Statistics for Data Science and Business Analysis

Statistics for Data Science and Business Analysis, available at $99.99, has an average rating of 4.53, with 92 lectures, 42 quizzes, based on 44807 reviews, and has 206818 subscribers.

You will learn about 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! This course is ideal for individuals who are People who want a career in Data Science or People who want a career in Business Intelligence or Business analysts or Business executives or Individuals who are passionate about numbers and quant analysis or Anyone who wants to learn the subtleties of statistics and how it is used in the business world or People who want to start learning statistics or People who want to learn the fundamentals of statistics It is particularly useful for People who want a career in Data Science or People who want a career in Business Intelligence or Business analysts or Business executives or Individuals who are passionate about numbers and quant analysis or Anyone who wants to learn the subtleties of statistics and how it is used in the business world or People who want to start learning statistics or People who want to learn the fundamentals of statistics.

Enroll now: Statistics for Data Science and Business Analysis

Summary

Title: Statistics for Data Science and Business Analysis

Price: $99.99

Average Rating: 4.53

Number of Lectures: 92

Number of Quizzes: 42

Number of Published Lectures: 92

Number of Published Quizzes: 41

Number of Curriculum Items: 134

Number of Published Curriculum Objects: 133

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will 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!

Who Should Attend

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

Target Audiences

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

Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist?

And you want to acquire the quantitative skills needed for the job?

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 full-time for several 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   

Please bear in mind that the course comes with Udemy’s 30-day unconditional money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you.

 

Click ‘Buy now’ and let’s start learning together today!

 

Course Curriculum

Chapter 1: Introduction

Lecture 1: What does the course cover?

Lecture 2: Download all resources

Chapter 2: Sample or population data?

Lecture 1: Understanding the difference between a population and a sample

Chapter 3: The fundamentals of descriptive statistics

Lecture 1: The various types of data we can work with

Lecture 2: Levels of measurement

Lecture 3: Categorical variables. Visualization techniques for categorical variables

Lecture 4: Categorical variables. Visualization techniques. Exercise

Lecture 5: Numerical variables. Using a frequency distribution table

Lecture 6: Numerical variables. Using a frequency distribution table. Exercise

Lecture 7: Histogram charts

Lecture 8: Histogram charts. Exercise

Lecture 9: Cross tables and scatter plots

Lecture 10: Cross tables and scatter plots. Exercise

Chapter 4: Measures of central tendency, asymmetry, and variability

Lecture 1: The main measures of central tendency: mean, median and mode

Lecture 2: Mean, median and mode. Exercise

Lecture 3: Measuring skewness

Lecture 4: Skewness. Exercise

Lecture 5: Measuring how data is spread out: calculating variance

Lecture 6: Variance. Exercise

Lecture 7: Standard deviation and coefficient of variation

Lecture 8: Standard deviation and coefficient of variation. Exercise

Lecture 9: Calculating and understanding covariance

Lecture 10: Covariance. Exercise

Lecture 11: The correlation coefficient

Lecture 12: Correlation coefficient

Chapter 5: Practical example: descriptive statistics

Lecture 1: Practical example

Lecture 2: Practical example: descriptive statistics

Chapter 6: Distributions

Lecture 1: Introduction to inferential statistics

Lecture 2: What is a distribution?

Lecture 3: The Normal distribution

Lecture 4: The standard normal distribution

Lecture 5: Standard Normal Distribution. Exercise

Lecture 6: Understanding the central limit theorem

Lecture 7: Standard error

Chapter 7: Estimators and estimates

Lecture 1: Working with estimators and estimates

Lecture 2: Confidence intervals – an invaluable tool for decision making

Lecture 3: Calculating confidence intervals within a population with a known variance

Lecture 4: Confidence intervals. Population variance known. Exercise

Lecture 5: Confidence interval clarifications

Lecture 6: Students T distribution

Lecture 7: Calculating confidence intervals within a population with an unknown variance

Lecture 8: Population variance unknown. T-score. Exercise

Lecture 9: What is a margin of error and why is it important in Statistics?

Chapter 8: Confidence intervals: advanced topics

Lecture 1: Calculating confidence intervals for two means with dependent samples

Lecture 2: Confidence intervals. Two means. Dependent samples. Exercise

Lecture 3: Calculating confidence intervals for two means with independent samples (part 1)

Lecture 4: Confidence intervals. Two means. Independent samples (Part 1). Exercise

Lecture 5: Calculating confidence intervals for two means with independent samples (part 2)

Lecture 6: Confidence intervals. Two means. Independent samples (Part 2). Exercise

Lecture 7: Calculating confidence intervals for two means with independent samples (part 3)

Chapter 9: Practical example: inferential statistics

Lecture 1: Practical example: inferential statistics

Lecture 2: Practical example: inferential statistics

Chapter 10: Hypothesis testing: Introduction

Lecture 1: The null and the alternative hypothesis

Lecture 2: Further reading on null and alternative hypotheses

Lecture 3: Establishing a rejection region and a significance level

Lecture 4: Type I error vs Type II error

Chapter 11: Hypothesis testing: Lets start testing!

Lecture 1: Test for the mean. Population variance known

Lecture 2: Test for the mean. Population variance known. Exercise

Lecture 3: What is the p-value and why is it one of the most useful tools for statisticians

Lecture 4: Test for the mean. Population variance unknown

Lecture 5: Test for the mean. Population variance unknown. Exercise

Lecture 6: Test for the mean. Dependent samples

Lecture 7: Test for the mean. Dependent samples. Exercise

Lecture 8: Test for the mean. Independent samples (Part 1)

Lecture 9: Test for the mean. Independent samples (Part 1)

Lecture 10: Test for the mean. Independent samples (Part 2)

Instructors

365 Careers
Creating opportunities for Data Science and Finance students

Rating Distribution

1 stars: 263 votes

2 stars: 584 votes

3 stars: 4149 votes

4 stars: 16218 votes

5 stars: 23593 votes

Frequently Asked Questions

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