Statistics for Data Science and Business Analysis
4.6 (169 ratings)
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Statistics for Data Science and Business Analysis

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis
New
4.6 (169 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,562 students enrolled
Created by 365 Careers
Last updated 9/2017
English
Price: $150
30-Day Money-Back Guarantee
Includes:
  • 4 hours on-demand video
  • 83 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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!
View Curriculum
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
Description

Statistics is a driving force in the industry you want to enter? You want to work as a Marketing Analyst, or a Business Intelligence Analyst? Or, as 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!

This is where you start. And it is the perfect beginning!

In no time, you will acquire the fundamental skills that 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
  • Contains 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 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 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 for you. We want you to succeed, which is why the course tries 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 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 keeps up a very good pace throughout the whole course
  • Bonus prizes - upon completion of 50% and 100% of the course, you will receive two bonus gifts

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 list 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 challenge your existing skills and will 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.

Let's start learning together now!

Who is the target audience?
  • 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
Compare to Other Data Science Courses
Curriculum For This Course
60 Lectures
03:53:43
+
Introduction
1 Lecture 03:28
+
Sample or population data?
1 Lecture 03:56

The first step of every statistical analysis you will perform is to determine whether the data you are dealing with is a population or a sample. Furthermore, we need to know the difference between a random sample and a representative sample.

Preview 03:56

Population vs sample
2 questions
+
The fundamentals of descriptive statistics
6 Lectures 21:18

Before we can start testing we have to get acquainted with the types of variables, as different types of statistical tests, require different types of data.

Preview 03:18

Types of data
2 questions

In this lecture we show the other classification of variables - levels of measurement. We explore their similarities and differences.

Levels of measurement
02:57

Levels of measurement
2 questions

Following the knowledge on types of data, we look into techniques for visualizing categorical variables, namely frequency distribution tables, bar charts, pie charts and Pareto diagrams.

Categorical variables. Visualization techniques for categorical variables
04:06

Following the categorization through the types of data, we look into the frequency distribution table for numerical variables.

Numerical variables. Using a frequency distribution table
03:24

Building up on the frequency distribution table, we learn how to illustrate it with a histogram.

Histogram charts
02:27

Descriptive statistics.

In this lecture we explore the different ways to illustrate relationship between variables.

Cross tables and scatter plots
05:06
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Measures of central tendency, asymmetry, and variability
6 Lectures 25:49

This lesson will introduce you to the three measures of central tendency - mean, median and mode.

The main measures of central tendency: mean, median, mode
04:24

In this lesson we show the he most commonly used tool to measure asymmetry is skewness. 

Measuring skewness
02:43

We start exploring the most common measures of variablity. This lesson focuses on variance.

Measuring how data is spread out: calculating variance
05:58

We build up on variance, by introducing standard deviation and the coefficient of variation.

Standard deviation and coefficient of variation
04:54

We continue with the most common measure of interconnection between variables - the covariance.

Calculating and understanding covariance
03:31

Correlation coeffcient - the quantitative representation of correlation between variables.

The correlation coefficient
04:19
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Practical example: descriptive statistics
1 Lecture 14:35

This is the practical example on descriptive statistics. 

It's a hands-on activity covering all lessons so far - types of data; levels of measurement; graphs and tables for categorical and numerical variables, and relationship between variables; measures of central tendency, asymmetry, variability, and relationship between variables.

Practical example
14:35
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Distributions
6 Lectures 16:29

An introductory lesson that shows what is to follow in the section: inferental statistics.

Introduction to inferential statistics
01:14

We define what a distribution is, what types of distributions are there and how this helps us with statistics.

Preview 03:40

What is a distribution
1 question

We introduce the Normal distribution and its great importance to statistics as a field.

Preview 03:45

The Normal distribution
1 question

We look into the Standard Normal distribution by deriving it from the Normal distribution. We elaborate on its use for testing.

The standard normal distribution
02:51

The Central Limit Theorem - one of the most important statistical concepts. Definition and an example.

Understanding the central limit theorem
03:40

The central limit theorem
1 question

We introduce the standard error - an important ingredient for making predictions.

Standard error
01:19
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Estimators and estimates
6 Lectures 23:36

We explore the estimators and estimates and differentiate between the two concepts.

Working with estimators and estimates
02:36

Estimators and estimates
1 question

This is the heart of the section - confidence intervals.

Confidence intervals - an invaluable tool for decision making
02:30

Confidence intervals
1 question

We see our first example of the use of confidence intervals and the z-score.

Preview 06:31

A little story about the inception of the Student's T distribution - an important part of inference with small samples.

Preview 03:14

Student's T distribution
1 question

We combine our knowledge on confidence intervals with that on the Student's T distribution.

Calculating confidence intervals within a population with an unknown variance
04:07

Understanding the margin of error and the effects of its different components on our confidence intervals.


What is a margin of error and why is it important in Statistics?
04:38

Margin of error
1 question
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Confidence intervals: advanced topics
4 Lectures 14:27
Calculating confidence intervals for two means with dependent samples
04:47

Calculating confidence intervals for two means with independent samples (part 1)
04:36

Calculating confidence intervals for two means with independent samples (part 2)
03:40

Calculating confidence intervals for two means with independent samples (part 3)
01:24
+
Practical example: inferential statistics
1 Lecture 09:37

This is a practical example on inferential statistics.

It is looking into the sales of a shoe shop. We explore the sales of different products and shops, using the material we have seen so far in the section.

Practical example: inferential statistics
09:37
+
Hypothesis testing: Introduction
3 Lectures 12:36

Null vs alternative
3 questions

Establishing a rejection region and a significance level
04:20

Rejection region and significance level
2 questions

Type I error vs Type II error
03:20

Type I error vs type II error
4 questions
4 More Sections
About the Instructor
365 Careers
4.5 Average rating
11,438 Reviews
83,806 Students
20 Courses
Creating opportunities for Business & Finance students

365 Careers is a firm specializing in high-end business, financial, data science, and office producitvity training programs. Our growing list of courses includes Excel (Microsoft Excel for Beginner and Advanced users), PowerPoint, Word, Outlook, Accounting, Finance 101, Investment Banking, Financial Modeling, Company Valuation, Financial Planning & Analysis, Job Hunting, Strategy, Management, Marketing, Decision Making and Negotiation, and Python trainings. Our goal is to provide to our students the practical instruments they will need in order to perform successfully at their future workplace.