Best Statistics Courses

Find the best online Statistics Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Statistics Courses.

The Data Science Course 2021: Complete Data Science Bootcamp

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

Created by 365 Careers - Creating opportunities for Business & Finance students

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Students: 411572, Price: $94.99

Students: 411572, Price:  Paid

The Problem

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.     

However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

 

And how can you do that?

 

Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)

 

Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture

 

The Solution

 

Data science is a multidisciplinary field. It encompasses a wide range of topics.

 

  • Understanding of the data science field and the type of analysis carried out

     

  • Mathematics

     

  • Statistics

     

  • Python

     

  • Applying advanced statistical techniques in Python

     

  • Data Visualization

     

  • Machine Learning

     

  • Deep Learning

     

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.

 

So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2021.

 

We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.

 

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).

 

The Skills

   1. Intro to Data and Data Science

Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?     

Why learn it?
As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.
 

   2. Mathematics 

Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.

 

We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.

 

Why learn it?

 

Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.

   3. Statistics 

You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.

 

Why learn it?

 

This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.

   4. Python

Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.

Why learn it?

 

When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.

 

   5. Tableau

Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.

Why learn it?

 

A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.

 

   6. Advanced Statistics 

Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.

 

Why learn it?

 

Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.

 

   7. Machine Learning 

The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.

 

Why learn it?

 

Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.

 

***What you get***

  • A $1250 data science training program

     

  • Active Q&A support

     

  • All the knowledge to get hired as a data scientist

     

  • A community of data science learners

     

  • A certificate of completion

     

  • Access to future updates

     

  • Solve real-life business cases that will get you the job   

You will become a data scientist from scratch

 

We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and become a part of our data scientist program today.

 

 

Statistics with R – Beginner Level

Basic statistical analyses using the R program

Created by Bogdan Anastasiei - University Teacher and Consultant

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Students: 110341, Price: $29.99

Students: 110341, Price:  Paid

If you want to learn how to perform the basic statistical analyses in the R program, you have come to the right place.

Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart or how to compute a simple statistical test like the one-sample t test. Everything is here, in this course, explained visually, step by step.

So, what will you learn in this course?

First of all, you will learn how to manipulate data in R, to prepare it for the analysis: how to filter your data frame, how to recode variables and compute new variables.

Afterwards, we will take care about computing the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc., both in the whole population and in subgroups of the population.

Then you will learn how to visualize data using tables and charts. So we will build tables and cross-tables, as well as histograms, cumulative frequency charts, column and mean plot charts, scatterplot charts and boxplot charts.

Since assumption checking is a very important part of any statistical analysis, we could not elude this topic. So we’ll learn how to check for normality and for the presence of outliers.

Finally, we will perform some basic, one-sample statistical tests and interpret the results. I’m talking about the one-sample t test, the binomial test and the chi-square test for goodness-of-fit.

So after graduating this course, you will know how to perform the essential statistical procedures in the R program. So… enroll today!

Statistics for Data Science and Business Analysis

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

Created by 365 Careers - Creating opportunities for Business & Finance students

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Students: 108459, Price: $94.99

Students: 108459, Price:  Paid

Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, 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 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 hard for over four 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.

 

Let's start learning together now!

 

Become a Probability & Statistics Master

Learn everything from Probability & Statistics, then test your knowledge with 600+ practice questions

Created by Krista King - Your geeky, trusty math tutor

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Students: 55539, Price: $199.99

Students: 55539, Price:  Paid

HOW BECOME A PROBABILITY & STATISTICS MASTER IS SET UP TO MAKE COMPLICATED MATH EASY:

This 163-lesson course includes video and text explanations of everything from Probability and Statistics, and it includes 45 quizzes (with solutions!) and an additional 8 workbooks with extra practice problems, to help you test your understanding along the way. Become a Probability & Statistics Master is organized into the following sections:

  • Visualizing data, including bar graphs, pie charts, Venn diagrams, histograms, and dot plots

  • Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots

  • Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores

  • Probability, including union vs. intersection and independent and dependent events and Bayes' theorem

  • Discrete random variables, including binomial, Bernoulli, Poisson, and geometric random variables

  • Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals

  • Hypothesis testing, including inferential statistics, significance levels, type I and II errors, test statistics, and p-values

  • Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE, and chi-square

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

Videos: Watch over my shoulder as I solve problems for every single math issue you’ll encounter in class. We start from the beginning... I explain the problem setup and why I set it up that way, the steps I take and why I take them, how to work through the yucky, fuzzy middle parts, and how to simplify the answer when you get it.

Notes: The notes section of each lesson is where you find the most important things to remember. It’s like Cliff Notes for books, but for math. Everything you need to know to pass your class and nothing you don’t.

Quizzes: When you think you’ve got a good grasp on a topic within a course, you can test your knowledge by taking one of the quizzes. If you pass, great! If not, you can review the videos and notes again or ask for help in the Q&A section.

Workbooks: Want even more practice? When you've finished the section, you can review everything you've learned by working through the bonus workbook. The workbooks include tons of extra practice problems, so they're a great way to solidify what you just learned in that section.

HERE'S WHAT SOME STUDENTS OF BECOME A PROBABILITY & STATISTICS MASTER HAVE TOLD ME:

  • “Krista is an experienced teacher who offers Udemy students complete subject matter coverage and efficient and effective lessons/learning experiences. She not only understands the course material, but also selects/uses excellent application examples for her students and presents them clearly and skillfully using visual teaching aids/tools.” - John

  • “Really good, thorough, well explained lessons.” - Scott F.

  • “This is my second course (algebra previously) from Ms. King's offerings. I enjoyed this course and learned a lot! Each video explains a concept, followed by the working of several examples. I learned the most by listening to Ms King's teaching of the concept, stopping the video, and then attempting to work the example problems. After working the problems, then watching her complete the examples, I found that I really retained the concepts. A great instructor!” - Charles M.

