Intro to Data Analysis (Udacity)

Offered by Udacity,
Intro to Data Analysis (Udacity)

Explore a variety of datasets, posing and answering your own questions about each. You'll be using the Python libraries NumPy, Pandas, and Matplotlib. This course will introduce you to the world of data analysis.

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

You'll learn how to go through the entire data analysis process, which includes:

  • Posing a question
  • Wrangling your data into a format you can use and fixing any problems with it
  • Exploring the data, finding patterns in it, and building your intuition about it
  • Drawing conclusions and/or making predictions
  • Communicating your findings

You'll also learn how to use the Python libraries NumPy, Pandas, and Matplotlib to write code that's cleaner, more concise, and runs faster.
Syllabus

Lesson 1
Data Analysis Process

  • Learn about the data analysis process.
  • Pose a question, wrangle your data, draw conclusions and/or make predictions.
  • Complete an analysis of Udacity student data using pure Python, with few additional libraries.

Lesson 2
NumPy and Pandas for 1D Data

  • Start learning to use NumPy and Pandas to make the data analysis process easier.
  • Features that apply to one-dimensional data.
  • Learn to use NumPy arrays, Pandas Series, and vectorized operations.

Lesson 3
NumPy and Pandas for 2D Data

  • Continue learning about NumPy and Pandas, this time focusing on two-dimensional data.
  • Learn to use two-dimensional NumPy arrays and Pandas DataFrames.
  • Group your data and to combine data from multiple files.

Lesson 4
Investigate a Dataset

  • Use NumPy and Pandas to go through the data analysis process on one of a list of recommended datasets.
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Authentication & Authorization: OAuth (Udacity) Udacity
Udacity

Authentication & Authorization: OAuth (Udacity)

Implementing Web Security with OAuth 2.0. As a Python programmer, leveraging Flask allows you to quickly and easily build your own web applications. But before you share your apps on the Internet you should protect your users' data, ensuring information stored on your site is safe from unwanted manipulation. You could implement web security and permissions on your own, but relying on trusted providers is a faster, safer, and easier way to allow users to login to your application - without having to create and maintain another account, profile, and password.

Self Paced
Self-Paced
HTTP & Web Servers (Udacity) Udacity
Udacity

HTTP & Web Servers (Udacity)

How does HTTP work? This course is intended for budding full-stack web developers to master the basics of HTTP, the protocol that underlies all web technology. In this course, you'll explore HTTP directly, talking with web servers and browsers by hand. You'll write and deploy low-level web applications in Python. And you'll learn more about how HTTP connects with other web technologies.

Self Paced
Self-Paced
Bioinformatic Methods II (Coursera) Coursera
University of Toronto

Bioinformatic Methods II (Coursera)

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

Nov 23rd 2026
5-12 Weeks
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
Machine Translation (Coursera) Coursera
Karlsruhe Institute of Technology - KIT

Machine Translation (Coursera)

Welcome to the CLICS-Machine Translation MOOC. This MOOC explains the basic principles of machine translation. Machine translation is the task of translating from one natural language to another natural language. Therefore, these algorithms can help people communicate in different languages. Such algorithms are used in common applications, from Google Translate to apps on your mobile device.

Nov 23rd 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
Big Data Analytics in Healthcare (Udacity) Udacity
Georgia Institute of Technology,Udacity

Big Data Analytics in Healthcare (Udacity)

Data science plays an important role in many industries. In facing massive amount of heterogeneous data, scalable machine learning and data mining algorithms and systems become extremely important for data scientists. The growth of volume, complexity and speed in data drives the need for scalable data analytic algorithms and systems. In this course, we study such algorithms and systems in the context of healthcare applications.

Self Paced
Self-Paced
Applied Social Network Analysis in Python (Coursera) Coursera
University of Michigan

Applied Social Network Analysis in Python (Coursera)

This course will introduce the learner to network analysis through the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness.. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

Nov 23rd 2026
4 Weeks
Data Visualization and D3.js (Udacity) Udacity
Udacity,Zipfian Academy

Data Visualization and D3.js (Udacity)

Communicating with Data. Learn the fundamentals of data visualization and practice communicating with data. This course covers how to apply design principles, human perception, color theory, and effective storytelling to data visualization. If you present data to others, aspire to be an analyst or data scientist, or if you’d like to become more technical with visualization tools, then you can grow your skills with this course.

Self Paced
Self-Paced
Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera)

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Nov 23rd 2026
4 Weeks