Statistics For Data Science And Business Analysis

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

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!

Requirements

Absolutely no experience is required. We will start from the basics and gradually build up your knowledge. Everything is in the course.

A willingness to learn and practice

Description

Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist?And you want to acquire the quantitative skills needed for the job?Well then, you’ve come to the right place!    Statistics for Data Science and Business Analysis is here for you! (with TEMPLATES in Excel included)   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. We have created a course that is:    Easy to understand

  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

  It is no secret that a lot of these topics have been explained online. Thousands of times. However, it is next to impossible to find a structured program that gives you an understanding of why certain statistical tests are being used so often. Modern software packages and programming languages are automating most of these activities, but this course gives you something more valuable – critical thinking abilities. Computers and programming languages are like ships at sea. They are fine vessels that will carry you to the desired destination, but it is up to you, the aspiring data scientist or BI analyst, to navigate and point them in the right direction.    Teaching is our passion

  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?

  Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow    Promotions – If you understand Statistics well, you will be able to back up your business ideas with quantitative evidence, which is an easy path to career growth   Secure Future – as we said, the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, data science careers are the ones doing the automating, not getting automatedGrowth - this isn’t a boring job. Every day, you will face different challenges that will test your existing skills and require you to learn something new    Please bear in mind that the course comes with Udemy’s 30-day unconditional money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you.

  Click 'Buy now' and let's start learning together today!

 

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 4 The various types of data we can work with

Lecture 5 Levels of measurement

Lecture 6 Categorical variables. Visualization techniques for categorical variables

Lecture 7 Categorical variables. Visualization techniques. Exercise

Lecture 8 Numerical variables. Using a frequency distribution table

Lecture 9 Numerical variables. Using a frequency distribution table. Exercise

Lecture 10 Histogram charts

Lecture 11 Histogram charts. Exercise

Lecture 12 Cross tables and scatter plots

Lecture 13 Cross tables and scatter plots. Exercise

Section 4: Measures of central tendency, asymmetry, and variability

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 18 Measuring how data is spread out: calculating variance

Lecture 19 Variance. Exercise

Lecture 20 Standard deviation and coefficient of variation

Lecture 21 Standard deviation and coefficient of variation. Exercise

Lecture 22 Calculating and understanding covariance

Lecture 23 Covariance. 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 37 Calculating confidence intervals within a population with a known variance

Lecture 38 Confidence intervals. Population variance known. Exercise

Lecture 39 Confidence interval clarifications

Lecture 40 Student's T distribution

Lecture 41 Calculating confidence intervals within a population with an unknown variance

Lecture 42 Population variance unknown. T-score. Exercise

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

Lecture 46 Calculating confidence intervals for two means with independent samples (part 1)

Lecture 47 Confidence intervals. Two means. Independent samples (Part 1). Exercise

Lecture 48 Calculating confidence intervals for two means with independent samples (part 2)

Lecture 49 Confidence intervals. Two means. Independent samples (Part 2). Exercise

Lecture 50 Calculating confidence intervals for two means with independent samples (part 3)

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

Section 11: Hypothesis testing: Let's start testing!

Lecture 57 Test for the mean. Population variance known

Lecture 58 Test for the mean. Population variance known. Exercise

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

Lecture 60 Test for the mean. Population variance unknown

Lecture 61 Test for the mean. Population variance unknown. Exercise

Lecture 62 Test for the mean. Dependent samples

Lecture 63 Test for the mean. Dependent samples. Exercise

Lecture 64 Test for the mean. Independent samples (Part 1)

Lecture 65 Test for the mean. Independent samples (Part 1)

Lecture 66 Test for the mean. Independent samples (Part 2)

Lecture 67 Test for the mean. Independent samples (Part 2). 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

Lecture 90 Dummy variables

Section 17: Practical example: regression analysis

Lecture 91 Practical example: regression analysis

Section 18: Bonus lecture

Lecture 92 Bonus lecture: Next steps

People who want a career in Data Science,People who want a career in Business Intelligence,Business analysts,Business executives,Individuals who are passionate about numbers and quant analysis,Anyone who wants to learn the subtleties of Statistics and how it is used in the business world,People who want to start learning statistics,People who want to learn the fundamentals of statistics