Statistics for Data Science and Business Analysis

Course Overview

Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst,

English
Created by
Last updated Fri, 04-Jun-2021
+ View more
Course overview

Course Overview

Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist? This is where you start. And it is the perfect beginning! In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations.

 

Target Audience

  • Business analysts
  • Business Executives
  • People who want a career in data science, business intelligence.
  • People looking for a refresher on their Stats knowledge.

 

Learning Objectives

  • Understand the fundamentals of statistics
  • Learn how to work with different types of data
  • How to plot different types of data
  • Calculate the measures of central tendency, asymmetry, and variability
  • Calculate correlation and covariance
  • Distinguish and work with different types of distributions
  • Estimate confidence intervals
  • Perform hypothesis testing
  • Make data driven decisions
  • Understand the mechanics of regression analysis
  • Carry out regression analysis
  • Use and understand dummy variables
  • Understand the concepts needed for data science even with Python and R!

 

Business Outcomes

People who take this course will develop a data-driven approach to problems at the workplace.

 

What will i learn?

Requirements
Curriculum for this course
69 Lessons 5 hrs
Statistics for Data Science and Business Analysis
1 Lessons 05:00:00 Hours
  • Statistics for Data Science and Business Analysis
    Preview 05:00:00
Module 1
3 Lessons
  • What does the course cover
    Preview .
  • Population vs sample
    Preview .
  • Module 1 Quiz
    Preview .
Module 2
14 Lessons
  • Types of data
    Preview .
  • Levels of measurement
    Preview .
  • Categorical variables. Visualization techniques
    Preview .
  • Numerical variables. Frequency distribution table
    Preview .
  • The histogram
    Preview .
  • Cross table and scatter plot
    Preview .
  • Mean, median, mode
    Preview .
  • Skewness
    Preview .
  • Variance
    Preview .
  • Standard deviation and coefficient of variation
    Preview .
  • Covariance
    Preview .
  • Correlation
    Preview .
  • Practical example - Descriptive Statistics
    Preview .
  • Module 2 Quiz
    Preview .
Module 3
18 Lessons
  • Student's T Distribtuion
    Preview .
  • Module 3 Quiz
    Preview .
  • Practical example_Al bundy
    Preview .
  • Confidence intervals. Two means. Independent samples (Part 3)
    Preview .
  • Confidence intervals. Two means. Independent samples (Part 2)
    Preview .
  • Confidence intervals. Two means. Independent samples (Part 1)
    Preview .
  • Confidence intervals. Two means. Dependent samples
    Preview .
  • Margin of error
    Preview .
  • Population variance unknown, t-score
    Preview .
  • Introduction
    Preview .
  • Population variance known, z-score
    Preview .
  • Definition of confidence intervals
    Preview .
  • Estimators and estimates
    Preview .
  • Standard error
    Preview .
  • Central limit theorem
    Preview .
  • The standard normal distribution
    Preview .
  • The Normal Distribution
    Preview .
  • What is a distribution
    Preview .
Module 4
9 Lessons
  • Null vs alternative
    Preview .
  • Rejection region and significance level
    Preview .
  • Type I error vs Type II error
    Preview .
  • Test for the mean. Population variance known
    Preview .
  • Test for the mean. Dependent samples
    Preview .
  • Test for the mean. Independent samples (Part1)
    Preview .
  • Test for the mean. Independent samples (Part2)
    Preview .
  • Practical Example - Hypothesis Testing
    Preview .
  • Module 4 Quiz
    Preview .
Module 5
22 Lessons
  • Adjusted R-squared
    Preview .
  • Module 5 Quiz
    Preview .
  • Regression analysis
    Preview .
  • Dummy variables
    Preview .
  • A5. No multicollinearity
    Preview .
  • A4. No autocorrelation
    Preview .
  • A3. Normality and homoscedasticity
    Preview .
  • A2. No endogeneity
    Preview .
  • A1. Linearity
    Preview .
  • Assumptions
    Preview .
  • F-test
    Preview .
  • Introduction
    Preview .
  • Multivariate linear regression model
    Preview .
  • Regression tables
    Preview .
  • Ordinary least squares (OLS)
    Preview .
  • R-squared
    Preview .
  • Decomposition
    Preview .
  • Example
    Preview .
  • Geometrical representation
    Preview .
  • Correlation vs regression
    Preview .
  • The linear regression model
    Preview .
  • Correlation and causation
    Preview .
Exercises and solutions
2 Lessons
  • Downloadable files
    Preview .
  • Online P-Value Calculator
    Preview .
+ View more
Other related courses
3 hrs
Updated Tue, 23-Mar-2021
0 0
3 hrs
0 0
About instructor
Includes:
  • 5 hrs On demand videos
  • 69 Lessons
  • Access on mobile and tv
  • Full lifetime access