Statistics are all around us, from marketing to sales to healthcare. The ability to collect, analyze, and draw conclusions from data is not only extremely valuable, but it is also becoming commonplace to expect roles that are not traditionally analytical to understand the fundamental concepts of statistics. This course will equip you with the necessary skills to feel confident in working with analyzing data to draw insights. You'll be introduced to common methods used for summarizing and describing data, learn how probability can be applied to commercial scenarios, and discover how experiments are conducted to understand relationships and patterns. You'll work with real-world datasets including crime data in London, England, and sales data from an online retail company!
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.What is statistics?50 xpUsing statistics in the real-world50 xpIdentifying data types100 xpDescriptive vs. Inferential statistics100 xpMeasures of center50 xpTypical number of robberies per London Borough50 xpChoosing a measure50 xpLondon Boroughs with most frequent crimes50 xpMeasures of spread50 xpDefining measures of spread100 xpBox plots for measuring spread50 xpWhich crime has the larger standard deviation50 xp
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!What are the chances?50 xpWhat is more likely?50 xpChances of the next sale being more than the mean50 xpConditional probability50 xpDependent vs. Independent events100 xpOrders of more than 10 basket products50 xpDiscrete distributions50 xpIdentifying distributions50 xpSample mean vs. Theoretical mean50 xpContinuous distributions50 xpDiscrete vs. Continuous distributions100 xpFinding the normal distribution50 xpCalculating probability with a uniform distribution50 xp
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!The binomial distribution50 xpRecognizing a binomial distribution50 xpHow probability affects the binomial distribution50 xpIdentifying n and p50 xpThe normal distribution50 xpRecognizing the normal distribution50 xpWhat makes the normal distribution special?100 xpIdentifying skewness100 xpDescribing distributions using kurtosis50 xpThe central limit theorem50 xpVisualizing sampling distributions50 xpThe CLT vs. The law of large numbers100 xpWhen to use the central limit theorem50 xpThe Poisson distribution50 xpIdentifying Poisson processes100 xpRecognizing lambda in the Poisson distribution50 xp
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!Hypothesis testing50 xpSunshine and sleep100 xpThe hypothesis testing workflow100 xpIndependent and dependent variables50 xpExperiments50 xpRecognizing controlled trials100 xpWhy use randomization?50 xpCorrelation50 xpIdentifying correlation between variables50 xpWhat can correlation tell you?50 xpConfounding variables50 xpInterpreting hypothesis test results50 xpSignificance levels vs. p-values100 xpType I and type II errors50 xpCongratulations!50 xp
George BoormanSee More
Curriculum Manager, DataCamp
George is a Curriculum Manager at DataCamp. He holds a PGDip in Exercise for Health and BSc (Hons) in Sports Science and has experience in project management across public health, applied research, and not-for-profit sectors. George is passionate about sports, tech for good, and all things data science.