Statistics for Data Science and Business 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!
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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?
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
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
- 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!
Watch Online Statistics for Data Science and Business Analysis
# | 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 |