Statistics For Data Science And Business Analysis

Statistics For Data Science And Business Analysis
Last updated 12/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis

What you’ll learn

Understand the fundamentals of statistics

Learn how to work with different types of data

How to plot different types of data

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

Understand the concepts needed for data science even with Python and R!

Requirements

A willingness to learn and practice

Description

Comprehensive

Practical

To the point

Packed with plenty of exercises and resources Data-driven

Introduces you to the statistical scientific lingo

Teaches you about data visualization

Shows you the main pillars of quant research

We worked full-time for several months to create the best possible Statistics course, which would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts and course notes, as well as a glossary with all new terms you will learn, are just some of the perks you will get by subscribing. What makes this course different from the rest of the Statistics courses out there?

High-quality production – HD video and animations (This isn’t a collection of boring lectures!) Knowledgeable instructor (An adept mathematician and statistician who has competed at an international level) Complete training – we will cover all major statistical topics and skills you need to become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist Extensive Case Studies that will help you reinforce everything you’ve learned Excellent support – if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day Dynamic – we don’t want to waste your time! The instructor sets a very good pace throughout the whole courseWhy do you need these skills?

Overview

Section 1: Introduction

Lecture 1 What does the course cover?

Lecture 2 Download all resources

Section 2: Sample or population data?

Lecture 3 Understanding the difference between a population and a sample

Section 3: The fundamentals of descriptive statistics

Lecture 5 Levels of measurement

Lecture 12 Cross tables and scatter plots

Lecture 13 Cross tables and scatter plots. Exercise

Lecture 14 The main measures of central tendency: mean, median and mode

Lecture 15 Mean, median and mode. Exercise

Lecture 16 Measuring skewness

Lecture 17 Skewness. Exercise

Lecture 24 The correlation coefficient

Lecture 25 Correlation coefficient

Section 5: Practical example: descriptive statistics

Lecture 26 Practical example

Lecture 27 Practical example: descriptive statistics

Section 6: Distributions

Lecture 28 Introduction to inferential statistics

Lecture 29 What is a distribution?

Lecture 30 The Normal distribution

Lecture 31 The standard normal distribution

Lecture 32 Standard Normal Distribution. Exercise

Lecture 33 Understanding the central limit theorem

Lecture 34 Standard error

Section 7: Estimators and estimates

Lecture 35 Working with estimators and estimates

Lecture 36 Confidence intervals – an invaluable tool for decision making

Lecture 39 Confidence interval clarifications

Lecture 40 Student’s T distribution

Lecture 43 What is a margin of error and why is it important in Statistics?

Section 8: Confidence intervals: advanced topics

Lecture 44 Calculating confidence intervals for two means with dependent samples

Lecture 45 Confidence intervals. Two means. Dependent samples. Exercise

Section 9: Practical example: inferential statistics

Lecture 51 Practical example: inferential statistics

Lecture 52 Practical example: inferential statistics

Section 10: Hypothesis testing: Introduction

Lecture 53 The null and the alternative hypothesis

Lecture 54 Further reading on null and alternative hypotheses

Lecture 55 Establishing a rejection region and a significance level

Lecture 56 Type I error vs Type II error

Lecture 59 What is the p-value and why is it one of the most useful tools for statisticians

Lecture 62 Test for the mean. Dependent samples

Lecture 63 Test for the mean. Dependent samples. Exercise

Section 12: Practical example: hypothesis testing

Lecture 68 Practical example: hypothesis testing

Lecture 69 Practical example: hypothesis testing

Section 13: The fundamentals of regression analysis

Lecture 70 Introduction to regression analysis

Lecture 71 Correlation and causation

Lecture 72 The linear regression model made easy

Lecture 73 What is the difference between correlation and regression?

Lecture 74 A geometrical representation of the linear regression model

Lecture 75 A practical example – Reinforced learning

Section 14: Subtleties of regression analysis

Lecture 76 Decomposing the linear regression model – understanding its nuts and bolts

Lecture 77 What is R-squared and how does it help us?

Lecture 78 The ordinary least squares setting and its practical applications

Lecture 79 Studying regression tables

Lecture 80 Regression tables. Exercise

Lecture 81 The multiple linear regression model

Lecture 82 The adjusted R-squared

Lecture 83 What does the F-statistic show us and why do we need to understand it?

Section 15: Assumptions for linear regression analysis

Lecture 84 OLS assumptions

Lecture 85 A1. Linearity

Lecture 86 A2. No endogeneity

Lecture 87 A3. Normality and homoscedasticity

Lecture 88 A4. No autocorrelation

Lecture 89 A5. No multicollinearity

Section 16: Dealing with categorical data

Section 17: Practical example: regression analysis

Lecture 91 Practical example: regression analysis

Section 18: Bonus lecture

Lecture 92 Bonus lecture: Next steps