English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5 Hours | 2.81 GB
Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis
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?
- 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 automated
- 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
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!
Table of Contents
Introduction
1 What does the course cover?
2 Download all resources
Sample or population data?
3 Understanding the difference between a population and a sample
The fundamentals of descriptive statistics
4 The various types of data we can work with
5 Levels of measurement
6 Categorical variables. Visualization techniques for categorical variables
7 Categorical variables. Visualization techniques. Exercise
8 Numerical variables. Using a frequency distribution table
9 Numerical variables. Using a frequency distribution table. Exercise
10 Histogram charts
11 Histogram charts. Exercise
12 Cross tables and scatter plots
13 Cross tables and scatter plots. Exercise
Measures of central tendency, asymmetry, and variability
14 The main measures of central tendency: mean, median and mode
15 Mean, median and mode. Exercise
16 Measuring skewness
17 Skewness. Exercise
18 Measuring how data is spread out: calculating variance
19 Variance. Exercise
20 Standard deviation and coefficient of variation
21 Standard deviation and coefficient of variation. Exercise
22 Calculating and understanding covariance
23 Covariance. Exercise
24 The correlation coefficient
25 Correlation coefficient
Practical example: descriptive statistics
26 Practical example
27 Practical example: descriptive statistics
Distributions
28 Introduction to inferential statistics
29 What is a distribution?
30 The Normal distribution
31 The standard normal distribution
32 Standard Normal Distribution. Exercise
33 Understanding the central limit theorem
34 Standard error
Estimators and estimates
35 Working with estimators and estimates
36 Confidence intervals – an invaluable tool for decision making
37 Calculating confidence intervals within a population with a known variance
38 Confidence intervals. Population variance known. Exercise
39 Confidence interval clarifications
40 Student’s T distribution
41 Calculating confidence intervals within a population with an unknown variance
42 Population variance unknown. T-score. Exercise
43 What is a margin of error and why is it important in Statistics?
Confidence intervals: advanced topics
44 Calculating confidence intervals for two means with dependent samples
45 Confidence intervals. Two means. Dependent samples. Exercise
46 Calculating confidence intervals for two means with independent samples (part 1)
47 Confidence intervals. Two means. Independent samples (Part 1). Exercise
48 Calculating confidence intervals for two means with independent samples (part 2)
49 Confidence intervals. Two means. Independent samples (Part 2). Exercise
50 Calculating confidence intervals for two means with independent samples (part 3)
Practical example: inferential statistics
51 Practical example: inferential statistics
52 Practical example: inferential statistics
Hypothesis testing: Introduction
53 The null and the alternative hypothesis
54 Further reading on null and alternative hypotheses
55 Establishing a rejection region and a significance level
56 Type I error vs Type II error
Hypothesis testing: Let’s start testing!
57 Test for the mean. Population variance known
58 Test for the mean. Population variance known. Exercise
59 What is the p-value and why is it one of the most useful tools for statisticians
60 Test for the mean. Population variance unknown
61 Test for the mean. Population variance unknown. Exercise
62 Test for the mean. Dependent samples
63 Test for the mean. Dependent samples. Exercise
64 Test for the mean. Independent samples (Part 1)
65 Test for the mean. Independent samples (Part 1)
66 Test for the mean. Independent samples (Part 2)
67 Test for the mean. Independent samples (Part 2). Exercise
Practical example: hypothesis testing
68 Practical example: hypothesis testing
The fundamentals of regression analysis
69 Introduction to regression analysis
70 Correlation and causation
71 The linear regression model made easy
72 What is the difference between correlation and regression?
73 A geometrical representation of the linear regression model
74 A practical example – Reinforced learning
Subtleties of regression analysis
75 Decomposing the linear regression model – understanding its nuts and bolts
76 What is R-squared and how does it help us?
77 The ordinary least squares setting and its practical applications
78 Studying regression tables
79 Regression tables. Exercise
80 The multiple linear regression model
81 The adjusted R-squared
82 The adjusted R-squared
83 What does the F-statistic show us and why do we need to understand it?
Assumptions for linear regression analysis
84 OLS assumptions
85 A1. Linearity
86 A2. No endogeneity
87 A3. Normality and homoscedasticity
88 A4. No autocorrelation
89 A5. No multicollinearity
Dealing with categorical data
90 Dummy variables
Practical example: regression analysis
91 Practical example: regression analysis
Bonus lecture
92 Bonus lecture: Next steps
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