Probability and Statistics for Business and Data Science

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About Course

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 hands-on course would cover the philosophy of statistics as well as how to apply it to real-world problems. Example challenges, in-class quizzes, and appraisal assessments are included in each segment.

We’ll begin by discussing the fundamentals of data, including how to examine it using measures of central propensity and dispersion, as well as how bivariate data sources can be related to one another.

We’ll then go on to probability, where we’ll hear about combinations and permutations, as well as conditional probability and how to use Bayes’ theorem.

After that, we’ll go over the most popular statistical distributions, giving you a firm basis on how to deal with regular, binomial, Poisson, and regular distributions.

First, we’ll discuss numbers, like hypothesis testing and the student’s T distribution, and how to adapt what we’ve experienced so far to real-world market situations.

We’ll wrap up the course with three parts on specialized subjects, including ANOVA (analysis of variance), regression analysis, and chi-squared analysis.

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

Our course provides HD video with concise illustrations and high-quality animations, as well as detailed case studies that demonstrate how to adapt what you’ve learned in the classroom to real-world situations.

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

Including:

  1. Measurements of Data
  2. Mean, Median, and Mode
  3. Variance and Standard Deviation
  4. Co-variance and Correlation
  5. Permutations and Combinations
  6. Unions and Intersections
  7. Conditional Probability
  8. Bayes Theorem
  9. Binomial Distribution
  10. Poisson Distribution
  11. Normal Distribution
  12. Sampling
  13. Central Limit Theorem
  14. Hypothesis Testing
  15. T-Distribution Testing
  16. Regression Analysis
  17. ANOVA
  18. Chi-Squared
  19. and much more!

 

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

Who this Probability and Statistics course is for:

  1. Someone interested in learning how to apply probability and statistics to business or data science
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What Will You Learn?

  • 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

Course Content

Probability and Statistics for Business and Data Science

  • Chi Squared Analysis Exercise Example
    00:00
  • Students T Distribution
    00:00
  • Type 1 and Type 2 Errors
    00:00
  • Hypothesis Testing Example Exercise 2
    00:00
  • Hypothesis Testing Example Exercise 1
    00:00
  • Standard Error
    00:00
  • Central Limit Theorem
    00:00
  • Sampling
    00:00
  • What is Statistics
    00:00
  • Students T Distribution Example Exercise
    00:00
  • Introduction to ANOVA
    00:00
  • ANOVA Analysis of Variance
    00:00
  • Chi Square Analysis
    00:00
  • Multiple Regression
    00:00
  • Regression Example
    00:00
  • Linear Regression
    00:00
  • Two Way ANOVA with Replication
    00:00
  • Two Way ANOVA Example Exercise
    00:00
  • Two Way ANOVA Overview
    00:00
  • F Distribution
    00:00
  • Normal Distribution Formulas and Z Scores
    00:00
  • Normal Distribution
    00:00
  • What is Probability
    00:00
  • Pearson Correlation Coefficient
    00:00
  • Bi variate Data and Covariance
    00:00
  • Measurements Quartiles
    00:00
  • Measurements of Dispersion
    00:00
  • Measurements of Central Tendency
    00:00
  • Measuring Data
    00:00
  • What is Data
    00:00
  • Permutations
    00:00
  • Combinations
    00:00
  • Intersections Unions and Complements
    00:00
  • Poisson Distribution
    00:00
  • Binomial Distribution
    00:00
  • Uniform Distribution
    00:00
  • Introduction to Distributions
    00:00
  • Bayes Theorem
    00:00
  • Addition and Multiplication Rules
    00:00
  • Conditional Probability
    00:00
  • Independent and Dependent Events
    00:00
  • 001 Course Overview Lecture
    00:00

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