Udemy

Excel for Data Science and Machine Learning

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  • 4,444 Students
  • Updated 1/2024
4.7
(566 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
5 Hour(s) 53 Minute(s)
Language
English
Taught by
365 Careers
Rating
4.7
(566 Ratings)

Course Overview

Excel for Data Science and Machine Learning

Perform Machine Learning and Advanced Statistical Analysis On Your Own - Even If You Don't Code! 100% in Excel

Why machine learning and data science in Excel?

Do data scientists and data analysts use Excel at all?

The answer is a resounding “Yes, they do!”

Few people in an organization can read a Jupyter Notebook, but literally everyone is familiar with Excel. It provides the direct, visual insight that both experts and beginners need to apply the most common machine learning methods. Plus, it is naturally suited to data preparation.

In fact, the simplicity of Excel lowers barriers to entry and allows you to undertake your own data analysis right away. Even if you are not a computer science graduate with Python coding skills, this course will teach you how to perform machine learning and advanced statistical analysis on your own.

Excel is the perfect environment to grasp the logic of different machine learning techniques in an easy-to-understand way. All you need to do is get started, and in no time, you will be able to fully understand the intuition behind ML algorithms without having to code at all.

So, if you are not into programming but you want to break into data science, statistical analysis, and machine learning, and you aspire to become a data analyst or data scientist, you’ve come to the right place.

Machine learning methods we will cover in the course:

  • Linear regression

  • Multiple Linear Regression

  • Logistic Regression

  • Cluster Analysis

  • K-Means Clustering

  • Decision Trees

You will learn fundamental statistical and machine learning concepts, such as:

  • Regression coefficients

  • Variability

  • OLS assumptions

  • ROC curve

  • Underfitting

  • Overfitting

  • Difference between classification and clustering

  • How to choose the number of clusters

  • How to cluster categorical data

  • When to standardize data

  • Pros and Cons of clustering

  • Entropy (Loss function)

  • Information gain

As you can see, we aim to teach you the foundations of machine learning and advanced statistical analysis in a software that is truly easy to understand. And the best part is, once you finish this course, you will have the transferable theoretical knowledge you’ll need if you decide to dive into the advanced frameworks available in Python.

So, if you are passionate about machine learning but you don’t know how to code, then this course is the perfect opportunity for you. Click ‘Buy Now’, get excited, and begin your ML journey today!!


Course Content

  • 9 section(s)
  • 72 lecture(s)
  • Section 1 Introduction
  • Section 2 Simple Linear Regression
  • Section 3 Multiple Linear Regression
  • Section 4 Linear Regression Practical Example
  • Section 5 Logistic Regression
  • Section 6 Cluster Analysis
  • Section 7 K-means Clustering
  • Section 8 Decision Trees
  • Section 9 Machine Learning in the Cloud

What You’ll Learn

  • Use This Course to Improve Your Excel Skills
  • Learn How to Perform Machine Learning Techniques on Your Own - No Coding Skills Required
  • Fundamental Statistical Concepts
  • Grasp the Intuition Behind Advanced Statistics
  • How to Use Excel for Advanced Statistical Analysis
  • Improve Your Analytical Thinking
  • Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Cluster Analysis
  • K-Means Clustering
  • Decision Trees


Reviews

  • J
    Jesús Rodríguez
    5.0

    Excelente curso, un poco desactualizado en la nueva versión de Azure studio, pero da las herramientas para hacerlo por nuestra cuenta.

  • H
    Huy Võ
    5.0

    Its really good

  • D
    David Allen
    4.5

    The explanations are clear. I'd just prefer if everything were done from scratch, in the sense of editing and writing stuff, before we put in regression results and all that. Although I figured it out, it would be much easier. But that's not anything major, it just makes me more hardworking and more attentive, and it also saves unnecessary time for the instructors. But that is by the way. The lessons were superb

  • C
    Cassandra Kerezsi
    3.0

    No feedback if you're having trouble, otherwise would give a better review. Good content overall, though.

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