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:
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?
Why do you need these skills?
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.
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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.
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.
In this lecture we show the other classification of variables - levels of measurement. We explore their similarities and differences.
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.
Following the categorization through the types of data, we look into the frequency distribution table for numerical variables.
Building up on the frequency distribution table, we learn how to illustrate it with a histogram.
In this lecture we explore the different ways to illustrate relationship between variables.
This lesson will introduce you to the three measures of central tendency - mean, median and mode.
In this lesson we show the he most commonly used tool to measure asymmetry is skewness.
We start exploring the most common measures of variablity. This lesson focuses on variance.
We build up on variance, by introducing standard deviation and the coefficient of variation.
We continue with the most common measure of interconnection between variables - the covariance.
Correlation coeffcient - the quantitative representation of correlation between variables.
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.
An introductory lesson that shows what is to follow in the section: inferental statistics.
We define what a distribution is, what types of distributions are there and how this helps us with statistics.
We introduce the Normal distribution and its great importance to statistics as a field.
We look into the Standard Normal distribution by deriving it from the Normal distribution. We elaborate on its use for testing.
The Central Limit Theorem - one of the most important statistical concepts. Definition and an example.
We introduce the standard error - an important ingredient for making predictions.
We explore the estimators and estimates and differentiate between the two concepts.
This is the heart of the section - confidence intervals.
We see our first example of the use of confidence intervals and the z-score.
A little story about the inception of the Student's T distribution - an important part of inference with small samples.
We combine our knowledge on confidence intervals with that on the Student's T distribution.
Understanding the margin of error and the effects of its different components on our confidence intervals.
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.
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