YOU'LL ALSO GET:

  • Lifetime access to Become a Probability & Statistics Master

  • Friendly support in the Q&A section

  • Udemy Certificate of Completion available for download

  • 30-day money back guarantee

Enroll today!

I can't wait for you to get started on mastering probability and statistics.

- Krista :)

Statistics for Business Analytics and Data Science A-Z™

Learn The Core Stats For A Data Science Career. Master Statistical Significance, Confidence Intervals And Much More!

Created by Kirill Eremenko - Data Scientist

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Students: 48041, Price: $99.99

Students: 48041, Price:  Paid

If you are aiming for a career as a Data Scientist or Business Analyst then brushing up on your statistics skills is something you need to do.

But it's just hard to get started... Learning / re-learning ALL of stats just seems like a daunting task.

That's exactly why I have created this course!

Here you will quickly get the absolutely essential stats knowledge for a Data Scientist or Analyst.

This is not just another boring course on stats. 

This course is very practical. 

I have specifically included real-world examples of business challenges to show you how you could apply this knowledge to boost YOUR career.

At the same time you will master topics such as distributions, the z-test, the Central Limit Theorem, hypothesis testing, confidence intervals, statistical significance and many more!

So what are you waiting for?

Enroll now and empower your career!

Workshop in Probability and Statistics

This workshop will teach you the fundamentals of statistics in order to give you a leg up at work or in school.

Created by George Ingersoll - MBA & PhD

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Students: 28056, Price: $39.99

Students: 28056, Price:  Paid

This workshop is designed to help you make sense of basic probability and statistics with easy-to-understand explanations of all the subject's most important concepts. Whether you are starting from scratch or if you are in a statistics class and struggling with your assigned textbook or lecture material, this workshop was built with you in mind.

Probability and Statistics for Business and Data Science

Learn how to apply probability and statistics to real data science and business applications!

Created by Jose Portilla - Head of Data Science, Pierian Data Inc.

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Students: 23054, Price: $99.99

Students: 23054, Price:  Paid

Welcome to Probability and Statistics for Business and Data Science!

In this course we cover what you need to know about probability and statistics to succeed in business and the data science field!

This practical course will go over theory and implementation of statistics to real world problems. Each section has example problems, in course quizzes, and assessment tests.

We’ll start by talking about the basics of data, understanding how to examine it with measurements of central tendency, dispersion, and also building an understanding of how bivariate data sources can relate to each other.

Afterwards we’ll dive into probability , learning about combinations and permutations, as well as conditional probability and how to apply bayes theorem.

Then we’ll move on to discussing the most common distributions found in statistics, creating a solid foundation of understanding how to work with uniform, binomial, poisson, and normal distributions.

Up next we’ll talk about statistics, applying what we’ve learned so far to real world business cases, including hypothesis testing and the student's T distribution.

We’ll end the course with 3 sections on advanced topics, such as ANOVA (analysis of variance), understanding regression analysis, and finally performing chi squared analysis.

The sections are modular and organized by topic, so you can reference what you need and jump right in!

Our course includes HD Video with clear explanations and high quality animations, we also include extensive case studies to show you how to apply this knowledge to the real world.

We'll cover everything you need to know about statistics and probability to clearly tackle real world business and data science problems!

Including: 

  • Measurements of Data

  • Mean, Median, and Mode

  • Variance and Standard Deviation

  • Co-variance and Correlation

  • Permutations and Combinations

  • Unions and Intersections

  • Conditional Probability

  • Bayes Theorem

  • Binomial Distribution

  • Poisson Distribution

  • Normal Distribution

  • Sampling

  • Central Limit Theorem

  • Hypothesis Testing

  • T-Distribution Testing

  • Regression Analysis

  • ANOVA

  • Chi Squared

  • and much more!

Not only do you get great technical content, but you’ll also have access to our online QA forums as well as our student chat channel. Where the TAs and myself are happy to help out with any questions you encounter! Upon finishing this course you’ll receive a certificate of completion you can post on your linkedin profile to show off to your colleagues, or even potential employers!

All of this content comes with a 30 day money back guarantee, so you can try out the course risk free!

So what are you waiting for? Enroll today and we'll see you inside the course!

Probability and Statistics 1: The Complete Guide

Learn everything fast through concise yet contented lectures

Created by L Sang - Maths tutor with a first-honours degree in Mathematics

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Students: 21987, Price: $89.99

Students: 21987, Price:  Paid

I know, Probability and Statistics is difficult. But is there a way to make it easy? Of course. I for one managed that.

I know a lot of people struggle with it; a very small group of people are good at it. Back in university, I was in that bigger group, the group that struggled through Probability and Statistics lecture. I needed help; I couldn't understand a thing, but I finally found help and turned my exam result around. I guess since you're looking at this, you need help too.

This 6-hour COMPLETE GUIDE course contains everything you need to know to get started with Probability and Statistics. It's packed with videos that have been categorised into different topics, hence easy for you to learn.

I've included lots of definitions, theorems, quizzes, examples, concise notes for EVERY single section, exercises, and a walkthrough of all the exercise sheets. Most importantly, I've done a BONUS section for you! It includes some additional questions that will strengthen your skill even more.

With this basic Probability and Statistics course, you will have a good core understanding to pursue many more difficult Mathematics topic. In this course, everything has been broken down into a simple structure to make learning and understanding easy for you.

This COMPLETE guide is for those of you are looking to get a full understanding of the basics; the important parts. You've already shown half of your determination by looking at the course, so if this course sounds right for you, boost your eagerness to learn and join me on this journey!

