Statistics for Data Science and Business Analysis

4h 49m 30s
English
Paid
November 22, 2024

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!  

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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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# Title Duration
1 What does the course cover? 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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