Statistics for Data Science and Business Analysis
- Description
- Curriculum
- FAQ
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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!
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Understanding the difference between a population and a sampleThe 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.
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Population vs sample
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The various types of data we can work with
Before we can start testing we have to get acquainted with the types of variables, as different types of statistical tests and visualizations, require different types of data.
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Types of data
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Levels of measurement
In this lecture we show the other classification of variables - levels of measurement
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Levels of measurement
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Categorical variables. Visualization techniques for categorical variables
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.
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Categorical variables. Visualization Techniques
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Categorical variables. Visualization techniques. ExerciseExercises on visualization techniques for categorical variables.
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Numerical variables. Using a frequency distribution table
Following the categorization through the types of data, we look into the frequency distribution table for numerical variables.
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Numerical variables. Using a frequency distribution table
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Numerical variables. Using a frequency distribution table. Exercise
Exercise on frequency distribution table for numerical variables.
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Histogram charts
Building up on the frequency distribution table, we learn how to illustrate data with histograms.
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Histogram charts
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Histogram charts. Exercise
Exercise on histograms.
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Cross tables and scatter plots
Descriptive statistics.
In this lecture we explore the different ways to demonstrate relationship between variables.
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Cross Tables and Scatter Plots
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Cross tables and scatter plots. Exercise
Exercise on cross tables and scatter plots.
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The main measures of central tendency: mean, median and mode
This lesson will introduce you to the three measures of central tendency - mean, median and mode.
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Mean, median and mode. Exercise
Exercise on the measures of central tendency.
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Measuring skewness
In this lesson we show the most commonly used tool to measure asymmetry - skewness, and its relationship with the mean, median, and mode.
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Skewness
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Skewness. Exercise
An exercise on skewness.
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Measuring how data is spread out: calculating variance
We start exploring the most common measures of variablity. This lesson focuses on variance.
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Variance. Exercise
An exercise on variance.
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Standard deviation and coefficient of variation
We build up on variance, by introducing standard deviation and the coefficient of variation.
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Standard deviation
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Standard deviation and coefficient of variation. Exercise
An exercise on standard deviation and coefficient of variation.
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Calculating and understanding covariance
We continue with the most common measure of interconnection between variables: covariance.
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Covariance. Exercise
An exercise on covariance.
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The correlation coefficient
Correlation coeffcient - the quantitative representation of correlation between variables.
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Correlation
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Correlation coefficient
An exercise on the correlation coefficient.
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Practical example
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.
We apply all the acquired knowledge on a real-life data for a real estate company and create business analytics from scratch.
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Practical example: descriptive statistics
Exercises based on the practical example.
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Introduction to inferential statistics
An introductory lesson that shows what is to follow in the section inferental statistics.
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What is a distribution?
We explain what a distribution is, what types of distributions are there and how this helps us to better understand statistics.
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What is a distribution
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The Normal distribution
We introduce the Normal distribution and its great importance to statistics as a field.
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The Normal distribution
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The standard normal distribution
We look into the Standard Normal distribution by deriving it from the Normal distribution, through the method of standardization. We elaborate on its use for testing.
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The standard normal distribution
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Standard Normal Distribution. Exercise
An exercise on the Standard Normal Distribution.
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Understanding the central limit theorem
The Central Limit Theorem - one of the most important statistical concepts. Definition and an example.
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The central limit theorem
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Standard error
We introduce the standard error - an important ingredient for making predictions.
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Standard error
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Working with estimators and estimates
We explore the estimators and estimates, and differentiate between the two concepts.
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Estimators and estimates
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Confidence intervals - an invaluable tool for decision making
This is the heart of the section - confidence intervals.
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Confidence intervals
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Calculating confidence intervals within a population with a known variance
We see our first example of the use of confidence intervals and introduce the concept of the z-score.
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Confidence intervals. Population variance known. Exercise
An exercise on confidence intervals.
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Confidence interval clarifications
Following several questions in the Q&A sections we have decided to add a lecture which digs a bit deeper into what confidence intervals are.
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Student's T distribution
A little story about the inception of the Student's T distribution - a valuable tool when working with small samples.
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Student's T distribution
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Calculating confidence intervals within a population with an unknown variance
We combine our knowledge on confidence intervals with that on the Student's T distribution, by making inferences using a small sample.
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Population variance unknown. T-score. Exercise
An exercise on confidence intervals, when population variance is uknown.
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What is a margin of error and why is it important in Statistics?
A deeper dive into the drivers of confidence intervals through the margin of error.
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Margin of error
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Calculating confidence intervals for two means with dependent samples
We show real life examples of confidence intervals. In this lesson, we focus on dependent samples, which are often found in medicine.
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Confidence intervals. Two means. Dependent samples. Exercise
An exercise on confidence intervals for two means (dependent samples).
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Calculating confidence intervals for two means with independent samples (part 1)
We carry on with the applications. This time the example is with independent samples, where the population variance is known.
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Confidence intervals. Two means. Independent samples (Part 1). Exercise
An exercise on confidence intervals for two means (independent samples).
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Calculating confidence intervals for two means with independent samples (part 2)
More often than not, we do not know the population variance, as it is too costly (or impossible) to have data on the whole population. We explore how to deal with the problem, through sample variance. We start from the simpler case, where we assume that the variance of the two samples is equal.
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Confidence intervals. Two means. Independent samples (Part 2). Exercise
An exercise on confidence intervals for two means (independent samples).
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Calculating confidence intervals for two means with independent samples (part 3)
We conclude the section on confidence intervals with the example on independent samples, where the variance is unknown and assumed to be different. That is the most common case.
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Practical example: inferential statistics
This is a practical example on inferential statistics.
We apply all the knowledge we have on descriptive statistics and inferential so far.
The data is based on purchases in a shoe shop. We explore the sales of different products and shops, and try to manage the inventory of our company better.
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Practical example: inferential statistics
This is a practical example on inferential statistics.
We apply all the knowledge we have on descriptive statistics and inferential so far.
The data is based on purchases in a shoe shop. We explore the sales of different products and shops, and try to manage the inventory of our company better.
Please find an exercise file and a solution file attached to this lecture.
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The null and the alternative hypothesis
Hypothesis testing is the heart of statistics. We start from the very basics: what are the null and alternative hypotheses. We show different examples and explain how to form hypotheses that are later to be tested.
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Further reading on null and alternative hypotheses
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Null vs alternative
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Establishing a rejection region and a significance level
Whenever we do hypothesis testing, we either accept or reject a hypothesis. In this lecture, we explain the rationale behind testing.
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Rejection region and significance level
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Type I error vs Type II error
There are two errors one can make when testing - false positive and false negative. In order to be better statisticians, we must be acquainted with those issues.
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Type I error vs type II error