Tips:

1) It will be very useful if you also take notes of your own as you're watching the lectures, it will help you understand everything better and quicker. Just pause if I move on to other topics too fast or if you haven't fully understood the previous sub-topic before you move on to the next parts.

2) Please ask any questions you may have in the Q&A section if you don't understand. It's one thing to not understand it, but it's a whole new experience and a very important thing to do when learning Maths to be able to discuss it with fellow students who are going through the same thing.

3) Use headphones for better sound. (I suggest you turn the volume up)

4) Don't forget you can always slow down or speed up the video!

Statistics with R – Advanced Level

Advanced statistical analyses using the R program

Created by Bogdan Anastasiei - University Teacher and Consultant

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Students: 21787, Price: $49.99

Students: 21787, Price:  Paid

If you want to learn how to perform real advanced statistical analyses in the R program, you have come to the right place.

Now you don’t have to scour the web endlessly in order to find how to do an analysis of covariance or a mixed analysis of variance, how to execute a binomial logistic regression, how to perform a multidimensional scaling or a factor analysis. Everything is here, in this course, explained visually, step by step.

So, what’s covered in this course?

First of all, we are going to study some more techniques to evaluate the mean differences. If you took the intermediate course- which I highly recommend you – you learned about the t tests and the between-subjects analysis of variance. Now we will go to the next level and tackle the analysis of covariance, the within-subjects analysis of variance and the mixed analysis of variance.

Next, in the section about the predictive techniques, we will approach the logistic regression, which is used when the dependent variable is not continuous – in other words, it is categorical. We are going to study three types of logistic regression: binomial, ordinal and multinomial.

Then we are going to deal with the grouping techniques. Here you will find out, in detail, how to perform the multidimensional scaling, the principal component analysis and the factor analysis, the simple and the multiple correspondence analysis, the cluster analysis (both k-means and hierarchical) , the simple and the multiple discriminant analysis.

So after finishing this course, you will be a real expert in statistical analysis with R – you will know a lot of sophisticated, state-of-the art analysis techniques that will allow you to deeply scrutinize your data and get the most information out of it. So don’t wait, enroll today!

Statistics for Data Science using Python

Statistics you need in the office: Core of Statistics, Inferential & Descriptive statistics, Hypothesis testing,

Created by Shan Singh - Data Scientist

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Students: 20163, Price: $29.99

Students: 20163, Price:  Paid

If You want to be a Data Scientist or Data Analyst then brushing up on your statistics skills is something you need to do.

But it's just hard to get started with Data Science in most of the course you will find theoritical knowledge on stats not having practical knowledge

I have explained Each topic in a easiest way as well as its implementation in Python from Scratch (most demanding language of Data Science Industry)

That's exactly why I have created this course for you!

Here you will quickly get the  essential stats knowledge for a Data Scientist or Analyst.

I have included real-world use-cases of business challenges to show you how you could apply Stats knowledge to boost your career.

At the same time you can master topics such as Descriptive Stats, distributions, z-test, the Central Limit Theorem, hypothesis testing,  & many more!

So what are you waiting for?

Enroll now and & get a transition into Data Science

Why should you take this course?

  • This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data

  • This course is taught by an actual mathematician that is in the same time also working as a data scientist.

  • This course is balancing both: theory & practical real-life example.

  • After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis.

R Programming for Statistics and Data Science 2021

R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R

Created by 365 Careers - Creating opportunities for Business & Finance students

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Students: 19235, Price: $94.99

Students: 19235, Price:  Paid

R Programming for Statistics and Data Science 2021

R Programming is a skill you need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn't you?  Data scientist is the hottest ranked profession in the US.

But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream title.

 

This course is packing all of this, and more, in one easy-to-handle bundle, and it’s the perfect start to your journey.

 

So, welcome to R for Statistics and Data Science!

 

R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’.   

Laying strong foundations

 

This course wastes no time and jumps right into hands-on coding in R. But don’t worry if you have never coded before, we start off light and teach you all the basics as we go along! We wanted this to be an equally satisfying experience for both complete beginners and those of you who would just like a refresher on R.

What makes this course different from other courses?

 

  • Well-paced learning.

Receive top class training with content which we’ve built - and rigorously edited - to deliver powerful and efficient results.

 

Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks.

 

  • Introductory guide to statistics.

We will take you through descriptive statistics and the fundamentals of inferential statistics.

 

We will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills.     

You’ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis.

 

  • The essentials of programming – R-based.

Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations.

 

  • Data manipulation and analysis techniques in detail.

Learn to work with vectors, matrices, data frames, and lists.

 

Become adept in ‘the Tidyverse package’ - R’s most comprehensive collection of tools for data manipulation – enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it.

 

Create meaning-heavy data visualizations and plots.

 

  • Practice makes perfect.

Reinforce your learning through numerous practical exercises, made with love, for you, by us.

What about homework, projects, & exercises?

 

There is a ton of homework that will challenge you in all sorts of ways. You will have the chance to tackle the projects by yourself or reach out to a video tutorial if you get stuck.

You: Is there something to show for the skills I will acquire?

Us: Indeed, there is – a verifiable certificate.

 

You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations & analysis in R.

 

 If that sounds good to you, then welcome to the classroom :)

Statistics for Data Analysis Using Excel 2016

Plain & Simple Lessons on Descriptive & Inferential Statistics Theory With Excel Examples for Business & Six Sigma

Created by Sandeep Kumar ­ - Experienced Quality Manager • Six Sigma Coach • Consultant

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Students: 13142, Price: $19.99

Students: 13142, Price:  Paid

Start loving data and making sense of it. Leverage the power of MS Excel to make it easy!

Learn statistics, and apply these concepts in your work place using Microsoft Excel.

