Statistics for Data Science and Business Analysis
4.5 (16,489 ratings)
76,939 students enrolled

# Statistics for Data Science and Business Analysis

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis
Bestseller
4.5 (16,489 ratings)
76,939 students enrolled
Last updated 6/2020
English
English [Auto], French [Auto], 7 more
Current price: \$96.99 Original price: \$149.99 Discount: 35% off
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This course includes
• 5 hours on-demand video
• 28 articles
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

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!
Course content
Expand all 92 lectures 04:51:43
+ Introduction
2 lectures 04:10

00:16
+ Sample or population data?
1 lecture 04:02

The first step of every statistical analysis you will perform is to determine whether the data you are dealing with is a population or a sample. Furthermore, we need to know the difference between a random sample and a representative sample.

Preview 04:02
Population vs sample
2 questions
+ The fundamentals of descriptive statistics
10 lectures 23:28

Before we can start testing we have to get acquainted with the types of variables, as different types of statistical tests and visualizations, require different types of data.

Preview 04:33
Types of data
2 questions

In this lecture we show the other classification of variables - levels of measurement

Levels of measurement
03:43
Levels of measurement
2 questions

Following the knowledge on types of data, we look into techniques for visualizing categorical variables, namely frequency distribution tables, bar charts, pie charts and Pareto diagrams.

Categorical variables. Visualization techniques for categorical variables
04:52
Categorical variables. Visualization Techniques
1 question

Exercises on visualization techniques for categorical variables.

Categorical variables. Visualization techniques. Exercise
00:03

Following the categorization through the types of data, we look into the frequency distribution table for numerical variables.

Numerical variables. Using a frequency distribution table
03:09
Numerical variables. Using a frequency distribution table
1 question

Exercise on frequency distribution table for numerical variables.

Numerical variables. Using a frequency distribution table. Exercise
00:03

Building up on the frequency distribution table, we learn how to illustrate data with histograms.

Histogram charts
02:14
Histogram charts
1 question

Exercise on histograms.

Histogram charts. Exercise
00:03

Descriptive statistics.

In this lecture we explore the different ways to demonstrate relationship between variables.

Cross tables and scatter plots
04:44
Cross Tables and Scatter Plots
1 question

Exercise on cross tables and scatter plots.

Cross tables and scatter plots. Exercise
00:03
+ Measures of central tendency, asymmetry, and variability
12 lectures 24:31

This lesson will introduce you to the three measures of central tendency - mean, median and mode.

The main measures of central tendency: mean, median and mode
04:20

Exercise on the measures of central tendency.

Mean, median and mode. Exercise
00:03

In this lesson we show the most commonly used tool to measure asymmetry - skewness, and its relationship with the mean, median, and mode.

Measuring skewness
02:37
Skewness
1 question

An exercise on skewness.

Skewness. Exercise
00:03

We start exploring the most common measures of variablity. This lesson focuses on variance.

Measuring how data is spread out: calculating variance
05:55

An exercise on variance.

Variance. Exercise
00:03

We build up on variance, by introducing standard deviation and the coefficient of variation.

Standard deviation and coefficient of variation
04:40
Standard deviation
1 question

An exercise on standard deviation and coefficient of variation.

Standard deviation and coefficient of variation. Exercise
00:03

We continue with the most common measure of interconnection between variables: covariance.

Calculating and understanding covariance
03:23

An exercise on covariance.

Covariance. Exercise
00:03

Correlation coeffcient - the quantitative representation of correlation between variables.

The correlation coefficient
03:17
Correlation
2 questions

An exercise on the correlation coefficient.

Correlation coefficient
00:03
+ Practical example: descriptive statistics
2 lectures 16:18

This is the practical example on descriptive statistics.

It's a hands-on activity covering all lessons so far - types of data; levels of measurement; graphs and tables for categorical and numerical variables, and relationship between variables; measures of central tendency, asymmetry, variability, and relationship between variables.

We apply all the acquired knowledge on a real-life data for a real estate company and create business analytics from scratch.

Practical example
16:15

Exercises based on the practical example.

Practical example: descriptive statistics
00:03
+ Distributions
7 lectures 18:46

An introductory lesson that shows what is to follow in the section inferental statistics.

Introduction to inferential statistics
01:00

We explain what a distribution is, what types of distributions are there and how this helps us to better understand statistics.

Preview 04:33
What is a distribution
1 question

We introduce the Normal distribution and its great importance to statistics as a field.

