Course
Introduction to Statistics
BasicSkill Level
Updated 12/2025TheoryProbability & Statistics4 hr16 videos56 Exercises3,450 XP140K+Statement of Accomplishment
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Start Course for FreeWhat you'll learn
- Assess hypothesis-testing results by interpreting p-values, significance levels, Type I and Type II errors, and correlation coefficients to determine statistical significance and relationship strength between variables.
- Differentiate between measures of center (mean, median, mode) and measures of spread (range, variance, standard deviation, interquartile range) based on data symmetry and presence of outliers
- Evaluate probabilities for independent and dependent events, including conditional scenarios, by applying fundamental probability formulas and the law of large numbers
- Identify numeric and categorical data types and match each with suitable summary visualizations such as histograms, scatter plots, and box plots
- Recognize appropriate applications and parameter impacts of key probability distributions—discrete uniform, binomial, Poisson, continuous uniform, and normal—while applying expected value concepts and the 68-95-99.7 rule
Prerequisites
There are no prerequisites for this course1
Summary Statistics
Summary statistics gives you the tools you need to describe your data. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.
2
Probability and distributions
Probability underpins a large part of statistics, where it is used to calculate the chance of events occurring. You'll work with real-world sales data and learn how data with different values can be interpreted as a probability distribution. You'll find out about discrete and continuous probability distributions, including the discovery of the normal distribution and how it occurs frequently in natural events!
3
More Distributions and the Central Limit Theorem
It's time to explore more probability distributions. You'll learn about the binomial distribution for visualizing the probability of binary outcomes, and one of the most important distributions in statistics, the normal distribution. You'll see how distributions can be described by their shape, along with discovering the Poisson distribution and its role in calculating the probabilities of events occuring over time. You'll also gain an understanding of the central limit theorem!
4
Correlation and Hypothesis Testing
In the final chapter, you'll be introduced to hypothesis testing and how it can be used to accurately draw conclusions about a population. You'll discover correlation and how it can be used to quantify a linear relationship between two variables. You'll find out about experimental design techniques such as randomization and blinding. You'll also learn about concepts used to minimize the risk of drawing the wrong conclusion about the results of hypothesis tests!
Introduction to Statistics
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FAQs
Is this course suitable for beginners?
Yes, this course is suitable for beginners. It introduces fundamental statistics concepts in an accessible way so that even those with no prior knowledge of the subject can gain confidence in working with and analyzing data.
Will I receive a certificate at the end of the course?
Yes, at the end of the course, you will receive a certificate of completion from DataCamp.
What will I learn from this course?
In this course, you’ll learn the necessary skills to feel confident in working with and analyzing data. You will be introduced to common methods for summarizing and describing data. You’ll learn about probability and how it can be applied to commercial scenarios, and you’ll discover how experiments are conducted to understand relationships and patterns.
Who will benefit from this course?
This course can benefit many jobs, such as marketing and sales executives, healthcare administrators, financial managers, accountants, and data scientists. All these roles require a fundamental understanding of statistics to make decisions using data.
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