Probability and Statistics for Business and Data Science from Udemy

Welcome to Probability and Statistics for Business and Data Science.

In this course we cover what you need to know about probability and statistics to succeed in business and the data science field.

This practical course will go over theory and implementation of statistics to real world problems. Each section has example problems, in course quizzes, and assessment tests.

We’ll start by talking about the basics of data, understanding how to examine it with measurements of central tendency, dispersion, and also building an understanding of how bivariate data sources can relate to each other.

Afterwards we’ll dive into probability , learning about combinations and permutations, as well as conditional probability and how to apply bayes theorem.

Then we’ll move on to discussing the most common distributions found in statistics, creating a solid foundation of understanding how to work with uniform, binomial, poisson, and normal distributions.

Up next we’ll talk about statistics, applying what we’ve learned so far to real world business cases, including hypothesis testing and the student's T distribution.

We’ll end the course with 3 sections on advanced topics, such as ANOVA (analysis of variance), understanding regression analysis, and finally performing chi squared analysis.

The sections are modular and organized by topic, so you can reference what you need and jump right in.

Our course includes HD Video with clear explanations and high quality animations, we also include extensive case studies to show you how to apply this knowledge to the real world.

We'll cover everything you need to know about statistics and probability to clearly tackle real world business and data science problems.

Including:

Measurements of Data
Mean, Median, and Mode
Variance and Standard Deviation
Co-variance and Correlation
Permutations and Combinations
Unions and Intersections
Conditional Probability
Bayes Theorem
Binomial Distribution
Poisson Distribution
Normal Distribution
Sampling
Central Limit Theorem
Hypothesis Testing
T-Distribution Testing
Regression Analysis
ANOVA
Chi Squared
and much more.

Not only do you get great technical content, but you’ll also have access to our online QA forums as well as our student chat channel. Where the TAs and myself are happy to help out with any questions you encounter. Upon finishing this course you’ll receive a certificate of completion you can post on your linkedin profile to show off to your colleagues, or even potential employers.

All of this content comes with a 30 day money back guarantee, so you can try out the course risk free.

So what are you waiting for? Enroll today and we'll see you inside the course.

What's inside

Learning objectives

Understand the basics of probability
Be able to implement basic statistics
Understand how to use various statistical distributions
Apply statistical methods and hypothesis testing to business problems

Understand how regression models work
Implement one way and two way anova
Understand chi squared tests
Be able to understand different types of data

Understand the basics of probability
Be able to implement basic statistics
Understand how to use various statistical distributions
Apply statistical methods and hypothesis testing to business problems
Understand how regression models work
Implement one way and two way anova
Understand chi squared tests
Be able to understand different types of data

Syllabus

Introduction

Course Overview Lecture - PLEASE DO NOT SKIP THIS!

FAQ - Frequently Asked Questions

Learn the basics of Data and how we'll use it for visualization and statistical analysis

What is Data?

Measuring Data

Five multiple choice questions on Types of Data, Mathematical Symbols & Syntax

Measurements of Central Tendency

Two multiple-choice questions on mean, median and mode

Measurements of Dispersion

Quiz #3 - Measures of Dispersion

Measurements - Quartiles

Quiz #4 - Quartiles and IQR

Bi-variate Data and Covariance

Pearson Correlation Coefficient

To complete this exercise, click on the Resources for this lecture, download and print the .pdf file titled Exercise01-Data. Answers are provided in a second file Exercise01-Data-ANSWERS. Good luck!

Let's build an understanding of Probability

What is Probability?

Permutations

Quiz #5 - Permutations

Combinations

Quiz #6 - Combinations

Intersections, Unions, and Complements

Independent and Dependent Events

Quiz #7 - Independent & Dependent Events

Conditional Probability

Quiz #8 - Conditional Probability

Addition and Multiplication Rules

Bayes Theorem

Quiz #9 - Bayes Theorem

To complete this exercise, click on the Resources for this lecture, download and print the .pdf file titled Exercise02-Probability. Answers are provided in a second file Exercise02-Probability-ANSWERS. Good luck!

It's time to understand basic statistical distributions!

Introduction to Distributions

Uniform Distribution

Quiz #10 - Uniform Distribution

Binomial Distribution

Quiz #11 - Binomial Distribution

Poisson Distribution

Quiz #12 - Poisson Distribution

Normal Distribution

Quiz #13 - Normal Distribution

Normal Distribution - Formulas and Z Scores

Quiz #14 - Z Score

To complete this exercise, click on the Resources for this lecture, download and print the .pdf file titled Exercise03-Distributions. Answers are provided in a second file Exercise03-Distributions-ANSWERS. Good luck!

