4 Udacity Statistics Courses For Improving Your Decision Making

If you’re looking for somewhere to learn new skills and take in-depth courses on various subjects, Udacity is the platform for you.

Udacity offers many free and paid courses taught by experts in relevant fields to help you learn and reach your full potential in your own time.

One subject that is notoriously difficult to get to grips with is statistics, but in Udacity it is taught by industry pros.

Math is a difficult subject for many people, so the idea of learning how to interpret and use numerical data science to make predictions and decisions with basic understanding is daunting to prospective students.

Luckily, Udacity offers a number of courses dedicated to learning about descriptive statistics.

There are four Udacity statistics courses in total available through Udacity, three of which are totally free.

These courses are Statistics: The Science of Decisions’, ‘Introduction to Statistics: Making Decisions Based on Data’, ‘Introduction to Inferential Statistics: Making Predictions from Data’, and ‘Become a Data Analyst’, which is a paid nano degree program offered by Udacity.

We’re going to be exploring your options for taking statistics courses with Udacity today, so stay tuned to find out everything you need to know about Udacity’s statistics programs.

Statistics: The Science Of Decisions

The first course we’re going to be discussing today is ‘Statistics: The Science of Decisions’.

This course is free, so you don’t need to pay anything.

Unlike some of the free online courses you might find on other, less reputable platforms, Udemy’s ‘Statistics’ course is comprehensive as well as detailed.

You don’t need to have any significant foundation of statistical knowledge to take this course since it’s designed to cater to beginners.

You just need to understand the basics of proportions, algebra, negative numbers, square roots, and exponents.

Since you don’t need to pay anything to take the course, either, all you need is the time to dedicate to learning the basics of statistics and data over approximately 4 months.

Although this is a free course, the content is taught by leading professionals in the industry.

The course instructors are Katie Kormanik, Ronald Rogers, and Sean Laraway.

Course material for ‘Statistics’ includes a variety of in-depth learning resources, complete with interactive quizzes throughout to test your knowledge and identify areas for improvement.

Since the course has self-paced learning, you can complete it in your own time, so it’s perfect for learners with busy schedules or unusual working hours.

The ‘Statistics’ course on Udacity begins with an Introduction to Statistics and Methods, which covers the basics of statistical research.

You will then learn how to describe data according to variability and central limit theorem tendency.

The third lesson is on Normal Distribution Analysis, and during this part of the course, you will learn about sampling distributions, standardized scores, probability theory, and normal distribution.

Lesson 4 is concerned with the Foundations of Inferential Statistics and basic tools, including testing hypotheses and making estimates, and lesson 5 is all about Comparing Means using t-tests and one-way ANOVA.

Finally, you’ll get to lesson 6, which covers Correlation, Regression, and Non-Parametrics.

If you complete and enjoy this course, it provides a natural path to the Introduction to Programming Nanodegree, so bear this in mind as you complete your coursework.

Introduction To Statistics: Making Decisions Based On Data

‘Introduction to Statistics: Making Decisions Based on Data’ covers some of the same material as the above ‘Statistics’ course offered by Udacity, but this is a different course with 6 different lessons included.

Like the previous course we discussed, this one is free, but it doesn’t take as long to complete. In fact, you only need to dedicate 2 months to learning.

It’s also a beginner-friendly course, so you don’t need any prior sophisticated knowledge of mathematics or statistics, specifically.

You’ll definitely benefit from having knowledge of basic algebra, but other than that, there are no prerequisites.

Lesson 1 is all about visualizing relationships in data, and the module will teach you how to interpret data to identify relationships and make predictions.

You’ll also learn about Simpson’s paradox. Moving on to lesson 2, you’ll be focusing on probability, including correlation, causation, and Bayes Rule.

Lesson 3 is titled ‘Estimation’, and covers mean and standard deviation, variance, the concepts of mean, median, and mode, and the premise of maximum likelihood estimation.

In lesson 4, you’ll learn how to handle outliers and learn about quartiles. The lesson also covers how to manipulate normal distribution and binomial distribution.

Lesson 5 is inference, so you’ll be focusing on hypothesis testing and confidence intervals t-tests before lesson 6, which is all about linear regression and correlation.

The course is led by Sebastian Thrun and includes learn-by-doing exercises. It’s primarily taught in video format.

Again, this course leads naturally into the Introduction to Programming Nanodegree, so if you have the budget and your ultimate goal in taking ‘Intro to Statistics’ is to go into the programming field, definitely consider taking this paid course next!

Introduction To Inferential Statistics: Making Predictions From Data

The third and final free course from Udacity we’ll be talking about today is ‘Introduction to Inferential Statistics: Making Predictions from Data’.

‘Introduction to Inferential Statistics’ is led by Katie Kormanik, Ronald Rogers, and Sean Laraway, and it takes approximately two months to cover the course material from start to finish.

As long as you have the time to dedicate to this course, you don’t need anything else because there’s no fee and you only need a beginner-level mathematical background.

While many of the free courses on Udacity consist of 6 lessons, this course has 7 lessons.

The first lesson is called ‘Estimation’, and it teaches you how to use confidence intervals and sample statistics hypothesis testing to make estimates regarding population parameters.

Afterward, in lesson 2, you’ll move on to hypothesis testing, which is a subject that both of the aforementioned courses cover, too.

