Statistics for Data Science and Business Analysis

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis

4.58 (42520 reviews)
Udemy
platform
English
language
Data Science
category
instructor
196,891
students
5 hours
content
Feb 2024
last update
$99.99
regular price

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!

Description

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!  

Content

Introduction

What does the course cover?
Download all resources

Sample or population data?

Understanding the difference between a population and a sample
Population vs sample

The fundamentals of descriptive statistics

The various types of data we can work with
Types of data
Levels of measurement
Levels of measurement
Categorical variables. Visualization techniques for categorical variables
Categorical variables. Visualization Techniques
Categorical variables. Visualization techniques. Exercise
Numerical variables. Using a frequency distribution table
Numerical variables. Using a frequency distribution table
Numerical variables. Using a frequency distribution table. Exercise
Histogram charts
Histogram charts
Histogram charts. Exercise
Cross tables and scatter plots
Cross Tables and Scatter Plots
Cross tables and scatter plots. Exercise

Measures of central tendency, asymmetry, and variability

The main measures of central tendency: mean, median and mode
Mean, median and mode. Exercise
Measuring skewness
Skewness
Skewness. Exercise
Measuring how data is spread out: calculating variance
Variance. Exercise
Standard deviation and coefficient of variation
Standard deviation
Standard deviation and coefficient of variation. Exercise
Calculating and understanding covariance
Covariance. Exercise
The correlation coefficient
Correlation
Correlation coefficient

Practical example: descriptive statistics

Practical example
Practical example: descriptive statistics

Distributions

Introduction to inferential statistics
What is a distribution?
What is a distribution
The Normal distribution
The Normal distribution
The standard normal distribution
The standard normal distribution
Standard Normal Distribution. Exercise
Understanding the central limit theorem
The central limit theorem
Standard error
Standard error

Estimators and estimates

Working with estimators and estimates
Estimators and estimates
Confidence intervals - an invaluable tool for decision making
Confidence intervals
Calculating confidence intervals within a population with a known variance
Confidence intervals. Population variance known. Exercise
Confidence interval clarifications
Student's T distribution
Student's T distribution
Calculating confidence intervals within a population with an unknown variance
Population variance unknown. T-score. Exercise
What is a margin of error and why is it important in Statistics?
Margin of error

Confidence intervals: advanced topics

Calculating confidence intervals for two means with dependent samples
Confidence intervals. Two means. Dependent samples. Exercise
Calculating confidence intervals for two means with independent samples (part 1)
Confidence intervals. Two means. Independent samples (Part 1). Exercise
Calculating confidence intervals for two means with independent samples (part 2)
Confidence intervals. Two means. Independent samples (Part 2). Exercise
Calculating confidence intervals for two means with independent samples (part 3)

Practical example: inferential statistics

Practical example: inferential statistics
Practical example: inferential statistics

Hypothesis testing: Introduction

The null and the alternative hypothesis
Further reading on null and alternative hypotheses
Null vs alternative
Establishing a rejection region and a significance level
Rejection region and significance level
Type I error vs Type II error
Type I error vs type II error

Hypothesis testing: Let's start testing!

Test for the mean. Population variance known
Test for the mean. Population variance known. Exercise
What is the p-value and why is it one of the most useful tools for statisticians
p-value
Test for the mean. Population variance unknown
Test for the mean. Population variance unknown. Exercise
Test for the mean. Dependent samples
Test for the mean. Dependent samples. Exercise
Test for the mean. Independent samples (Part 1)
Test for the mean. Independent samples (Part 1)
Test for the mean. Independent samples (Part 2)
Test for the mean. Independent samples (Part 2)
Test for the mean. Independent samples (Part 2). Exercise

Practical example: hypothesis testing

Practical example: hypothesis testing
Practical example: hypothesis testing

The fundamentals of regression analysis

Introduction to regression analysis
Introduction
Correlation and causation
Correlation and causation
The linear regression model made easy
The linear regression model
What is the difference between correlation and regression?
Correlation vs regression
A geometrical representation of the linear regression model
A geometrical representation of the linear regression model
A practical example - Reinforced learning

Subtleties of regression analysis

Decomposing the linear regression model - understanding its nuts and bolts
Decomposition
What is R-squared and how does it help us?
R-squared
The ordinary least squares setting and its practical applications
The ordinary least squares setting and its practical applications
Studying regression tables
Studying regression tables
Regression tables. Exercise
The multiple linear regression model
The multiple linear regression model
The adjusted R-squared
The adjusted R-squared
What does the F-statistic show us and why do we need to understand it?

