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:
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Easy to understand
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Comprehensive
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Practical
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To the point
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Packed with plenty of exercises and resources
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Data-driven
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Introduces you to the statistical scientific lingo
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Teaches you about data visualization
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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?
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High-quality production – HD video and animations (This isn’t a collection of boring lectures!)
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Knowledgeable instructor (An adept mathematician and statistician who has competed at an international level)
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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
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Extensive Case Studies that will help you reinforce everything you’ve learned
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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
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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?
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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
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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
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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
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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: Student's 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: Let's 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
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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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