Introduction to Statistics for Business & Data Science

Introduction to Statistics for Business & Data Science, available at $84.99, has an average rating of 4.55, with 41 lectures, 27 quizzes, based on 34 reviews, and has 2172 subscribers.

You will learn about Statistics – Use statistical methods to analyse data Data Analysis – Learn the most important tools for making sense of data. Data Visualization – Learn how to use Excel to create effective visuals. Interpretation – Learn to understand and comment on data visualizations. Cleaning Data – Use statistics to get your data ready for analysis Sampling – Learn different methods, and when they work. Probability – Learn all the most important techniques in basic probability theory. Bayesian Statistics – an introduction to Bayes' theorem This course is ideal for individuals who are People who want to use statistics to make better decisions. It is particularly useful for People who want to use statistics to make better decisions.

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Summary

Title: Introduction to Statistics for Business & Data Science

Price: $84.99

Average Rating: 4.55

Number of Lectures: 41

Number of Quizzes: 27

Number of Published Lectures: 40

Number of Published Quizzes: 27

Number of Curriculum Items: 70

Number of Published Curriculum Objects: 69

Original Price: £44.99

Quality Status: approved

Status: Live

What You Will Learn

Statistics – Use statistical methods to analyse data
Data Analysis – Learn the most important tools for making sense of data.
Data Visualization – Learn how to use Excel to create effective visuals.
Interpretation – Learn to understand and comment on data visualizations.
Cleaning Data – Use statistics to get your data ready for analysis
Sampling – Learn different methods, and when they work.
Probability – Learn all the most important techniques in basic probability theory.
Bayesian Statistics – an introduction to Bayes' theorem

Who Should Attend

People who want to use statistics to make better decisions.

Target Audiences

People who want to use statistics to make better decisions.

This is a course for people who want to use statistics to make better business and data science decisions. In the course I cover all the foundational concepts needed to begin your journey in data science, business analytics, or any area in which you need to use data to understand the world. No prior knowledge of the subject is assumed, and you’ll need nothing more than basic arithmetic and a desire to learn to get everything from this course.

I make statistics understandable and usable, particularly if you are new to the field, or have not used statistics in a meaningful way in business or data science previously. This is an excellent start!

The course is hands on and practical, with a focus on showing you how to use the skills taught. Wherever possible, I show you how to implement the techniques in Microsoft Excel.

Key concepts taught in the course include:

· Descriptive Statistics – the nuts and bolts needed when analyzing and working with data, from averages to measures of spread and correlation, to more advanced measures such as skew.

· Cleaning Data – the world sometimes gives us messy data, and I’ll show you how to clean it up and make sense of it.

· Visualizing Data – I’ll show you all the standard ways to usefully visualize your data, and in each case show you how to use Excel to create beautiful visualizations.

· Probability – I’ll teach you all the fundamentals of probability, up to and including conditional probability and an intro to Bayesian statistics.

As always, if you ever have questions about the content I’m waiting in the Q&A to help you out, and try to answer all my students questions.

I really hope you enjoy this course!

Woody

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction from Woody

Lecture 2: Introduction from Paul Siegel

Chapter 2: Descriptive Statistics

Lecture 1: Data for this chapter

Lecture 2: The Mean Average

Lecture 3: The Median Average

Lecture 4: The Mode Average

Lecture 5: Comparing Averages

Lecture 6: Quantiles, Range and Inter-Quartile Range – Data

Lecture 7: Standard Deviation and Variance

Lecture 8: Standard Deviation and Variance – Data

Lecture 9: The Coefficient of Variation

Lecture 10: The Coefficient of Variation – Data

Lecture 11: Skew

Lecture 12: Skew – Data

Lecture 13: Kurtosis

Lecture 14: Correlation

Lecture 15: Correlation – Data

Chapter 3: Cleaning Data

Lecture 1: Anomalies and Outliers

Lecture 2: Anomalies and Outliers – Data

Lecture 3: Coding Data

Chapter 4: Data Visualization

Lecture 1: Line Graphs

Lecture 2: Bar Charts

Lecture 3: Dual Axis Charts

Lecture 4: Pie Charts

Lecture 5: Histograms

Lecture 6: Histograms – Data

Lecture 7: Box Plots

Lecture 8: Cumulative Frequency

Lecture 9: Comparing Visualizations

Chapter 5: Sampling

Lecture 1: Populations and Samples

Lecture 2: Random Sampling

Lecture 3: Non-Random Sampling

Chapter 6: Probability

Lecture 1: What is Probability?

Lecture 2: Set Notation

Lecture 3: Independent Events

Lecture 4: Mutually Exclusive Events

Lecture 5: Tree Diagrams

Lecture 6: Venn Diagrams

Lecture 7: Conditional Probability

Lecture 8: Bayes' Theorem

Instructors

Woody Lewenstein
Mathematics Teacher
Starweaver Instructor Team
Learning | Doing | Connecting®
Paul Siegel
Top rated instructor and course creator

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 2 votes
4 stars: 9 votes
5 stars: 23 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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