Statistics for Data Science and Business Analysis

4h 49m 30s
English
Paid

Course 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!  

Read more about the course

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   

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

Who this course is for:
  • 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

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!

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#1: What does the course cover?

All Course Lessons (64)

#Lesson TitleDurationAccess
1
What does the course cover? Demo
03:55
2
Understanding the difference between a population and a sample
04:03
3
The various types of data we can work with
04:34
4
Levels of measurement
03:44
5
Categorical variables. Visualization techniques for categorical variables
04:53
6
Numerical variables. Using a frequency distribution table
03:10
7
Histogram charts
02:15
8
Cross tables and scatter plots
04:45
9
The main measures of central tendency: mean, median and mode
04:21
10
Measuring skewness
02:38
11
Measuring how data is spread out: calculating variance
05:56
12
Standard deviation and coefficient of variation
04:41
13
Calculating and understanding covariance
03:24
14
The correlation coefficient
03:18
15
Practical example
16:16
16
Introduction to inferential statistics
01:01
17
What is a distribution?
04:34
18
The Normal distribution
03:55
19
The standard normal distribution
03:31
20
Understanding the central limit theorem
04:21
21
Standard error
01:28
22
Working with estimators and estimates
03:08
23
Confidence intervals - an invaluable tool for decision making
02:42
24
Calculating confidence intervals within a population with a known variance
08:02
25
Confidence interval clarifications
04:39
26
Student's T distribution
03:23
27
Calculating confidence intervals within a population with an unknown variance
04:37
28
What is a margin of error and why is it important in Statistics?
04:53
29
Calculating confidence intervals for two means with dependent samples
06:05
30
Calculating confidence intervals for two means with independent samples (part 1)
04:32
31
Calculating confidence intervals for two means with independent samples (part 2)
03:58
32
Calculating confidence intervals for two means with independent samples (part 3)
01:28
33
Practical example: inferential statistics
10:07
34
The null and the alternative hypothesis
05:53
35
Establishing a rejection region and a significance level
07:06
36
Type I error vs Type II error
04:15
37
Test for the mean. Population variance known
06:35
38
What is the p-value and why is it one of the most useful tools for statisticians
04:14
39
Test for the mean. Population variance unknown
04:49
40
Test for the mean. Dependent samples
05:19
41
Test for the mean. Independent samples (Part 1)
04:23
42
Test for the mean. Independent samples (Part 2)
04:27
43
Practical example: hypothesis testing
07:17
44
Introduction to regression analysis
01:03
45
Correlation and causation
04:13
46
The linear regression model made easy
05:51
47
What is the difference between correlation and regression?
01:44
48
A geometrical representation of the linear regression model
01:26
49
A practical example - Reinforced learning
05:46
50
Decomposing the linear regression model - understanding its nuts and bolts
03:38
51
What is R-squared and how does it help us?
05:25
52
The ordinary least squares setting and its practical applications
02:24
53
Studying regression tables
04:55
54
The multiple linear regression model
02:56
55
The adjusted R-squared
05:25
56
What does the F-statistic show us and why do we need to understand it?
02:02
57
OLS assumptions
02:22
58
A1. Linearity
01:51
59
A2. No endogeneity
04:10
60
A3. Normality and homoscedasticity
05:48
61
A4. No autocorrelation
03:15
62
A5. No multicollinearity
03:27
63
Dummy variables
05:04
64
Practical example: regression analysis
14:10

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