This course is about Statistics and Data Analysis. The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concept. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use Microsoft Excel to perform these calculations.

Following areas of statistics are covered:

Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation

Data Visualization - 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot

Probability - Basic Concepts, Permutations, Combinations

Population and Sampling

Probability Distributions - Normal, Binomial and Poisson Distributions

Hypothesis Testing - One Sample and Two Samples - z Test, t Test, p Test, F Test, Chi Square Test

ANOVA - Perform Analysis of Variance (ANOVA) step by step doing manual calculation and by MS Excel.

Mathematics & Statistics for Machine Learning

Learn these concepts First before learning Machine Learning | With English Subtitles

Created by Govind Kumar - Artificial Intelligence and Business Transformation Expert

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Students: 12941, Price: $44.99

Students: 12941, Price:  Paid

The trainer of this course is an AI expert and he has observed that many students and young professionals make the mistake of learning machine learning without understanding the core concepts in maths and statistics. This course will help to address that gap in a big way.

Since Machine Learning is a field at the intersection of multiple disciplines like statistics, probability, computer science, and mathematics, its essential for practitioners and budding enthusiasts to assimilate these core concepts.

These concepts will help you to lay a strong foundation to build a thriving career in artificial intelligence.

This course teaches you the concepts mathematics and statistics but from an application perspective. It’s one thing to know about the concepts but it is another matter to understand the application of those concepts. Without this understanding, deploying and utilizing machine learning will always remain challenging.

You will learn concepts like measures of central tendency vs dispersion, hypothesis testing, population vs sample, outliers and many interesting concepts. You will also gain insights into gradient decent and mathematics behind many algorithms.

We cover the below concepts in this course:

  • Measures of Central Tendency vs Dispersion

  • Mean vs Standard Deviation

  • Percentiles

  • Types of Data

  • Dependent vs independent variables

  • Probability

  • Sample Vs population

  • Hypothesis testing

  • Concept of stability

  • Types of distribution

  • Outliers

  • Maths behind machine learning algorithms like regression, decision tree and kNN

  • Gradient descent.

Probability for Statistics and Data Science

Probability for improved business decisions: Introduction, Combinatorics, Bayesian Inference, Distributions

Created by 365 Careers - Creating opportunities for Business & Finance students

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Students: 10631, Price: $89.99

Students: 10631, Price:  Paid

Probability is probably the most fundamental skill you need to acquire if you want to be successful in the world of business. What most people don’t realize is that having a probabilistic mindset is much more important than knowing “absolute truths”.

You are already here, so actually you know that.

And it doesn’t matter if it is pure probability, statistics, business intelligence, finance or data science where you want to apply your probability knowledge…

Probability for Statistics and Data Science has your back!

This is the place where you’ll take your career to the next level – that of probability, conditional probability, Bayesian probability, and probability distributions.

You may be wondering: “Hey, but what makes this course better than all the rest?”

Probability for Statistics and Data Science has been carefully crafted to reflect the most in-demand skills that will enable you to understand and compute complicated probabilistic concepts. This course is:

  • Easy to understand

  • Comprehensive

  • Practical

  • To the point

  • Beautifully animated (with amazing video quality)

Packed with plenty of exercises and resources

That’s all great, but what will you actually learn? Probability. And nothing less.

To be more specific, we focus on the business implementation of probability concepts. This translates into a comprehensive course consisting of:

  • An introductory part that will acquaint you with the most basic concepts in the field of probability: event, sample space, complement, expected value, variance, probability distribution function

  • We gradually build on your knowledge with the first widely applicable formulas:

  • Combinatorics or the realm of permutations, variations, and combinations. That’s the place where you’ll learn the laws that govern “everyday probability”

  • Once you’ve got a solid background, you’ll be ready for some deeper probability theory – Bayesian probability.

  • Have you seen this expression: P(A|B) = P(B|A)P(A)/P(B) ? That’s the Bayes’ theorem – the most fundamental building block of Bayesian inference. It seems complicated but it will take you less than 1 hour to understand not only how to read it, but also how to use it and prove it

  • To get there you’ll learn about unions, intersections, mutually exclusive sets, overlapping sets, conditional probability, the addition rule, and the multiplication rule

Most of these topics can be found online in one form or another. But we are not bothered by that because we are certain of the outstanding quality of teaching that we provide.

What we are really proud of, though, is what comes next in the course. Distributions.

Distributions are something like the “heart” of probability applied in data science. You may have heard of many of them, but this is the only place where you’ll find detailed information about many of the most common distributions.

  • Discrete: Uniform distribution, Bernoulli distribution, Binomial distribution (that’s where you’ll see a lot of the combinatorics from the previous parts), Poisson

  • Continuous: Normal distribution, Standard normal distribution, Student’s T, Chi-Squared, Exponential, Logistic

Not only do we have a dedicated video for each one of them, how to determine them, where they are applied, but also how to apply their formulas.

Finally, we’ll have a short discussion on 3 of the most common places where you can stumble upon probability:

  • Finance

  • Statistics

  • Data Science

    If that’s not enough, keep in mind that we’ve got real-life cases after each of our sections. We know that nobody wants to learn dry theory without seeing it applied to real business situations so that’s in store, too!

We think that this will be enough to convince you curriculum-wise. But we also know that you really care about WHO is teaching you, too. 

Teaching is our passion  

We worked hard for over four months to create the best possible Probability course that 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, are just some of the perks you will get. What else?

Exceptional Q&A support. Yes. That’s our favorite part – interacting with you on the various topics you learn about (and you are going to love it, too!)

What makes this course different from the rest of the Probability courses out there?  

  • High-quality production – HD video and animations (This isn’t a collection of boring lectures!)