Assumptions for linear regression analysis

OLS assumptions
OLS assumptions
A1. Linearity
A1. Linearity
A2. No endogeneity
A2. No endogeneity
A3. Normality and homoscedasticity
A3. Normality and homoscedasticity
A4. No autocorrelation
A4. No autocorrelation
A5. No multicollinearity
A5. No multicollinearity

Dealing with categorical data

Dummy variables

Practical example: regression analysis

Practical example: regression analysis

Bonus lecture

Bonus lecture: Next steps

Screenshots

Statistics for Data Science and Business Analysis - Screenshot_01Statistics for Data Science and Business Analysis - Screenshot_02Statistics for Data Science and Business Analysis - Screenshot_03Statistics for Data Science and Business Analysis - Screenshot_04

Reviews

Dayanand
November 14, 2023
Given the course's structure and content delivery, I strongly believe it is not well-suited for beginners. The foundational concepts were either glossed over or assumed as pre-known, making it challenging for someone new to the field to keep pace with the curriculum. Based on my experience, I would hesitate to recommend this course, especially to those who are at the beginning of their learning journey in this subject. The course's approach to advanced topics is not conducive to a comprehensive understanding, which is critical for anyone serious about mastering the material. For those considering this course, I would advise looking for alternatives that offer a more balanced and in-depth approach, especially if you are new to the field. The time investment required for this course might not yield the expected educational returns, particularly for beginners seeking a solid foundation in the subject matter.
Thomas
November 14, 2023
I don't think this course works. The teacher introduces new concepts in almost every lesson and doesn't provide enough exercises and explanations for them to sink in. They are used immediately in the next lesson, so I had a really hard time following. This is not so bad in the first 30% or so of the course, but becomes worse and worse as the course progresses. I can't say that I have grasped some of the basic concepts of statistics after this course. Sure, I know what is out there, but I feel far from being able to apply them. The course also makes use of excel without really explaining what they do. A simple "here's the core formulae to use" lesson would do the trick. Furthermore - and this is probably personal taste - I really disliked the animations and the way the course was presented. Why not show your face every now and then?
Satyam
November 12, 2023
It is a good starting point for people who want to understand and brush up statistics and data analysis.
Nisrina
November 11, 2023
so good to have this lecture, so compact to have refresher. I used to have this lecture for about 6 months, but having this course it is easy to understand and only took me to finish for about 2 days. thanks!
Sumit
October 31, 2023
the course is wonderful but it consist a little technical language. overall amazing experience, sure it will wrap around your head after repeating it once in a while.
Fabio
October 28, 2023
Just great! Having fun and learning a lot with the course. If you're searching for an introductory course on statistics to improve decision making, this is the one to go.
Clifford
October 26, 2023
Great course to understand the nuts and bolts of Statistics explained in clear and concise language which is not overtly technical or "Statistical" in nature. It clearly shows folks who are not originally from a Statistics' background can understand Statistics very well if there is an appetite to simplify the explanation of Statistical concepts tailored to fit everyone's needs.
Ramy
October 21, 2023
I am new to the field, however I enjoyed their method of presentation and explanations. No fluff in the course. However, I feel it is a little rushed in the end, we needed more examples of the Regression uses.
Yaraslau
October 17, 2023
A pretty good recollection of the statistical courses I was too lazy to study during my time at the university. Sometimes the underlying logic under the formulas is omitted and we are supposed to just take them as magic, which is a shame (sometimes the explanations are there and they are pretty great and make me understand the concepts much better). Also the quizzes are probably a bit too simple (just one straightforward question), although I might've just been paying enough attention to the lectures. But overall decent to get and/or recollect the foundations of statistics.
Nicole
September 1, 2023
as it goes on it becomes harder to follow because it doesn't walk me through the calculations. even in the exercises I don't know if I am doing them right.
Nicolas
August 31, 2023
Very thorough and well explained, mostly enjoyed the practical examples, which is what I will use to apply in real case scenarios.
Conor
August 28, 2023
This course was a fantastic way to improve my understanding of analytics for Games/Business. A lot of the topics covered in the later parts of the course are very theory and calculation-heavy. This can feel daunting to some but the videos are quite clear and the concepts in them are explained thoroughly. There are a few videos where I believe they could explain certain topics first, or condense subjects down a bit into a single video, so that figuring out the calculation for that particular section is a bit easier, but that's just my opinion. Highly recommend this course if you're a complete beginner or are someone who has dealt with data but doesn't know how to interpret it at a level that you are satisfied with.
Aisulu
August 23, 2023
Some of the topics seem poorly explained for the non-mathematical person and feel rushed. I had to go on YouTube in order to understand some of the concepts
Aryan
August 22, 2023
Even though I'm not into Data Science and Analysis, The contents were easy to understand along with great explanations and examples. Overall a great and fun Experience for me.
Katherine
August 20, 2023
Being new to statistics, I found the language challenging; the real-world examples and when he used plain language and provided exercises made the material more relatable.

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1298780
udemy ID
7/20/2017
course created date
7/30/2019
course indexed date
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