Intro to Statistics (Udacity)

Offered by Udacity,
Intro to Statistics (Udacity)

Get ready to analyze, visualize, and interpret data! Thought-provoking examples and chances to combine statistics and programming will keep you engaged and challenged.

Class Deals by MOOC List - Click here and see Udacity's Active Discounts, Deals, and Promo Codes.

Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics.
This course will cover visualization, probability, regression and other topics that will help you learn the basic methods of understanding data with statistics.

Syllabus

Lesson 1
Visualizing relationships in data
Seeing relationships in data.
Making predictions based on data.
Simpson's paradox.

Lesson 2
Probability
Introduction to Probability.
Bayes Rule.
Correlation vs. Causation.

Lesson 3
Estimation
Maximum Likelihood Estimation.
Mean, Median, Mode.
Standard Deviation and Variance.

Lesson 4
Outliers and Normal Distribution.
Outliers, Quartiles.
Binomial Distribution.
Manipulating Normal Distribution.

Lesson 5
Inference
Confidence Intervals.
Hypothesis Testing.

Lesson 6
Regression
Linear regression.
Correlation.

Lesson 7
Final Exam

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Model Building and Validation (Udacity) Udacity
Udacity

Model Building and Validation (Udacity)

Advanced Techniques for Analyzing Data. This course will teach you how to start from scratch in answering questions about the real world using data. Machine learning happens to be a small part of this process. The model building process involves setting up ways of collecting data, understanding and paying attention to what is important in the data to answer the questions you are asking, finding a statistical, mathematical or a simulation model to gain understanding and make predictions.

Self Paced
Self-Paced
Basic Statistics (Coursera) Coursera
University of Amsterdam

Basic Statistics (Coursera)

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.

Sep 28th 2026
5-12 Weeks
Spark (Udacity) Udacity
Udacity,Insight

Spark (Udacity)

Master how to work with big data and build machine learning models at scale using Spark! In this course, you’ll learn how to use Spark to work with big data and build machine learning models at scale, including how to wrangle and model massive datasets with PySpark, the Python library for interacting with Spark. In the first lesson, you will learn about big data and how Spark fits into the big data ecosystem. In lesson two, you will be practicing processing and cleaning datasets to get comfortable with Spark’s SQL and dataframe APIs. In the third lesson, you will debug and optimize your Spark code when running on a cluster. In lesson four, you will use Spark’s Machine Learning Library to train machine learning models at scale.

Self Paced
Self-Paced
Data Wrangling with MongoDB (Udacity) Udacity
Udacity,MongoDB University

Data Wrangling with MongoDB (Udacity)

In this course, we will explore how to wrangle data from diverse sources and shape it to enable data-driven applications. Some data scientists spend the bulk of their time doing this! Students will learn how to gather and extract data from widely used data formats. They will learn how to assess the quality of data and explore best practices for data cleaning. We will also introduce students to MongoDB, covering the essentials of storing data and the MongoDB query language together with exploratory analysis using the MongoDB aggregation framework.

Self Paced
Self-Paced
Understanding Clinical Research: Behind the Statistics (Coursera) Coursera
University of Cape Town

Understanding Clinical Research: Behind the Statistics (Coursera)

If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.

Sep 28th 2026
5-12 Weeks
Intro to Descriptive Statistics (Udacity) Udacity
Udacity

Intro to Descriptive Statistics (Udacity)

Mathematics for Understanding Data. 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.

Self Paced
Self-Paced
Real-Time Analytics with Apache Storm (Udacity) Udacity
Udacity,Twitter

Real-Time Analytics with Apache Storm (Udacity)

The world is trending in real time! Learn from Twitter to scalably process tweets, or any big data stream, in real-time to drive d3 visualizations using Apache Storm, the "Hadoop of Real Time." Storm is free, open source, and fun to use! Learn from Karthik Ramasamy, about the distributed, fault-tolerant, and flexible technology used to power Twitter’s real-time data flow pipeline. Twitter open sourced Storm in 2011, and it graduated to a top-level Apache project in September, 2014.

Self Paced
Self-Paced
Single Variable Calculus (Coursera) Coursera
University of Pennsylvania

Single Variable Calculus (Coursera)

Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences. Distinguishing features of the course include: 1) the introduction and use of Taylor series and approximations from the beginning; 2) a novel synthesis of discrete and continuous forms of Calculus; 3) an emphasis on the conceptual over the computational; and 4) a clear, dynamic, unified approach.

Sep 28th 2026
5-12 Weeks