  • Knowledgeable instructor (an adept mathematician who has competed at an international level) who will bring you not only his probability knowledge but the complicated interconnections between his areas of expertise – finance and data science

  • Comprehensive – we will cover all major probability topics and skills you need to level up your career

  • Extensive Case Studies - helping you reinforce everything you’ve learned  

  • Exceptional support – we said that, but let’s say it again - 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

  • Succinct – the biggest investment you’ll make is your own time. And we will not waste it. All our teaching is straight to the point

    Still not convinced?

Here’s why you need these skills?  

  1. Salary/Income – most businesses are starting to realize      the advantages of implementing data-driven decisions. And those are all stepping on probability. A probabilistic mindset is definitely one of the non-automatable skills that managers of the next decade will be  expected to have

  2. Promotions and secure future – If you understand probability well, you will be able to back up your business and positions in much more convincing way, draining from quantitative evidence; needless to say, that’s the path to career growth       

  3. New horizons – probability is a pathway to many positions in any industry. While it is rarely a full-time position, it is crucial for most business jobs nowadays. And it’s not a boring aspect!

Please bear in mind that the course comes with Udemy’s 30-day 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!

 

Deep Learning Foundation : Linear Regression and Statistics

Learn linear regression from scratch, Statistics, R-Squared, Python, Gradient descent, Deep Learning, Machine Learning

Created by Jay Bhatt - Data Scientist by Profession Instructor by Passion

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Students: 9145, Price: $19.99

Students: 9145, Price:  Paid

Hi Everyone welcome to new course which is created to sharpen your linear regression and statistical basics. linear regression is starting point for a data science this course focus is on making your foundation strong for deep learning and machine learning algorithms. In this course I have explained hypothesis testing, Unbiased estimators, Statistical test , Gradient descent. End of the course you will be able to code your own regression algorithm from scratch.

Master statistics & machine learning: intuition, math, code

A rigorous and engaging deep-dive into statistics and machine-learning, with hands-on applications in Python and MATLAB.

Created by Mike X Cohen - Neuroscientist, writer, professor

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Students: 8243, Price: $19.99

Students: 8243, Price:  Paid

Statistics and probability control your life. I don't just mean What YouTube's algorithm recommends you to watch next, and I don't just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.

You need to understand statistics.

Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called 'data science' and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.

If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field -- ranging from data scientist to engineering to research scientist to deep learning modeler -- you'll need to know statistics and machine-learning. And you'll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.

There are six reasons why you should take this course:

  • This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.

  • After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren't taught here. That's because you will learn the foundations upon which advanced methods are build.

  • This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.

  • Enrolling in the course gives you access to the Q&A, in which I actively participate every day.

  • I've been studying, developing, and teaching statistics for 20 years, and I'm, like, really great at math.

What you need to know before taking this course:

  • High-school level maths. This is an applications-oriented course, so I don't go into a lot of detail about proofs, derivations, or calculus.

  • Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don't have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.

  • I recommend taking my free course called "Statistics literacy for non-statisticians". It's 90 minutes long and will give you a bird's-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!

  • You do not need any previous experience with statistics, machine learning, deep learning, or data science. That's why you're here!

Is this course up to date?

Yes, I maintain all of my courses regularly. I add new lectures to keep the course "alive," and I add new lectures (or sometimes re-film existing lectures) to explain maths concepts better if students find a topic confusing or if I made a mistake in the lecture (rare, but it happens!).

You can check the "Last updated" text at the top of this page to see when I last worked on improving this course!

What if you have questions about the material?

This course has a Q&A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q&A by posting to ongoing discussions.

And, you can also post your code for feedback or just to show off -- I love it when students actually write better code than mine! (Ahem, doesn't happen so often.)

What should you do now?

First of all, congrats on reading this far; that means you are seriously interested in learning statistics and machine learning. Watch the preview videos, check out the reviews, and, when you're ready, invest in your brain by learning from this course!

Statistics for Data Analysis Using R

Learn Programming in R & R Studio • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science

Created by Sandeep Kumar ­ - Experienced Quality Manager • Six Sigma Coach • Consultant

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Students: 7751, Price: $99.99

Students: 7751, Price:  Paid

Perform simple or complex statistical calculations using R Programming! - You don't need to be a programmer for this :)

Learn statistics, and apply these concepts in your workplace using R.

The course will teach you the basic concepts related to Statistics and Data Analysis,  and help you in applying these concepts. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use R to perform these calculations.

Following areas of statistics are covered:

Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. (Using base R function and the psych package)

Data Visualization - 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot (using base R commands)

Probability - Basic Concepts, Permutations, Combinations (Basic theory only)

Population and Sampling - Basic concepts (theory only)

Probability Distributions - Normal, Binomial  and Poisson Distributions (Base R functions and the visualize package)

Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test, Chi-Square Test

ANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using R.

Statistics literacy for non-statisticians

Learn the key terms and analysis methods in statistics

Created by Mike X Cohen - Neuroscientist, writer, professor

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Students: 6858, Price: Free

Students: 6858, Price:  Free

In this short course, you will learn the meaning of key terms in statistics, such as p-value, ANOVA, variance, etc.

By the end of this course, you will feel more comfortable talking about and reading about commonly used statistical analysis methods.

Note that this course does not cover the math of the analyses, nor software to perform statistical analyses.

Math for Middle Schoolers: Statistics

Learn the Basics of Statistics and Succeed in your Middle School Math Classes

Created by Abinaya Anbuchelvan - Student

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Students: 5416, Price: Free

Students: 5416, Price:  Free

Math for Middle Schoolers: Statistics is a fun, informative, and simple course for middle schoolers that allows them to learn about and understand the different statistics topics generally taught in middle school FOR FREE! The goal of this course is to help you master these concepts and to provide you with the knowledge to learn more complicated concepts later. By following this course to the end, you will definitely have a much more thorough understanding of statistics! This course will help you learn and improve in the following areas:

  • Graphs
  • Measures of Central Tendency 
  • Plots

All the videos consist of a detailed description of the concept followed by examples to ensure a thorough understanding of the topic.

