak

- Contingency Tables in Excel

- Binomial Distribution and Normal Distribution in Excel

So what are you waiting for? Learn Statistics and Data Analysis in a way that will enhance your knowledge and improve your career!

Thanks for joining the course. I am looking forward to seeing you. let's get started!

Who this course is for:

  • Machine learning enthusiasts, scholars or anyone seeking to hone the data science skills necessary
  • Beginner and intermediate developers interested in data science.

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