**Statistics for Data Science and Business Analysis**

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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