Statistics / Data Analysis in SPSS: Inferential Statistics

Increase Your Data Analytic Skills – Highly Valued And Sought After By Employers

Created by Quantitative Specialists - Specializing in Statistics, Research Design, and Measurement

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Students: 5376, Price: $94.99

Students: 5376, Price:  Paid

November, 2019. 

Join more than 1,000 students and get instant access to this best-selling content - enroll today!

Get marketable and highly sought after skills in this course that will substantially increase your knowledge of data analytics, with a focus in the area of significance testing, an important tool for A/B testing and product assessment.

Many tests covered, including three different t tests, two ANOVAs, post hoc tests, chi-square tests (great for A/B testing), correlation, and regression. Database management also covered!

Two in-depth examples provided of each test for additional practice.

This course is great for professionals, as it provides step by step instruction of tests with clear and accurate explanations. Get ahead of the competition and make these tests important parts of your data analytic toolkit!

Students will also have the tools needed to succeed in their statistics and experimental design courses.

Data Analytics is an rapidly growing area in high demand (e.g., McKinsey)

Statistics play a key role in the process of making sound business decisions that will generate higher profits. Without statistics, it's difficult to determine what your target audience wants and needs. 

  Inferential statistics, in particular, help you understand a population's needs better so that you can provide attractive products and services. 

  This course is designed for business professionals who want to know how to analyze data. You'll learn how to use IBM SPSS to draw accurate conclusions on your research and make decisions that will benefit your customers and your bottom line. 

  Use Tests in SPSS to Correctly Analyze Inferential Statistics 

  • Use the One Sample t Test to Draw Conclusions about a Population

  • Understand ANOVA and the Chi Square

  • Master Correlation and Regression

  • Learn Data Management Techniques

  Analyze Research Results Accurately to Make Better Business Decisions 

  With SPSS, you can analyze data to make the right business decisions for your customer base. And by understanding how to use inferential statistics, you can draw accurate conclusions about a large group of people, based on research conducted on a sample of that population. 

  This easy-to-follow course, which contains illustrative examples throughout, will show you how to use tests to assess if the results of your research are statistically significant. 

  You'll be able to determine the appropriate statistical test to use for a particular data set, and you'll know how to understand, calculate, and interpret effect sizes and confidence intervals. 

  You'll even know how to write the results of statistical analyses in APA format, one of the most popular and accepted formats for presenting the results of statistical analyses, which you can successfully adapt to other formats as needed. 

  Contents and Overview 

  This course begins with a brief introduction before diving right into the One Sample t Test, Independent Samples t Test, and Dependent Samples t Test. You'll use these tests to analyze differences and similarities between sample groups in a population. This will help you determine if you need to change your business plan for certain markets of consumers. 

  Next, you'll tackle how to use ANOVA (Analysis of Variance), including Post-hoc Tests and Levene's Equal Variance Test. These tests will also help you determine what drives consumer decisions and behaviors between different groups. 

  When ready, you'll master correlation and regression, as well as the chi-square. As with all previous sections, you'll see illustrations of how to analyze a statistical test, and you'll access additional examples for more practice. 

  Finally, you'll learn about data management in SPSS, including sorting and adding variables. 

  By the end of this course, you'll be substantially more confident in both IBM SPSS and statistics. You'll know how to use data to come to the right conclusions about your market. 

  By understanding how to use inferential statistics, you'll be able to identify consumer needs and come up with products and/or services that will address those needs effectively. 

Join the over 1,000 students who have taken this best-selling course - enroll today!

Statistics/Data Analysis with SPSS: Descriptive Statistics

Increase Your Descriptive Data Analytic Skills – Highly Valued And Sought After By Employers

Created by Quantitative Specialists - Specializing in Statistics, Research Design, and Measurement

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Students: 4967, Price: $74.99

Students: 4967, Price:  Paid

November, 2019.

Get marketable and highly sought after skills in this course that will increase your knowledge of data analytics, with a focus on descriptive statistics, an important tool for understanding trends in data and making important business decisions.  

Enroll now to join the more than 2000 students and get instant access to all course content!

Whether a student or professional in the field, learn the important basics of both descriptive statistics and IBM SPSS so that you can perform data analyses and start using descriptive statistics effectively. 

        By monitoring and analyzing data correctly, you can make the best decisions to excel in your work as well as increase profits and outperform your competition. 

        This beginner's course offers easy to understand step-by-step instructions on how to make the most of IBM SPSS for data analysis. 

  Make Better Business Decisions with SPSS Data Analysis 

  • Create, Copy, and Apply Value Labels

  • Insert, Move, Modify, Sort, and Delete Variables

  • Create Charts and Graphs

  • Measure Central Tendency, Variability, z-Scores, Normal Distribution, and Correlation

  Interpret and Use Data Easily and Effectively with IBM SPSS 

        IBM SPSS is a software program designed for analyzing data. You can use it to perform every aspect of the analytical process, including planning, data collection, analysis, reporting, and deployment. 

        This introductory course will show you how to use SPSS to run analyses, enter and code values, and interpret data correctly so you can make valid predictions about what strategies will make your organization successful. 

  Contents and Overview 

        This course begins with an introduction to IBM SPSS. It covers all of the basics so that even beginners will feel at ease and quickly progress. You'll tackle creating value labels, manipulating variables, modifying default options, and more. 

        Once ready, you'll move on to learn how to create charts and graphs, such as histograms, stem and leaf plots, and more. You'll be able to clearly organize and read data that you've collected. 

