Intro to Descriptive Statistics

Beginner

Approx. 2 months

Join thousands of students Course Summary

Statistics is an important field of math that is used to analyze, interpret, and predict outcomes from data. Descriptive statistics will teach you the basic concepts used to describe data. This is a great beginner course for those interested in Data Science, Economics, Psychology, Machine Learning, Sports analytics and just about any other field.

Why Take This Course?

This course will teach you the basic terms and concepts in statistics as well as guide you through introductory probability.

You will learn how to....

• Use statistical research methods.
• Compute and interpret values like: Mean, Median, Mode, Sample, Population, and Standard Deviation.
• Compute simple probabilities.
• Explore data through the use of bar graphs, histograms, box plots, and other common visualizations.
• Investigate distributions and understand a distributions properties.
• Manipulate distributions to make probabilistic predictions on data.

Prerequisites and Requirements

This course assumes understanding of basic algebra and arithmetic.

See the Technology Requirements for using Udacity.

What Will I Learn? Projects P1: Predicting Boston Housing Prices The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then used to estimate the best selling price for your client’s home. P1: Test a Perceptual Phenomenon Use descriptive statistics and a statistical test to analyze the Stroop effect, a classic result of experimental psychology. Give your readers a good intuition for the data and use statistical inference to draw a conclusion based on the results. P0: 7 Day Warm-Up: Find the Optimal Chopstick Length An opportunity to get started with data analysis and receive some quick feedback about your progress. Set up iPython notebook and commonly used data analysis libraries on your own computer. Use them to dig into the results of an experiment testing the optimal length of chopsticks and present your findings. Syllabus Lesson 1 : Intro to Research Methods

You will be introduced to several statistical study methods and learn the positives and negatives of each.

Lesson 2 : Visualizing Data

You will learn how to take your data and display it to the world. You will learn to create and interpret histograms, bar charts, and frequency plots.

Lesson 3 : Central Tendency

In this lesson you will learn to compute and interpret the 3 measures of center for distributions: the mean, median, and mode.

Lesson 4 : Variability

You will learn how to quantify the spread of data using the range and standard deviation. You will also learn how to identify outliers in data sets using the concept of the interquartile range.

Lesson 5 : Standardizing

You will learn how to convert distributions into the standard normal distribution using the Z-score. You will also learn how to compute proportions using standardized distributions.

Lesson 6 : Normal Distribution

You will learn how to use normalized distributions to compute probabilities. You will also learn how to use the Z-table to look up the proportions of observations above, below, or in between values.

Lesson 7 : Sampling Distributions

You will learn how to apply the concepts of probability and normalization to sample data sets.