However, on this course, you’ll specifically be learning how to identify when a population parameter’s value has been altered by treatment.

This course also covered t-tests in lesson 3.

You’ll learn how to find out what a treatment’s effect is using testing methods and how to compare the mean difference between smaller sample sizes.

In lesson 4, the subject is the ANOVA test, which is what you’ll use to see whether three, four, or more groups have differences between them.

Lessons 5 and 6 cover correlation and regression, respectively.

In lesson 5, you’ll be describing and testing how strong the two variables’ relationship is, and in lesson 6, you’ll be identifying how changes in two variables are related to one another.

Finally, in lesson 7, which is called ‘Chi-squared Tests’, you’ll find out about regression chi-squared test methods for categorical data frequencies.

If you enjoy the ‘Introduction to Inferential Statistics course, consider taking the Digital Marketing Nanodegree program offered by Udacity through Facebook Blueprint.

Become A Data Analyst

If you’re looking for a more advanced course on statistics from Udacity, the ‘Become a Data Analyst’ Nanodegree course might be for you.

Bear in mind that to do well on this course, you will need to dedicate 10 hours per week of your time for 4 months.

This is a significant time commitment, so consider whether this is realistic for you to fit around existing commitments.

There’s also a prerequisite for this Nanodegree, which isn’t the case with the free statistics courses on Udacity.

You must have Python experience, especially with Pandas and NumPy, and you should also have experience with SQL.

Udacity’s ‘Become a Data Analyst’ course is split into 4 modules: Introduction to Data Analysis, Practical Statistics, Data Wrangling, and Data Visualization with Python.

During the introduction to the course, you’ll learn how to analyze data using wrangling and how to communicate said data.

This will involve the use of Python, Pandas, and NumPy.

As part of the introduction, you’ll be working on two projects: Explore Weather Trends and Investigate a Dataset.

The Practical Statistics module will teach you how inferential statistics can be applied to situations that you might encounter professionally, such as creating learning models and A/B test analysis.

Your project for this module will be called ‘Analyze Experiment Results’.

As part of this, you’ll be given a dataset obtained from an experiment and asked to write a report based on questions that require the use of statistical methods to answer.

The part of the course about Data Wrangling involves gathering and assessing data before cleaning it.

You’ll also be using Python here to get data ready for analysis using wrangling.

You’ll be taking part in the ‘Wrangle and Analyze Data’ project, which requires you to use the skills learned in the module and document the process.

Finally, in the Data Visualization with Python module, you’ll be applying the principles of visualization to analyzing data.

The ‘Communicate Data Findings’ project included in the module will prompt you to use these visualization skills to explore data and effectively communicate the variable relationships and properties in a presentation.

Frequently Asked Questions

Are Udacity Courses Free?

Many courses available on Udacity are free, meaning there’s no fee required to take the course.

However, this is not true for all courses on Udacity, so it’s important to be aware that some courses are paid when you’re researching the best course for you.

Currently, there are more than 200 free courses you can sign up for on Udacity.

However, you also have the option to pay for Nanodegree courses, such as the ‘Become a Data Analyst’ course we discussed in this article.

Is It Worth Paying For Udacity?

If you have your eye on one of Udacity’s Nanodegree courses, such as the ‘Become a Data Analyst’ course, you might be wondering whether it’s worth paying the (admittedly fairly high) cost of the paid course as opposed to simply enrolling in a free program.

Nanodegree courses with Udacity come with a lot of perks that aren’t included in the free courses.

For example, when you pay for a Udacity Nanodegree, you’ll get a more in-depth learning experience complete with mentorship and grading at the end of the course.

If you complete the course to a good standard, you will get a certificate to mark your achievement, and a Udacity certificate is something that many companies respect and value when it comes to hiring applicants.

So, if you’re hoping to improve your employment prospects in your chosen field by taking a statistics course online, paying for Udacity is worth it.

Is Udacity Data Analyst Nanodegree Worth It?

If our summary of the Data Analyst Nanodegree wasn’t enough to convince you to enroll on the course, we can confirm that the Data Analyst Nanodegree from Udacity is worth the money.

Not only will this course allow you to learn advanced programming skills and help you to understand complicated datasets in a way that fits into your existing schedule, but by the end of the course, you’ll be able to say that you can competently visualize and interpret data.

This is a skill that employers in related fields value very highly, and something that not many job applicants will be able to say they can do.

This Nanodegree also offers you support from one of Udacity’s mentors, the experience of working on projects designed by experts in the industry, and access to Udacity’s helpful career services.

However, if you don’t feel you’ll be able to dedicate 10 hours per week to the program, it may not be worth paying the course fee because you will need to dedicate this amount of time consistently in order to pass the course.

Final Thoughts

Udacity offers a variety of different courses on statistics.

There are both free and paid courses, some of which require 2 months to complete, and some of which will require a 4-month commitment.

The free courses, which include ‘Statistics’, ‘Introduction to Statistics’, and ‘Introduction to Inferential Statistics’, are beginner-friendly and connect with paid Nanodegree courses if you want to take your learning further.

There is also a Nanodegree course on how to ‘Become a Data Analyst’, which takes 4 months to complete, involves professionally-led projects, and comes with an official certificate at the end of the course.