Statistics For Data Science And Business Decisions

Master Statistics for Data Science and Business Decisions.

What you'll learn
Master the fundamentals of Statistics for making effective business decisions
Requirements
No
Description
The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is required to make correct and relevant interpretations of data. Lack of this knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics.Course Contents include:Basic Data DescriptorsCategories of descriptive dataMeasures of central tendencyMeasures of DispersionStandard deviation measure and Chebyshev’s theoremDescriptive Measures of Association, Probability, and Statistical DistributionsMeasures of association, the covariance and correlation measuresCausation versus correlationProbability and random variablesDiscrete versus continuous dataIntroduction to statistical distributionsThe Normal DistributionWorking with Distributions, Normal, Binomial, PoissonApplications of the Normal distributionThe Binomial and Poisson distributionsSample versus population dataCentral Limit TheoremRegression AnalysisWelcome to this course.

Overview

Section 1: Introduction

Lecture 1 Course Overview

Section 2: Descriptive Statistics

Lecture 2 Introduction to Descriptive Statistics

Section 3: Descriptive Statistics: Measures of Central Tendency

Lecture 3 Mean, Median and Mode

Lecture 4 Mean vs Median

Section 4: Descriptive Statistics: Measures of Dispersion

Lecture 5 Measures of Dispersion - Range and IQR

Lecture 6 Box Plots

Lecture 7 Standard Deviation

Lecture 8 Chebyshev's Theorem

Section 5: Descriptive Statistics: Measures of Association

Lecture 9 Measures of Association

Lecture 10 Covariance

Lecture 11 Correlation

Section 6: Probability and Random Variables

Lecture 12 Probability

Lecture 13 Random Experiment and Random Variables

Lecture 14 Probability Distributions

Section 7: Normal Distribution

Lecture 15 Understanding Normal Distribution

Lecture 16 Visualizing effect of mean and standard deviation for Normal Distribution

Lecture 17 Notation and Standard Normal Distribution

Lecture 18 Using Normal Distribution in Excel and Python

Lecture 19 NORM.INV and norm.ppf functions

Section 8: Case Study: Business decision on choosing production process

Lecture 20 Question: Business Decision using Normal Distribution

Section 9: Sampling and Central Limit Theorem

Lecture 21 Why Sampling?

Lecture 22 Random Sampling

Lecture 23 Central Limit Theorem

Section 10: Discrete Probability Distributions

Lecture 24 Bernoulli Process

Lecture 25 Binomial Distribution

Lecture 26 Poisson Distribution

Lecture 27 Poisson Distribution - Examples

Section 11: Regression Analysis

Lecture 28 Regression - Introduction

Lecture 29 Regression - Building the Model

Lecture 30 Estimating model parameters - Python

Lecture 31 Estimating model parameters - Excel LINEST() (Optional)

Lecture 32 Interpreting estimated model

Lecture 33 Prediction on new data

Lecture 34 Errors, Residuals and R-square

Data scientists, Business analysts, statistics students