As an optional resource for those familiar with Python, we've included the interactive Dash scripts used to display the Binomial Distribution, Poisson Distribution, Normal Distribution and side-by-side Normal Distributions on the screen. Changes to the input parameters (mean, standard deviation, degrees of freedom, etc.) are immediately reflected in the graphs. Enjoy!

Statistics

What is Statistics?

Sampling

Central Limit Theorem

Quiz #15 - Sampling and CLT

Standard Error

Hypothesis Testing

Hypothesis Testing Example Exercise #1

Hypothesis Testing Example Exercise #2

Quiz #16 - Hypothesis Testing #1

Type 1 and Type 2 Errors

Quiz #17 - Hypothesis Testing #2

Student's T Distribution

Student's T Distribution Example Exercise

To complete this exercise, click on the Resources for this lecture, download and print the .pdf file titled Exercise04-Statistics. Answers are provided in a second file Exercise04-Statistics-ANSWERS. Good luck!

Analysis of Variance (ANOVA)

Introduction to ANOVA

ANOVA - Analysis of Variance

F Distribution

Two Way ANOVA Overview

Two Way ANOVA Example Exercise

Two Way ANOVA with Replication

To complete this exercise, click on the Resources for this lecture, download and print the .pdf file titled Exercise05-ANOVA. Answers are provided in a second file Exercise05-ANOVA-ANSWERS. Good luck!

Regression

Linear Regression

Regression Example

Multiple Regression

To complete this exercise, click on the Resources for this lecture, download and print the .pdf file titled Exercise06-Regression. Answers are provided in a second file Exercise06-Regression-ANSWERS. Good luck!

Chi-Square Analysis

Chi Squared Analysis - Exercise Example

To complete this exercise, click on the Resources for this lecture, download and print the .pdf file titled Exercise07-ChiSquare. Answers are provided in a second file Exercise07-ChiSquare-ANSWERS. Good luck!

Thank you for taking the course!

BONUS LECTURE

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops knowledge in data science and business, which are valuable for career progression in both fields

Explores the essential concepts of probability and statistics along with real-world applications to the business and data science field

Provides a comprehensive explanation of key topics, beginning with the basics and gradually progressing towards advanced ideas

Covers advanced topics like ANOVA, regression analysis, and chi-squared analysis, making it suitable for learners seeking a deeper understanding of statistics

Builds a solid foundation in data analysis and interpretation, which are fundamental skills for data-driven decision-making

Taught by Jose Portilla, a highly experienced instructor in data science and machine learning

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Probability and Statistics for Business and Data Science with these activities:

Read 'Introduction to Probability' by Bertsekas and Tsitsiklis

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This book provides a comprehensive overview of probability theory, making it an excellent resource for students who want to learn more about the subject.

View Parallel and Distributed Computation: Numerical... on Amazon

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Read the book, taking notes on important concepts.
Do the practice problems at the end of each chapter.

Compile Course Materials

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Organize your course materials, ensuring that you have everything you need to succeed in the course.

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Download and print the course syllabus.
Purchase the required textbooks.
Set up system for organizing and storing notes and assignments.

Find a Probability Mentor

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Having a mentor can provide you with guidance and support throughout your learning journey.

Browse courses on Probability

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Identify someone who is knowledgeable about probability and who is willing to mentor you.
Meet with your mentor regularly to discuss your progress and get feedback.

Six other activities

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Review Calculus

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Knowing calculus will be critical to your success in this course, so take some time to refresh your memory of important calculus concepts.

Browse courses on Calculus

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Review your calculus notes from previous courses.
Do some practice problems from a calculus textbook or online resource.
Take a practice quiz or exam to test your understanding of calculus concepts.

Join a Study Group

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Studying with other students can help you learn the material more effectively and identify areas where you need additional support.

Browse courses on Probability

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Find a study group that meets regularly.
Attend the study group meetings and participate in the discussions.
Help other students with their understanding of the material.

Solve Probability Problems

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Solving probability problems will help you develop the critical thinking skills you need to succeed in this course.

Browse courses on Probability

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Find a practice problem set online or in a textbook.
Work through the problems, taking your time to understand the concepts involved.
Check your answers against the provided answer key.

Attend a Probability Workshop

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Attending a workshop will give you the opportunity to learn from experts in the field and network with other students.

