Best Business Statistics Courses - Q1 2023

In today's business environment, decisions are increasingly based on data. That data is often combined from disparate sources that require skills in wrangling, cleaning, organizing, and analyzing in meaningful and accurate ways. At center of this work is the discipline of statistics. Understanding business statistics can help you to find jobs, progress in your career, and make better business decisions.
Our experts have evaluated the top courses across the leading online learning platforms based on strict criteria to identify the best choices for you.

Introduction to Statistics

Transparent graphs and spreadsheets presented across the walls of a roomGo to Training
on Coursera's website
Campus.com Rating
4.6
/5

Time to Complete:

3.75 weeks @ 4 hours per week

# Enrolled:

206,378

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Exploring and Producing Data for Business Decision Making

University of Illinois LogoGo to Training
on Coursera's website
Campus.com Rating
4.5
/5

Time to Complete:

6 weeks @ 4 hours per week

# Enrolled:

36,753

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Understanding Clinical Research: Behind the Statistics

A stethoscope with graphs superimposedGo to Training
on Coursera's website
Campus.com Rating
4.3
/5

Time to Complete:

7 weeks @ 4 hours per week

# Enrolled:

137,058

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Statistics and data literacy for non-statisticians

a conceptual graphic showing a neural brain, a normal distribution graph, and a formulaGo to Training
on Udemy's website
Campus.com Rating
4.3
/5

Time to Complete:

0.5 weeks @ 4 hours per week

# Enrolled:

17,631

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Statistics & Mathematics for Data Science & Data Analytics

A man in a tie. Behind him are multiple statistical graphs and formulasGo to Training
on Udemy's website
Campus.com Rating
3.8
/5

Time to Complete:

3 weeks @ 4 hours per week

# Enrolled:

5,871

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Introduction to Statistics

The Datacamp logoGo to Training
on Datacamp's website
Campus.com Rating
3.7
/5

Time to Complete:

1 week @ 4 hours per week

# Enrolled:

13,173

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Introduction to Statistics in Spreadsheets

a bar graph is shown with the title of the courseGo to Training
on Datacamp's website
Campus.com Rating
4.5
/5

Time to Complete:

1 week @ 4 hours per week

# Enrolled:

27,192

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Statistics and Data Analysis in Excel

A man works on a laptop. Statistical graphs can be seen on his screen.Go to Training
on FutureLearn's website
Campus.com Rating
4.7
/5

Time to Complete:

5 weeks @ 4 hours per week

# Enrolled:

65

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Statistics Foundations 3: Using Data Sets

a conceptual graphic of a man holding a laptop. He points to a large normal curve.Go to Training
on LinkedIn Learning's website
Campus.com Rating
4.2
/5

Time to Complete:

0.5 weeks @ 4 hours per week

# Enrolled:

8,965

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Statistics 1 Part 1: Introductory statistics, probability and estimation

A professor stands in front of a large screen of numbersGo to Training
on EdX's website
Campus.com Rating
4.3
/5

Time to Complete:

15 weeks @ 4 hours per week

# Enrolled:

Not available

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Inferential Statistics

a conceptual graphic of a bar chartGo to Training
on Coursera's website
Campus.com Rating
4.3
/5

Time to Complete:

4 weeks @ 4 hours per week

# Enrolled:

104,567

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Topics To Look For in a Business Statistics Course

The following are the topics that our experts feel should be covered in an Excel for Accounting Course.
  • Understanding data
  • Structured vs unstructured data
  • Probability
  • Linear regression
  • Multiple regression
  • Confidence intervals
  • Statistical inference
  • ANOVA
  • Binomial distributions
  • Sampling distributions
  • Two population means
  • Mean, median, and mode
  • Scatterplots
  • Formulating and testing hypothesis
  • Computing measures of central tendency and variation

The Major Online Learning Platforms

Each online learning platform is unique.  Some specialize in certain types of content, some partner with major universities or corporations to develop content, and some have special learning features.  While not exhaustive, the list below includes the major online platforms whose courses we feature.

Pros / Cons

Pros: Certificates often are offered from major universities or corporations.

Cons: The quality of course content and features available varies dramatically between courses.

Cost

$$$

Pros / Cons

Pros: Courses are very similar to those offered in colleges.

Cons: The quality of content and features offered varies dramatically between learning programs.

Cost

$$$$

Pros / Cons

Pros: A focus on microlearning with consistently high-quality content.  Consistent use of quizzes throughout all courses.

Cons: Pricing is skewed toward annual subscriptions, which is far more content (and cost) than most learners need within a year.

Cost

$$$

Pros / Cons

Pros: Labs offer a means of gaining practical experience in technical skills.

Cons: There are no incremental quizzes or ways to test learners on specific topics.

Cost

$$$$

Pros / Cons

Pros: Udemy tends to be one of the more affordable options for individuals looking to learn specific technical skills.


Cons: Highly inconsistent quality between courses.

Cost

$

Pros / Cons

Pros: Consistent, high-quality micro-content that focuses on practical exercises.

Cons: The catalog is somewhat limited compared to other vendors on this list.

Cost

$$$

Pros / Cons

Pros: Many free online classes are offered. Live online classroom experiences are offered for some of their courses.

Cons: Variable quality of courses, with some courses having relatively low quality.

Cost

$$$$$

Pros / Cons

Pros: Skillshare has a large library of courses related to creative skills.

Cons: Most courses are just a series of lectures with no interactivity, quizzing, or opportunities to practice concepts.

Cost

$$$

For more information, check out our detailed post on the best online course platforms.

Frequently Asked Questions about Data Analytics

How can data analytics help business?

Structuring, organizing, analyzing, and reporting data can greatly benefit businesses. Some examples include:

  • Combining disparate data sources to come up with unique insights. For instance, you may have some business data in Excel and other business data in customer relationship databases and e-commerce systems.
  • Data-driven decision making – Companies that analyze the right data at the right times can make decisions on facts rather than hunches or strong opinions. Data can also be an equalizer in situations where the opinions of those with strong personalities win out over more reserved personalities.
  • Customer insights – These days, there is a wide range of quantitative and qualitative data that can be used to gain customer insights. These include website user data, demographic data, buying behavior data, surveys, and many other forms of data. Understanding the structure of all of these types of data and how they can be used to gain accurate insights is an important job for most businesses.
  • Streamlining processes – It is often necessary to gather disparate data in a purposeful way in order to identify bottlenecks and high-cost parts of processes. Doing so can save companies millions in time and money.

Does data analytics require coding ?

Yes – in most cases, data analysts are expected to know some basic coding. This is because analyzing data often requires querying and transforming the data from multiple data sources. Understanding languages like SQL, advanced Excel functions, and Python or R can be extremely helpful for these tasks.

Does data analytics require statistics?

Yes - generally speaking, data analysts are expected to understand the basic concepts of probability, as well as appropriate ways to calculate and present different types of data.

Are data analytics and data science same?

The major difference between data analytics and data science is the scope of analysis. Generally speaking, Data Scientists must be more skilled at statistical models and programming, as they perform sophisticated analyses of data structures. Often Data Scientists have specific domain knowledge (such as Finance or Education). Data Analysts generally perform higher-level analysis – they gather, organize and report on data within a specific scope. Data analysts are not usually as involved in making data predictions as data scientists.

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