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Introduction to Statistics

Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!

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4 Hours16 Videos56 Exercises14,571 Learners3450 XPData Analyst Track

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Course Description

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!
  1. 1

    Summary Statistics

    Free

    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.

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    What is statistics?
    50 xp
    Using statistics in the real-world
    50 xp
    Identifying data types
    100 xp
    Descriptive vs. Inferential statistics
    100 xp
    Measures of center
    50 xp
    Typical number of robberies per London Borough
    50 xp
    Choosing a measure
    50 xp
    London Boroughs with most frequent crimes
    50 xp
    Measures of spread
    50 xp
    Defining measures of spread
    100 xp
    Box plots for measuring spread
    50 xp
    Which crime has the larger standard deviation
    50 xp
  2. 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!

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  3. 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!

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  4. 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!

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In the following tracks

Data Analyst

Collaborators

james-datacamp
James Chapman
izzyweber-9bc35945-95bd-423b-833e-40780c76586f
Izzy Weber
George Boorman Headshot

George Boorman

Analytics and Data Science Curriculum Manager, DataCamp

George is an Analytics and Data Science 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.
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