        Then you'll master central tendency, which includes finding the mean, median, and mode. You'll also learn how to measure the standard deviation and variance, as well as how to find the z-score. 

        The course ends with introductory statistics video lectures that dive deeper into graphs, central tendency, normal distribution, variability, and z-scores. 

        Upon completion of this course, you'll be ready to apply what you've learned to excel in your statistics classes and make smarter business decisions. You'll be able to use the many features in SPSS to gather and interpret data more effectively, as well as plan strategies that will yield the best results as well as the highest profit margins. 

Statistics with MATLAB

Statistics with MATLAB (Please don't give rank to the lecture before all the lectures are uploaded)

Created by Prof. Dr. Academic Educator - Prof. Dr. Academic Educator

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Students: 4819, Price: Free

Students: 4819, Price:  Free

In this course, statistic subjects will be covered using MATLAB. We will start with the explanation of vectors, matrices and cells, then proceed with the tables which is an important subject in statistics. Density functions and cumulative distribution functions will be explained. Histograms and boxplots use in MATLAB will be explained by examples. We will consider Hypothesis tests using MATLAB functions ztest, ttest, vartest. Analysis of variance, and multivariate analysis of variance will be studied using MATLAB. Linear and non-linear regression models will be covered. Generation of random data for definite densities and simulation using random data is the last topic to be covered in this course.

Beginner Statistics for Data Analytics – Learn the Easy Way!

Learn the fundamentals of statistics and regression analysis in an easy, fun way! No background in stats necessary

Created by Nate @ Wisdify - Co-Founder & Lead Instructor @ Wisdify

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Students: 4024, Price: $44.99

Students: 4024, Price:  Paid

This is not another boring stats course. We'll teach you the fundamental statistical tools to be successful in analytics...without boring you with complex formulas and theory.

Statistical analysis can benefit almost anyone in any industry. We live in a world flooded with data. Having the tools to analyze and synthesize that data will help you stand out on your team.

In a few short hours, you'll have the fundamental skills to help you immediately start applying sophisticated statistical analyses to your data.

Our course is:   

  • Very easy to understand - There is not memorizing complex formulas (we have Excel to do that for us) or learning abstract theories. Just real, applicable knowledge.

  • Fun - We keep the course light-hearted with fun examples

  • To the point - We removed all the fluff so you're just left with the most essential knowledge

What you'll be able to do by the end of the course  

  1. Create visualizations such as histograms and scatter plots to visually show your data  

  2. Apply basic descriptive statistics to your past data to gain greater insights

  3. Combine descriptive and inferential statistics to analyze and forecast your data

  4. Utilize a regression analysis to spot trends in your data and build a robust forecasting model

Let's start learning!  

Introduction to Bayesian Statistics

Bayes' Theorem and Bayesian statistics from scratch - a beginner's guide.

Created by Woody Lewenstein - Mathematics Teacher

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Students: 3391, Price: Free

Students: 3391, Price:  Free

Bayesian statistics is used in many different areas, from machine learning, to data analysis, to sports betting and more. It's even been used by bounty hunters to track down shipwrecks full of gold!

This beginner's course introduces Bayesian statistics from scratch. It is appropriate both for those just beginning their adventures in Bayesian statistics as well as those with experience who want to understand it more deeply.

We begin by figuring out what probability even means, in order to distinguish the Bayesian approach from the Frequentist approach.

Next we look at conditional probability, and derive what we call the "Baby Bayes' Theorem", and then apply this to a number of scenarios, including Venn diagram, tree diagram and normal distribution questions.

We then derive Bayes' Theorem itself with the use of two very famous counter-intuitive examples.

We then finish by looking at the puzzle that Thomas Bayes' posed more than 250 years ago, and see how Bayes' Theorem, along with a little calculus, can solve it for us.

Introduction to Statistics

Introductory Statistics as Covered in the Social, Behavioral, and Natural Sciences

Created by Quantitative Specialists - Specializing in Statistics, Research Design, and Measurement

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Students: 2101, Price: $49.99

Students: 2101, Price:  Paid

November, 2019

In the course, you will learn how to easily and effectively analyze and interpret data involving introductory statistics. The following topics are covered in this course:

Scales of measurement - nominal, ordinal, interval, ratio. 

  • Goal/Learning Objective: Easily understand the often-confused scales of measurement covered in most statistics texts.

Central Tendency - mean, median, and mode are illustrated along with practice problems; measures of central tendency and skewed distributions are explained, as well as how to calculate the weighted mean. 

  • Goals/Learning Objectives: Summarize a set of data, find the center location in a distribution of scores, understand and identify the location of measures of central tendency in skewed distributions, understand and interpret how to find the overall or combined mean for two different sets of data.

Variability - How to calculate the standard deviation and variance as well as how to interpret percentiles are provided in simple and clear language. 

  • Goals/Learning Objectives: Understand and explain variability (spread) in a set of numbers, including how to rank data and interpret data such as standardized test scores (for example, the 95th percentile).

Charts and Graphs - How to calculate a cumulative frequency distribution table as well as how to calculate a stem and leaf plot is illustrated. 

  • Goals/Learning Objectives: Learn how to easily organize, summarize, understand, and explain a set of numbers.

Probability, the Normal Curve and z-Scores - An introduction to probability is provided, along with properties of the normal distribution and how to calculate and interpret z-scores

  • Goals/Learning Objectives: Understand beginning probability including important characteristics of the normal (Gaussian) distribution, as well as how to calculate and interpret z-scores.

Bonus Features: Cement understanding with practice opportunities including several quizzes with complete video coverage of the solutions. 

Update: New Videos Added on Hypothesis Testing and on Correlation!    (See Sections 6 and 7 of the Course.)