Preview 03:54
The Normal distribution
1 question

We look into the Standard Normal distribution by deriving it from the Normal distribution, through the method of standardization. We elaborate on its use for testing.

The standard normal distribution
03:30
The standard normal distribution
1 question

An exercise on the Standard Normal Distribution.

Standard Normal Distribution. Exercise
00:03

The Central Limit Theorem - one of the most important statistical concepts. Definition and an example.

Understanding the central limit theorem
04:20
The central limit theorem
1 question

We introduce the standard error - an important ingredient for making predictions.

Standard error
01:26
Standard error
1 question
+ Estimators and estimates
9 lectures 31:23

We explore the estimators and estimates, and differentiate between the two concepts.

Working with estimators and estimates
03:07
Estimators and estimates
1 question

This is the heart of the section - confidence intervals.

Confidence intervals - an invaluable tool for decision making
02:41
Confidence intervals
1 question

We see our first example of the use of confidence intervals and introduce the concept of the z-score.

Preview 08:01

An exercise on confidence intervals.

Confidence intervals. Population variance known. Exercise
00:03

Following several questions in the Q&A sections we have decided to add a lecture which digs a bit deeper into what confidence intervals are.

Confidence interval clarifications
04:38

A little story about the inception of the Student's T distribution - a valuable tool when working with small samples.

Preview 03:22
Student's T distribution
1 question

We combine our knowledge on confidence intervals with that on the Student's T distribution, by making inferences using a small sample.

Calculating confidence intervals within a population with an unknown variance
04:36

An exercise on confidence intervals, when population variance is uknown.

Population variance unknown. T-score. Exercise
00:03

A deeper dive into the drivers of confidence intervals through the margin of error.

What is a margin of error and why is it important in Statistics?
04:52
Margin of error
1 question
7 lectures 16:08

We show real life examples of confidence intervals. In this lesson, we focus on dependent samples, which are often found in medicine.

Calculating confidence intervals for two means with dependent samples
06:04

An exercise on confidence intervals for two means (dependent samples).

Confidence intervals. Two means. Dependent samples. Exercise
00:03

We carry on with the applications. This time the example is with independent samples, where the population variance is known.

Calculating confidence intervals for two means with independent samples (part 1)
04:31

An exercise on confidence intervals for two means (independent samples).

Confidence intervals. Two means. Independent samples (Part 1). Exercise
00:03

More often than not, we do not know the population variance, as it is too costly (or impossible) to have data on the whole population. We explore how to deal with the problem, through sample variance. We start from the simpler case, where we assume that the variance of the two samples is equal.

Calculating confidence intervals for two means with independent samples (part 2)
03:57

An exercise on confidence intervals for two means (independent samples).

Confidence intervals. Two means. Independent samples (Part 2). Exercise
00:03

We conclude the section on confidence intervals with the example on independent samples, where the variance is unknown and assumed to be different. That is the most common case.

Calculating confidence intervals for two means with independent samples (part 3)
01:27
+ Practical example: inferential statistics
2 lectures 10:08

This is a practical example on inferential statistics.

We apply all the knowledge we have on descriptive statistics and inferential so far.

The data is based on purchases in a shoe shop. We explore the sales of different products and shops, and try to manage the inventory of our company better.

Practical example: inferential statistics
10:05

This is a practical example on inferential statistics.

We apply all the knowledge we have on descriptive statistics and inferential so far.

The data is based on purchases in a shoe shop. We explore the sales of different products and shops, and try to manage the inventory of our company better.

Please find an exercise file and a solution file attached to this lecture.

Practical example: inferential statistics
00:03
+ Hypothesis testing: Introduction
4 lectures 18:26

Hypothesis testing is the heart of statistics. We start from the very basics: what are the null and alternative hypotheses. We show different examples and explain how to form hypotheses that are later to be tested.

Preview 05:51
Further reading on null and alternative hypotheses
01:16
Null vs alternative
3 questions

Whenever we do hypothesis testing, we either accept or reject a hypothesis. In this lecture, we explain the rationale behind testing.

Establishing a rejection region and a significance level
07:05
Rejection region and significance level
2 questions

There are two errors one can make when testing - false positive and false negative. In order to be better statisticians, we must be acquainted with those issues.

Type I error vs Type II error
04:14
Type I error vs type II error
4 questions
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

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?

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 hard for over four 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 course

Why do you need these skills?

1. 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

2. 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

3. 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 automated

4. Growth - 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.

Let's start learning together now!

Who this course is for:
• People who want a career in Data Science
• People who want a career in Business Intelligence