Mô tả

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 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.  

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

Bạn sẽ học được gì

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!

Yêu cầu

  • 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

Nội dung khoá học

18 sections

Introduction

2 lectures
What does the course cover?
03:54
Download all resources
00:16

Sample or population data?

2 lectures
Understanding the difference between a population and a sample
04:02
Population vs sample
2 questions

The fundamentals of descriptive statistics

16 lectures
The various types of data we can work with
04:33
Types of data
2 questions
Levels of measurement
03:43
Levels of measurement
2 questions
Categorical variables. Visualization techniques for categorical variables
04:52
Categorical variables. Visualization Techniques
1 question
Categorical variables. Visualization techniques. Exercise
00:03
Numerical variables. Using a frequency distribution table
03:09
Numerical variables. Using a frequency distribution table
1 question
Numerical variables. Using a frequency distribution table. Exercise
00:03
Histogram charts
02:14
Histogram charts
1 question
Histogram charts. Exercise
00:03
Cross tables and scatter plots
04:44
Cross Tables and Scatter Plots
1 question
Cross tables and scatter plots. Exercise
00:03

Measures of central tendency, asymmetry, and variability

15 lectures
The main measures of central tendency: mean, median and mode
04:20
Mean, median and mode. Exercise
00:03
Measuring skewness
02:37
Skewness
1 question
Skewness. Exercise
00:03
Measuring how data is spread out: calculating variance
05:55
Variance. Exercise
00:03
Standard deviation and coefficient of variation
04:40
Standard deviation
1 question
Standard deviation and coefficient of variation. Exercise
00:03
Calculating and understanding covariance
03:23
Covariance. Exercise
00:03
The correlation coefficient
03:17
Correlation
2 questions
Correlation coefficient
00:03

Practical example: descriptive statistics

2 lectures
Practical example
16:15
Practical example: descriptive statistics
00:03

Distributions

12 lectures
Introduction to inferential statistics
01:00
What is a distribution?
04:33
What is a distribution
1 question
The Normal distribution
03:54
The Normal distribution
1 question
The standard normal distribution
03:30
The standard normal distribution
1 question
Standard Normal Distribution. Exercise
00:03
Understanding the central limit theorem
04:20
The central limit theorem
1 question
Standard error
01:27
Standard error
1 question

Estimators and estimates

13 lectures
Working with estimators and estimates
03:07
Estimators and estimates
1 question
Confidence intervals - an invaluable tool for decision making
02:41
Confidence intervals
1 question
Calculating confidence intervals within a population with a known variance
08:01
Confidence intervals. Population variance known. Exercise
00:03
Confidence interval clarifications
04:38
Student's T distribution
03:22
Student's T distribution
1 question
Calculating confidence intervals within a population with an unknown variance
04:36
Population variance unknown. T-score. Exercise
00:03
What is a margin of error and why is it important in Statistics?
04:52
Margin of error
1 question

Confidence intervals: advanced topics

7 lectures
Calculating confidence intervals for two means with dependent samples
06:04
Confidence intervals. Two means. Dependent samples. Exercise
00:03
Calculating confidence intervals for two means with independent samples (part 1)
04:31
Confidence intervals. Two means. Independent samples (Part 1). Exercise
00:03
Calculating confidence intervals for two means with independent samples (part 2)
03:57
Confidence intervals. Two means. Independent samples (Part 2). Exercise
00:03
Calculating confidence intervals for two means with independent samples (part 3)
01:27

Practical example: inferential statistics

2 lectures
Practical example: inferential statistics
10:06
Practical example: inferential statistics
00:03

Hypothesis testing: Introduction

7 lectures
The null and the alternative hypothesis
05:52
Further reading on null and alternative hypotheses
01:16
Null vs alternative
3 questions
Establishing a rejection region and a significance level
07:05
Rejection region and significance level
2 questions
Type I error vs Type II error
04:14
Type I error vs type II error
4 questions

Hypothesis testing: Let's start testing!

13 lectures
Test for the mean. Population variance known
06:34
Test for the mean. Population variance known. Exercise
00:03
What is the p-value and why is it one of the most useful tools for statisticians
04:13
p-value
4 questions
Test for the mean. Population variance unknown
04:48
Test for the mean. Population variance unknown. Exercise
00:03
Test for the mean. Dependent samples
05:18
Test for the mean. Dependent samples. Exercise
00:03
Test for the mean. Independent samples (Part 1)
04:22
Test for the mean. Independent samples (Part 1)
00:03
Test for the mean. Independent samples (Part 2)
04:26
Test for the mean. Independent samples (Part 2)
1 question
Test for the mean. Independent samples (Part 2). Exercise
00:03

Practical example: hypothesis testing

2 lectures
Practical example: hypothesis testing
07:16
Practical example: hypothesis testing
00:03

The fundamentals of regression analysis

11 lectures
Introduction to regression analysis
01:02
Introduction
1 question
Correlation and causation
04:12
Correlation and causation
1 question
The linear regression model made easy
05:50
The linear regression model
2 questions
What is the difference between correlation and regression?
01:43
Correlation vs regression
1 question
A geometrical representation of the linear regression model
01:25
A geometrical representation of the linear regression model
1 question
A practical example - Reinforced learning
05:45

Subtleties of regression analysis

14 lectures
Decomposing the linear regression model - understanding its nuts and bolts
03:37
Decomposition
1 question
What is R-squared and how does it help us?
05:24
R-squared
2 questions
The ordinary least squares setting and its practical applications
02:23
The ordinary least squares setting and its practical applications
1 question
Studying regression tables
04:54
Studying regression tables
3 questions
Regression tables. Exercise
00:03
The multiple linear regression model
02:55
The multiple linear regression model
1 question
The adjusted R-squared
05:24
The adjusted R-squared
3 questions
What does the F-statistic show us and why do we need to understand it?
02:01

Assumptions for linear regression analysis

12 lectures
OLS assumptions
02:21
OLS assumptions
1 question
A1. Linearity
01:50
A1. Linearity
1 question
A2. No endogeneity
04:09
A2. No endogeneity
1 question
A3. Normality and homoscedasticity
05:47
A3. Normality and homoscedasticity
1 question
A4. No autocorrelation
03:14
A4. No autocorrelation
2 questions
A5. No multicollinearity
03:26
A5. No multicollinearity
1 question

Dealing with categorical data

1 lectures
Dummy variables
05:03

Practical example: regression analysis

1 lectures
Practical example: regression analysis
14:09

Bonus lecture

1 lectures
Bonus lecture: Next steps
01:02

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