Browse courses on Probability

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Find a probability workshop that is relevant to your interests.
Register for the workshop.
Attend the workshop and participate in the activities.

Create a Probability Tutorial

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Creating a tutorial will help you solidify your understanding of probability concepts and improve your communication skills.

Browse courses on Probability

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Choose a specific probability topic to cover.
Research the topic thoroughly.
Write a clear and concise tutorial.
Create visuals and examples to illustrate the concepts.

Build a Probability Model

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Building a probability model will give you hands-on experience with the concepts you are learning in this course.

Browse courses on Probability

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Choose a real-world problem that can be modeled using probability.
Collect data and analyze it to identify the probability distribution that best fits the data.
Build a probability model using the chosen distribution.
Use the model to make predictions about the future.

Career center

Learners who complete Probability and Statistics for Business and Data Science will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians apply statistical methods to collect, analyze, interpret, and present data. This course starts from the basics of data, and shows how to apply statistics to solve real world problems. After taking this course, you'll be familiar with common statistical distributions and techniques, such as binomial, poisson, and normal distributions, as well as hypothesis testing and regression analysis.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists use inferential and descriptive statistics to draw meaningful conclusions from data. This course teaches the basics of probability and statistics, up to advanced techniques like regression analysis and chi-squared tests. These topics are widely used in data science, and knowing them will give you an advantage in this role.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts collect, analyze, interpret, and communicate data to make better business decisions. This course covers statistical concepts such as probability, mean, median, standard deviation, confidence intervals, hypothesis testing, and regression analysis, all of which are used by Business Analysts to make data-driven business decisions.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers plan and conduct surveys, analyze data, and draw conclusions which are used to inform business decisions. This course covers the basics of probability and statistics, which are used by Market Researchers to design surveys and understand research data.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial Analysts make investment recommendations by analyzing the financial performance of companies. This course covers statistical concepts such as probability, mean, median, standard deviation, confidence intervals, hypothesis testing, and regression analysis, all of which are used by Financial Analysts to analyze financial data and make investment recommendations.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

Operations Research Analysts apply analytical methods to solve operational problems in organizations. This course covers statistical concepts such as optimization, linear programming, and simulation, which are used by Operations Research Analysts to improve efficiency and productivity.

See salaries and explore the career path for Operations Research Analyst

Quality Assurance Analyst

Quality Assurance Analysts ensure that products and services meet quality standards. This course covers statistical concepts such as sampling, hypothesis testing, and regression analysis, which are used by Quality Assurance Analysts to evaluate the quality of products and services.

See salaries and explore the career path for Quality Assurance Analyst

Risk Manager

Risk Managers identify, assess, and manage risks within an organization. This course covers statistical concepts such as probability, loss distributions, and risk assessment, which are used by Risk Managers to identify and manage risks.

See salaries and explore the career path for Risk Manager

Actuary

Actuaries use mathematical and statistical methods to assess risk in the insurance and finance industries. This course covers statistical concepts such as probability, loss distributions, and risk assessment, which are used by Actuaries to assess risk and determine insurance premiums.

See salaries and explore the career path for Actuary

Data Engineer

Data Engineers design, build, and maintain data infrastructure and systems. This course covers statistical concepts such as data management, data cleaning, and data transformation, which are used by Data Engineers to build and maintain data infrastructure and systems.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. This course covers statistical concepts such as probability, linear regression, and classification, which are used by Machine Learning Engineers to design and build machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, build, and maintain software systems. This course covers statistical concepts such as data structures, algorithms, and complexity analysis, which are used by Software Engineers to design and build software systems.

See salaries and explore the career path for Software Engineer

Computer Scientist

Computer Scientists research and develop new computing technologies. This course covers statistical concepts such as algorithms, complexity analysis, and artificial intelligence, which are used by Computer Scientists to research and develop new computing technologies.

See salaries and explore the career path for Computer Scientist

Biostatistician

Biostatisticians apply statistical methods to solve problems in the life sciences. This course covers statistical concepts such as survival analysis, clinical trials, and epidemiology, which are used by Biostatisticians to solve problems in the life sciences.

See salaries and explore the career path for Biostatistician

Epidemiologist

Epidemiologists investigate the causes of disease and other health problems in populations. This course covers statistical concepts such as survival analysis, clinical trials, and epidemiology, which are used by Epidemiologists to investigate the causes of disease and other health problems in populations.

See salaries and explore the career path for Epidemiologist