ACE the AP Statistics Exam and MASTER Elementary Statistics!

My AP Statistics and Elementary Video Series will help you ace the AP exam and master all Elementary Statistics Concepts

Created by Jerry Linch - AP Statistics Instructor and College Math Instructor

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Students: 1877, Price: $29.99

Students: 1877, Price:  Paid

Want to ace the AP Statistics exam and also do well in your class? Maybe you are taking an elementary or introductory statistics course in college and need the extra help. We'll help you do it with 90 lessons, including several hours of illustrated lecture video, several worked-out example questions, and a complete understanding of the graphing calculator and its statistical capabilities.

Each lesson also comes with a downloadable word document of course notes to help you learn the material as you watch the video lessons.

Although our course is catered towards high school students taking the AP test, college students in a first year statistics course will also find this class life-saving.

Did we mention you'll also have an awesome teacher?

Jerry Linch obtained his B.S. in Mathematics from the University of Nebraska and M.S. in Statistics from the University of Houston Clear Lake. With several years of practice in the actuarial field, he has an excellent understanding of the material and can explain the concepts at a level which any entry level student can understand. If you want a comprehensive course of all the AP Statistics topics and most all elementary statistics topics covered in a college course and explained with ease, then this course is for you.

Statistics & Mathematics for Data Science & Data Analytics

Learn the statistics & probability for data science and business analysis

Created by Nikolai Schuler - Professional Data Scientist und BI Consultant

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Students: 1804, Price: $94.99

Students: 1804, Price:  Paid

Are you aiming for a career in Data Science or Data Analytics?

Good news, you don't need a Maths degree - this course is equipping you with the practical knowledge needed to master the necessary statistics.

It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory.

Sure, there is more to Data Science than only statistics. But still it plays an essential role to know these fundamentals ins statistics.

I know it is very hard to gain a strong foothold in these concepts just by yourself. Therefore I have created this course.

Why should you take this course?

  • This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data

  • This course is taught by an actual mathematician that is in the same time also working as a data scientist.

  • This course is balancing both: theory & practical real-life example.

  • After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis.

What is in this course?

This course is giving you the chance to systematically master the core concepts in statistics & probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions , decision trees and more.

In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly.

If you feel like this sounds good to you, then take this chance to improve your skills und advance career by enrolling in this course.

Statistics for Beginner

Foundational Course for Data Science, Machine Learning & Artificial Intelligence

Created by Ankit Sharma - Senior Demand Planner

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Students: 1566, Price: $94.99

Students: 1566, Price:  Paid

Specially Designed Course for DS, ML & AI

Statistics is the foundational course for the topics in Data Science,Machine learning & Artificial Intelligence.And it is design specially for students interested in Analytics.All the concept & logic is explained lucidly which is back by example & practice question.This course will cover following topics-

a) Introduction to Statistics - Branches of Statistics & Measurements

b) Descriptive Statistics- Central Tendency, Dispersion and Graphs

c) Inferential Statistics - Normal Distribution, Standard Normal Distribution , Sampling Distribution and Central Limit Theorem (CLT)

d) Hypothesis Testing- Hypothesis Formulation, Critical Value Method , P-Value Method, Alpha Level &T-distribution

After completing the course, you will able to do following things-

a) Data Analysis of datasets by using Excel to find any patterns or trends.

b) Using of CLT concept to calculate unknown population mean.

b) Hypothesis testing of claims or assumptions which is use heavily in data driven companies.

c) Concept of P-value and Alpha Value which is very important in analytics.

Statistics for Data Analysis Using Python

Learn Python from Basics • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science

Created by Sandeep Kumar ­ - Experienced Quality Manager • Six Sigma Coach • Consultant

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Students: 1440, Price: $94.99

Students: 1440, Price:  Paid

Perform simple or complex statistical calculations using Python! - You don't need to be a programmer for this :)

You are not expected to have any prior knowledge of Python. I will start with the basics. Coding exercises are provided to test your learnings.

The course not only explains, how to conduct statistical tests using Python but also explains in detail, how to perform these using a calculator (as if, it was the 1960s). This will help you in gaining the real intuition behind these tests.

Learn statistics, and apply these concepts in your workplace using Python.

The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use Python to perform these calculations.

The following areas of statistics are covered:

Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation.

Data Visualization - Commonly used plots such as Histogram, Box and Whisker Plot and Scatter Plot, using the Matplotlib.pyplot and Seaborn libraries.

Probability - Basic Concepts, Permutations, Combinations

Population and Sampling - Basic concepts

Probability Distributions - Normal, Binomial and Poisson Distributions

Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test and Chi-Square Test

ANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using Python.

The Goodness of Fit and the Contingency Tables.

Bayesian Statistics

Bayes Theorem, Bayesian networks, Bayesian sampling methods, Bayesian inference, machine learning and much more

Created by Philipp Loick - PhD student

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Students: 1311, Price: $39.99

Students: 1311, Price:  Paid

Bayesian Statistics is a fascinating field and today the centerpiece of many statistical applications in data science and machine learning. In this course, we will cover the main concepts of Bayesian Statistics including among others Bayes Theorem, Bayesian networks, Enumeration & Elimination for inference in such networks, sampling methods such as Gibbs sampling and the Metropolis-Hastings algorithm, Bayesian inference and the relation to machine learning.

This course is designed around examples and exercises that provide plenty of opportunities to build intuition and apply your gathered knowledge. Many examples come from real-world applications in science, business or engineering or are taken from data science job interviews.

While this is not a programming course, I have included multiple references to programming resources relevant to Bayesian statistics. The course is specifically designed for students without many years of formal mathematical education. The only prerequisite is high-school level mathematics, ideally a first-year university mathematics course and a basic understanding of probability.